Dissolved oxygen concentration control system and method

Abstract

The invention discloses a dissolved oxygen concentration control system and method. The system is characterized in that dissolved oxygen concentration control equipment is electrically connected with a sewage treatment tank; the dissolved oxygen concentration control equipment is used for constructing a dissolved oxygen concentration linear dynamic change model, a first sliding mode controller and a trend rate controller; determining a control rate equation based on the dissolved oxygen concentration linear dynamic change model, the first sliding mode controller and the trend rate controller, and solving the control rate equation; and the dissolved oxygen concentration control input module of the sewage treatment tank is used for acquiring a solving result of the control rate equation as a dissolved oxygen concentration control input variable quantity of the sewage treatment tank at the current sampling moment, and controlling the dissolved oxygen concentration of the sewage treatment tank according to the dissolved oxygen concentration control input variable quantity and a dissolved oxygen concentration control input value at the current sampling moment. And determining a dissolved oxygen concentration control input value of the sewage treatment tank at the next sampling moment. The dissolved oxygen concentration of the sewage treatment tank can be stably and accurately controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and particularly relates to a control system and method for dissolved oxygen concentration. Background Art

[0002] With the rapid economic development and the advancement of urban industrialization, the discharge of industrial sewage and domestic wastewater is increasing day by day. The problem of water shortage caused by water pollution and the growing water demand have exacerbated the contradiction between water supply and demand. At present, sewage treatment systems are widely used to reduce water pollution to alleviate the contradiction between water supply and demand. The sewage treatment system is an industrial system widely used in fields such as petrochemical industry and residential life, which can promote the recycling of wastewater, greatly reduce the industrial water demand, and avoid environmental pollution. However, the sewage treatment system in the sewage treatment plant is a very complex time-varying dynamic system. The internal reaction process is affected by external disturbances such as influent flow rate, pollutant load, and unknown influent components, and exhibits characteristics such as nonlinearity, strong coupling, and strong disturbance. Therefore, the control research of the sewage treatment system is of great significance for improving its operating performance and effluent quality.

[0003] In the sewage treatment system, controlling the concentration of dissolved oxygen (DO for short) can improve the sewage treatment effect. For traditional control methods, since the controller parameters cannot adaptively change online, the control performance is greatly affected by external disturbances; while for the fuzzy control method, although it can improve the control effect and reduce energy consumption, it highly depends on expert experience and has low control accuracy; due to the strong nonlinearity and non-stationary dynamic characteristics of the sewage treatment process, it is difficult to establish an accurate mathematical model in the model-based control algorithm, and an approximate model obtained by reducing the order is usually used, resulting in the neglect of some dynamic characteristics of the high-order system and generating the problem of unmodeled dynamics; while the application of neural networks in the identification and control of the sewage treatment system, although it can save energy consumption and improve the robustness of control, requires a large amount of historical data to train the neural network. For most sewage treatment plants, due to the limitation of measuring equipment, a large amount of data available for training the neural network cannot be provided, and when the sewage treatment system changes, the neural network needs to be retrained, and the neural network still cannot avoid the problem of unmodeled dynamics. Based on this, there is an urgent need to provide a method for controlling the dissolved oxygen concentration that does not rely on a model and has high control accuracy and strong robustness. Summary of the Invention

[0004] The inventors of the present invention found that when controlling the dissolved oxygen concentration, the parameters of traditional controllers cannot adaptively change, resulting in a large impact of control performance on external disturbances; the fuzzy control method highly depends on expert experience and has low control accuracy; the control method based on a mathematical model is prone to unmodeled dynamic problems, and the control method based on a neural network cannot provide a large amount of data for training the neural network and also has unmodeled dynamic problems. In view of the above problems, the inventors of the present invention proposed a control system and method for dissolved oxygen concentration that overcome the above problems or at least partially solve the above problems.

[0005] In a first aspect, an embodiment of the present invention provides a control system for dissolved oxygen concentration, including a sewage treatment tank, and further including: a dissolved oxygen concentration control device;

[0006] The dissolved oxygen concentration control device is electrically connected to the sewage treatment tank;

[0007] The dissolved oxygen concentration control device is used to construct a linear dynamic change model of dissolved oxygen concentration, a first sliding mode controller, and a rate of change controller; based on the linear dynamic change model of dissolved oxygen concentration, the first sliding mode controller, and the rate of change controller, determine a control rate equation and solve the control rate equation; the control rate equation is used to calculate the change amount of the dissolved oxygen concentration control input at the current sampling moment, and the change amount of the dissolved oxygen concentration control input at the current sampling moment represents the difference between the dissolved oxygen concentration control input value at the next sampling moment and the dissolved oxygen concentration control input value at the current sampling moment;

[0008] The sewage treatment tank includes x, and x is used to obtain the solution result of the control rate equation as the change amount of the dissolved oxygen concentration control input at the current sampling moment of the sewage treatment tank, and determine the dissolved oxygen concentration control input value at the next sampling moment of the sewage treatment tank according to the change amount of the dissolved oxygen concentration control input at the current sampling moment and the dissolved oxygen concentration control input value at the current sampling moment.

[0009] In one embodiment, the linear dynamic change model of dissolved oxygen concentration is constructed in the following manner:

[0010] Under the Lipschitz continuous condition and the first preset condition, construct a nonlinear dynamic change model of dissolved oxygen concentration; the first preset condition is that the partial derivative of the nonlinear dynamic change model of dissolved oxygen concentration with respect to the dissolved oxygen concentration control input value is continuous.

[0011] Convert the nonlinear dynamic change model of dissolved oxygen concentration to obtain a linear dynamic change model of dissolved oxygen concentration, and the linear dynamic change model of dissolved oxygen concentration is:

[0012] Δy(k + 1) = y(k) + φ c (k)Δu(k);

[0013] In the above formula, k represents the k-th sampling moment, y(k) represents the simulated output value of the dissolved oxygen concentration at the k-th sampling moment in the sewage treatment simulation tank, represents the change rate of the dissolved oxygen concentration at the k-th sampling moment, and Δu(k) represents the change amount of the dissolved oxygen concentration control input at the k-th sampling moment in the sewage treatment simulation tank.

[0014] In one embodiment, the first sliding mode controller is constructed in the following manner:

[0015] Adopt fast integration to construct a second sliding mode controller in continuous time, and based on the second sliding mode controller, construct an integral error term;

[0016] Construct a dissolved oxygen concentration tracking error function; the dissolved oxygen concentration tracking error function is used to determine the dissolved oxygen concentration tracking error value at the current sampling moment, and the dissolved oxygen concentration tracking error value represents the difference between the simulated output value of the dissolved oxygen concentration and the desired output value of the dissolved oxygen concentration;

[0017] According to the preset sampling period, the integral error term, the dissolved oxygen concentration tracking error function, and the dissolved oxygen concentration linear dynamic change model, construct a first sliding mode controller, and the first sliding mode controller is used to calculate the sliding mode surface at each sampling moment.

[0018] In one embodiment, the integral error term is:

[0019]

[0020] In the above formula, E(k) represents the integral error term, e(k) represents the dissolved oxygen concentration tracking error value at the k-th sampling moment, α is a positive number, k represents the k-th sampling moment, p1 and p1 are positive odd numbers satisfying p1>p1>0, and βsgn(e) represents the fast integration term.

[0021] In one embodiment, the first sliding mode controller is:

[0022]

[0023] In the above formula, s(k) represents the sliding mode surface at the k-th sampling moment, and Δy d (k) represents the change amount of the desired output of the dissolved oxygen concentration at the k-th sampling moment.

[0024] In one embodiment, the trend change rate controller is:

[0025] s(k + 1) = s(k) - mT|s(k)| λ sgn(s(k)) - nTs(k);

[0026] In the above formula, m and n represent positive constants, 0<λ<1; T represents the sampling period.

[0027] In one embodiment, based on the linear dynamic change model of dissolved oxygen concentration, the first sliding mode controller and the trend rate controller, a control rate equation is determined, including:

[0028] The linear dynamic change model of dissolved oxygen concentration, the first sliding mode controller and the formula of the trend rate controller are combined to obtain a control rate equation, which is:

[0029]

[0030] In one embodiment, solving the control rate equation includes:

[0031] Calculating the dissolved oxygen concentration change rate of the system at the current sampling moment;

[0032] Calculating a dissolved oxygen concentration tracking error value at a current sampling moment of the system;

[0033] Obtaining the expected output change of dissolved oxygen concentration at the current sampling moment of the system;

[0034] The dissolved oxygen concentration change rate, the dissolved oxygen concentration expected output change and the dissolved oxygen concentration tracking error value at the current sampling moment are substituted into the control rate equation for solution.

[0035] In one embodiment, calculating the dissolved oxygen concentration change rate of the system at the current sampling time includes:

[0036] An improved projection algorithm is used to calculate an estimated value of the dissolved oxygen concentration change rate as the value of the dissolved oxygen concentration change rate.

[0037] In one embodiment, determining the dissolved oxygen concentration control input value of the sewage treatment simulation tank at the next moment according to the dissolved oxygen concentration control input change at the current moment and the dissolved oxygen concentration control input value at the current moment includes:

[0038] The sum of the dissolved oxygen concentration control input change at the current moment and the dissolved oxygen concentration control input value at the current moment is used as the dissolved oxygen concentration control input value of the sewage treatment simulation tank at the next moment.

[0039] In a second aspect, an embodiment of the present invention provides a method for controlling dissolved oxygen concentration, comprising:

[0040] Construct a linear dynamic change model of dissolved oxygen concentration, a first sliding mode controller, and a rate-of-change controller; based on the linear dynamic change model of dissolved oxygen concentration, the first sliding mode controller, and the rate-of-change controller, determine a control rate equation and solve the control rate equation; the control rate equation is used to calculate the change amount of the dissolved oxygen concentration control input at the current sampling moment, and the change amount of the dissolved oxygen concentration control input at the current sampling moment represents the difference between the dissolved oxygen concentration control input value at the next sampling moment and the dissolved oxygen concentration control input value at the current sampling moment;

[0041] Obtain the solution result of the control rate equation as the change amount of the dissolved oxygen concentration control input at the current sampling moment of the sewage treatment tank, and determine the dissolved oxygen concentration control input value at the next sampling moment of the sewage treatment tank according to the change amount of the dissolved oxygen concentration control input at the current sampling moment and the dissolved oxygen concentration control input value at the current sampling moment.

[0042] In a third aspect, an embodiment of the present invention provides a computer storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the foregoing control method of dissolved oxygen concentration is implemented.

[0043] In a fourth aspect, an embodiment of the present invention provides a dissolved oxygen concentration control device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the foregoing control method of dissolved oxygen concentration is implemented.

[0044] The beneficial effects of the above technical solutions provided by the embodiments of the present invention at least include:

[0045] The control system for dissolved oxygen concentration provided by the embodiments of the present invention includes a dissolved oxygen concentration control device and a sewage treatment tank connected electrically. The dissolved oxygen concentration control device is used to construct a linear dynamic change model of dissolved oxygen concentration, a first sliding mode controller, and a change rate controller; based on the linear dynamic change model of dissolved oxygen concentration, the first sliding mode controller, and the change rate controller, determine the control rate equation and solve the control rate equation; the solution result of the control rate equation is the change amount of the dissolved oxygen concentration control input at the current sampling moment, and the change amount of the dissolved oxygen concentration control input at the current sampling moment represents the difference between the dissolved oxygen concentration control input value at the next sampling moment and the current sampling moment. In other words, solve the control rate equation to obtain the difference between the dissolved oxygen concentration control input value at the next sampling moment and the current sampling moment. The dissolved oxygen concentration control input module of the sewage treatment tank is used to obtain the solution result of the control rate equation as the change amount of the dissolved oxygen concentration control input at the current sampling moment of the sewage treatment tank, and obtain the dissolved oxygen concentration control input value at the next sampling moment of the sewage treatment tank according to the change amount of the dissolved oxygen concentration control input at the current sampling moment and the dissolved oxygen concentration control input value at the current sampling moment. Calculate the change amount between the dissolved oxygen concentration control input value at the current sampling moment and the next sampling moment through the dissolved oxygen concentration control device, and then determine the dissolved oxygen concentration control input value at the next sampling moment, which can accurately determine the dissolved oxygen concentration control input value at the next sampling moment. At the same time, it avoids establishing a mathematical model and solves the problem of ignoring the partial dynamic characteristics of the high-order system when calculating the dissolved oxygen concentration through the mathematical model. It also does not need to establish a neural network and avoid providing data for training the neural network. In addition, the linear dynamic change model of dissolved oxygen concentration, the first sliding mode controller, and the change rate controller constructed in the control system for dissolved oxygen concentration provided by the embodiments of the present invention can avoid being affected by external disturbances and improve the control performance and the robustness of the system.

[0046] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written specification, claims, and drawings.

[0047] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0048] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation to the present invention. In the drawings:

[0049] Figure 1 It is a schematic structural diagram of the control system for dissolved oxygen concentration in the embodiments of the present invention;

[0050] Figure 2 It is a flowchart of the method for solving the control rate equation in the embodiment of the present invention;

[0051] Figure 3 It is a flowchart of the control method of the dissolved oxygen concentration control device in the embodiment of the present invention;

[0052] Figure 4 It is a schematic diagram of the influent flow rate data in the embodiment of the present invention;

[0053] Figure 5 It is an example diagram of the key component concentration in the embodiment of the present invention;

[0054] Figure 6 It is a schematic diagram of the sunny day DO concentration in the embodiment of the present invention;

[0055] Figure 7 It is a schematic diagram of the sunny day DO tracking error in the embodiment of the present invention;

[0056] Figure 8 It is a schematic diagram of the rainy day DO concentration in the embodiment of the present invention;

[0057] Figure 9 It is a schematic diagram of the rainy day DO tracking error in the embodiment of the present invention;

[0058] Figure 10 It is a schematic diagram of the influent flow rate and ammonia nitrogen concentration curve in the embodiment of the present invention;

[0059] Figure 11 It is a schematic diagram of the actual operation reference trajectory of DO in the embodiment of the present invention;

[0060] Figure 12 It is a schematic diagram of the DO control effect under different reference trajectories in the embodiment of the present invention;

[0061] Figure 13 It is a schematic diagram of the DO tracking error effect under different reference trajectories in the embodiment of the present invention;

[0062] Figure 14 It is a flowchart of the dissolved oxygen concentration control method in the embodiment of the present invention. Detailed implementation manners

[0063] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0064] To solve the problems of high dependence on models, low control accuracy, and poor system robustness in the existing technology for controlling the dissolved oxygen concentration, the embodiments of the present invention provide a control system and method for the dissolved oxygen concentration.

[0065] For the convenience of describing the control system for the dissolved oxygen concentration, the same parameter symbols in the embodiments of the present invention represent the same parameter meanings.

[0066] Embodiment

[0067] The embodiments of the present invention provide a control system for the dissolved oxygen concentration, and its structure is as Figure 1 shown, including a sewage treatment tank and a dissolved oxygen concentration control device; wherein, the sewage treatment tank includes a biochemical reaction tank and a secondary sedimentation tank, and the biochemical reaction tank includes an anoxic tank and an aerobic tank. Figure 1 "Controller" in it is the dissolved oxygen concentration control device; S O,5 represents the output dissolved oxygen concentration of the sewage treatment tank, that is, the output dissolved oxygen concentration of the system. The value of the output dissolved oxygen concentration can be obtained by measurement. K L a5 represents the control input of the dissolved oxygen concentration in the sewage treatment tank (that is, the input dissolved oxygen concentration value), that is, the control input of the system.

[0068] The dissolved oxygen concentration control device is electrically connected to the sewage treatment tank.

[0069] The dissolved oxygen concentration control device is used to construct a linear dynamic change model of the dissolved oxygen concentration, a first sliding mode controller, and a rate of change controller; based on the linear dynamic change model of the dissolved oxygen concentration, the first sliding mode controller, and the rate of change controller, determine the control rate equation and solve the control rate equation; the control rate equation is used to calculate the change amount of the dissolved oxygen concentration control input at the current sampling moment, and the change amount of the dissolved oxygen concentration control input at the current sampling moment characterizes the difference between the dissolved oxygen concentration control input value at the next sampling moment and the current sampling moment.

[0070] The sewage treatment tank includes a dissolved oxygen concentration control input module, and the dissolved oxygen concentration control input module is used to obtain the solution result of the control rate equation as the change amount of the dissolved oxygen concentration control input at the current sampling moment of the sewage treatment tank, and determine the dissolved oxygen concentration control input value at the next sampling moment of the sewage treatment tank according to the change amount of the dissolved oxygen concentration control input at the current sampling moment and the dissolved oxygen concentration control input value at the current sampling moment.

[0071] The dissolved oxygen concentration control device calculates the change amount of the dissolved oxygen concentration control input value between two sampling moments. Based on the dissolved oxygen concentration control input value at the current sampling moment and the change amount of the dissolved oxygen concentration control input value between the current sampling moment and the next sampling moment, the dissolved oxygen concentration control input value at the next sampling moment is determined. It can accurately determine the dissolved oxygen concentration control input value at the next sampling moment. During the control process, establishing a mathematical model is avoided, solving the problem of ignoring the partial dynamic characteristics of the high-order system when calculating the dissolved oxygen concentration through the mathematical model. Also, establishing a neural network is avoided, solving the problem of requiring a large amount of data for training the neural network. In addition, the dissolved oxygen concentration linear dynamic change model, the first sliding mode controller, and the rate-of-change controller constructed in the dissolved oxygen concentration control system provided by the embodiments of the present invention can avoid being affected by external disturbances, improving the control performance and the robustness of the system.

[0072] In some alternative embodiments, the dissolved oxygen concentration linear dynamic change model is constructed in the following manner:

[0073] Under the Lipschitz continuity condition and the first preset condition, a dissolved oxygen concentration non-linear dynamic change model is constructed; the first preset condition is that the partial derivative of the dissolved oxygen concentration non-linear dynamic change model with respect to the dissolved oxygen concentration control input value is continuous. The dissolved oxygen concentration non-linear dynamic change model is:

[0074] y(k + 1) = f(y(k), y(k - 1), …, y(k - n y ), u(k), u(k - 1), …, u(k - n u )); (1)

[0075] In formula (1), u(k) ∈ R represents the control input of the system at the k-th sampling moment (i.e., the K L a5 at the k-th sampling moment of the system), y(k) ∈ R represents the output dissolved oxygen concentration of the system at the k-th sampling moment (i.e., the S o,5 ) at the k-th sampling moment of the system), n y represents the input order of the system, n u represents the output order of the system, and f(·) represents an unknown non-linear vector function.

[0076] From a practical perspective, the first preset condition is a typical constraint condition for the input-output change rate of the dissolved oxygen concentration control system. From the perspective of energy conservation, the sewage treatment process satisfies the law of energy conservation. Therefore, a bounded input energy change should produce a bounded output energy change within the system. In other words, the dissolved oxygen concentration non-linear dynamic change model satisfies the following two assumptions:

[0077] Hypothesis 1: Except for finite time points, the partial derivative of the non - linear function f(·) in the non - linear dynamic change model of dissolved oxygen concentration (i.e., formula (1)) with respect to the control input K L and a5 is continuous.

[0078] Hypothesis 2: In the non - linear dynamic change model of dissolved oxygen concentration, for any sampling time k and Δu(k), it satisfies:

[0079] |y(k + 1)-y(k)|≤b|Δu(k)|;

[0080] In the above formula, b is a positive constant, Δu(k) represents the change in the control input of dissolved oxygen concentration at the k - th sampling time, and other parameters have been described above. They will not be elaborated in the embodiments of the present invention here.

[0081] For the non - linear dynamic change model of dissolved oxygen concentration that satisfies Hypothesis 1 and Hypothesis 2, when |Δu(k)|≠0, there exists a time - varying parameter (also known as the pseudo - partial derivative) φ c (k)∈R, such that the non - linear dynamic change model of dissolved oxygen concentration can be converted into a compact - form dynamic linearization model (CFDL data model), and φ c (k) is bounded at any sampling time. By converting the non - linear dynamic change model of dissolved oxygen concentration, a linear dynamic change model of dissolved oxygen concentration is obtained. The linear dynamic change model of dissolved oxygen concentration is:

[0082] Δy(k + 1)=φ c (k)Δu(k); (2)

[0083] In the above formula (2), k represents the k - th sampling time, Δy(k + 1) represents the change in the simulated output value of dissolved oxygen concentration at the (k + 1) - th sampling time in the sewage treatment simulation pool, φ c (k) represents the change rate of dissolved oxygen concentration at the k - th sampling time, and Δu(k) represents the change in the control input of dissolved oxygen concentration at the k - th sampling time in the sewage treatment simulation pool.

[0084] Furthermore, by transforming formula (2), we get:

[0085] y(k + 1)=y(k)+φ c (k)Δu(k); (3)

[0086] The parameters in the above formula (3) have been described above. They will not be elaborated in the embodiments of the present invention here.

[0087] In some alternative embodiments, the sliding mode surface can make the system state reach the equilibrium point of the system and ensure the robustness of the system. The first sliding mode controller is constructed in the following way:

[0088] (1) Adopt fast integration to construct a second-order sliding mode controller in continuous time. Based on the second-order sliding mode controller, construct an integral error term;

[0089] (1) The second-order sliding mode controller is:

[0090]

[0091] In the above formula (4), both α and β are greater than 0, β is a constant, P1 > P2 > 0 and both P1 and P2 are positive odd numbers, t represents time, sgn(e) represents the fast integration term, e(t) represents the tracking error of the system dissolved oxygen concentration, and other parameters have been described above. They will not be elaborated in the embodiments of the present invention.

[0092] The second-order sliding mode controller can ensure that the system state converges to zero within a finite time. The following is the proof process:

[0093] Based on the sliding mode surface (FITSMS sliding mode surface) shown in the second-order sliding mode controller (formula 4), when the tracking error e(t) of the dissolved oxygen concentration is on the sliding mode surface s(t), the tracking error of the dissolved oxygen concentration can converge to zero within a finite time T s and the system reaches stability. If the tracking error of the dissolved oxygen concentration converges completely, then e = 0. Let s(t) = 0. From formula (4), we can get:

[0094]

[0095] By transposing formula (5), we can get:

[0096]

[0097] By differentiating both sides of formula (6), we can get:

[0098]

[0099] The parameters in the above formula (5), formula (6), and formula (7) have been described above. They will not be elaborated in the embodiments of the present invention.

[0100] Let e(T s ) = 0, T s represents the sliding mode surface based on the second-order sliding mode controller. The system state can converge to zero within a finite time T s and the initial error value e(0) is bounded and not zero. By solving formula (7), we can get:

[0101]

[0102] Since the second sliding mode controller contains an integral term of βsgn(e), when the system state is far from the equilibrium point, the tracking error of the dissolved oxygen concentration converges faster. In fact, due to the action of the βsgn(e) integral term, the tracking error of the dissolved oxygen concentration is on the sliding mode surface from the initial moment, thus ensuring the global robustness of the system. Compared with the traditional sliding mode surface, fast integration can accelerate the convergence speed of the sliding mode surface. The following is the proof process that the second sliding mode controller can converge faster:

[0103] Traditional sliding mode surface:

[0104] Traditional sliding mode control cannot guarantee the robustness of the system during the reaching phase. To solve the problem of ensuring the robustness of the system, the tracking error e of the system dissolved oxygen concentration in continuous time is defined (i.e., the difference between the simulated output value of the system dissolved oxygen concentration and the expected output value of the dissolved oxygen concentration), and integral sliding mode control (ISMC) is introduced to eliminate the reaching phase:

[0105] e = y d -y; (9)

[0106] In the above formula (9), e represents the tracking error of the system dissolved oxygen concentration in continuous time, y d represents the expected output value of the dissolved oxygen concentration, and y represents the simulated output value of the system dissolved oxygen concentration;

[0107] Based on formula (9), the ISMC sliding mode surface is:

[0108]

[0109] In the above formula (10), s(t) represents the ISMC sliding mode surface, t represents time, e(t) represents the tracking error of the system dissolved oxygen concentration, and k > 0.

[0110] Based on formula (10), when the integral has an initial value of -e(0) / k, it can ensure that the system state is on the sliding mode surface at the initial moment and there is no reaching phase. Therefore, according to s(t) = 0, we can get: e(t) = e(0)exp(-kt); however, this indicates that the ISMC sliding mode surface cannot guarantee that the system state converges to zero within a finite time. Therefore, based on the ISMC sliding mode surface and combined with integration, integral terminal sliding mode control (ITSMC) is constructed to achieve the convergence of the system state to zero within a finite time. The ITSMC sliding mode surface is:

[0111]

[0112] In the above formula (11), α > 0, p1 and p2 are positive odd numbers satisfying p1 > p2 > 0, and other parameters have been described above. They will not be elaborated in the embodiments of the present invention;

[0113] Based on Equation (11), the integral term is defined as Let e I (0) = -e(0) / α. Then, according to s(t) = 0, we can obtain:

[0114]

[0115] The parameters in the above Equation (8) have been described previously, and will not be elaborated in the embodiments of the present invention;

[0116] Let e(T f ) = 0, and solve for T f , where T f represents that the system state can converge to zero within a finite time T f .

[0117] According to Equation (8), we can obtain T s <T f .

[0118] (2) Based on the second sliding mode controller, the integral error term is defined as:

[0119]

[0120] In the above Equation (13), E(k) represents the integral error term, e(k) represents the tracking error value of the dissolved oxygen concentration at the k-th sampling moment, α is a positive number, k represents the k-th sampling moment, p1 and p2 are positive odd numbers satisfying p1 > p2 > 0, βsgn(e) represents the fast integral term, and other parameters have been described previously, and will not be elaborated in the embodiments of the present invention.

[0121] The second sliding mode controller is discretized, and the discrete form of the second sliding mode controller is defined as follows:

[0122] s(k) = e(k) + TE(k - 1); (14)

[0123] In the above Equation (14), T represents the sampling period.

[0124] (2) Construct a dissolved oxygen concentration tracking error function; the dissolved oxygen concentration tracking error function is used to determine the tracking error value of the dissolved oxygen concentration at the current sampling moment, and the tracking error value of the dissolved oxygen concentration characterizes the difference between the simulated output value of the dissolved oxygen concentration and the expected output value of the dissolved oxygen concentration;

[0125] Based on the second sliding mode controller, a dissolved oxygen concentration tracking error function in discrete time is defined, and the dissolved oxygen concentration tracking error function is:

[0126] e(k) = y d (k) - y(k); (15)

[0127] In the above formula (15), e(k) represents the tracking error value of dissolved oxygen concentration at the kth sampling moment, y d (k) represents the expected output value of dissolved oxygen concentration at the kth sampling moment. Other parameters have been described above and will not be described in detail in the embodiment of the present invention.

[0128] (iii) constructing a first sliding mode controller according to a preset sampling period, an integral error term, a dissolved oxygen concentration tracking error function and a linear dynamic change model of dissolved oxygen concentration, wherein the first sliding mode controller is used to calculate a sliding mode surface at each sampling moment.

[0129] By combining formula (3), formula (13), formula (14) and formula (15), we can get the first sliding mode controller, which is:

[0130]

[0131] In the above formula (16), s(k) represents the sliding surface at the kth sampling time, Δy d (k) represents the expected output change of dissolved oxygen concentration at the kth sampling moment. Other parameters have been described above and will not be described in detail in the embodiment of the present invention.

[0132] In some optional embodiments, for the linear dynamic change model of dissolved oxygen concentration, due to the interference of influent flow and unknown component concentration in the sewage treatment process, the dissolved oxygen concentration tracking error cannot always be on the first sliding surface, which will lead to the deterioration of the global robustness of the system. In order to meet the accessibility of the dissolved oxygen concentration tracking error to the first sliding surface, while accelerating the convergence speed and reducing chattering, the convergence law is defined as follows:

[0133]

[0134] In the above formula (17), m and n represent positive constants, 0<λ<1, sng() is the fast integral term, and s is the sliding surface;

[0135] Discretize the reaching law shown in formula (17) to obtain the reaching rate controller, which is:

[0136] s(k+1)=s(k)-mT|s(k)| λ sgn(s(k))-nTs(k); (18)

[0137] In the above formula (18), m and n represent positive constants, 0<λ<1; T represents the sampling period, and other parameters have been described in the above, and will not be described in detail in the embodiment of the present invention.

[0138] In some optional embodiments, based on the linear dynamic change model of dissolved oxygen concentration, the first sliding mode controller and the trend rate controller, a control rate equation is determined, including:

[0139] By combining the linear dynamic change model of dissolved oxygen concentration (Equation (3)), the first sliding mode controller (Equation (16)), and the rate-of-change controller (Equation (18)), the control rate equation is obtained, and the control rate equation is as follows:

[0140]

[0141] In the above formula (19), Δy d (k) represents the expected output value of the dissolved oxygen concentration between the k-th sampling moment and the next sampling moment.

[0142] In some alternative embodiments, the control rate equation is solved, as Figure 2 shown, and it can be achieved by the following method:

[0143] Step S21: Calculate the change rate of the dissolved oxygen concentration at the current sampling moment of the system;

[0144] Step S22: Calculate the tracking error value of the dissolved oxygen concentration at the current sampling moment of the system;

[0145] Step S23: Obtain the change amount of the expected output of the dissolved oxygen concentration at the current sampling moment of the system;

[0146] Step S24: Substitute the change rate of the dissolved oxygen concentration, the change amount of the expected output of the dissolved oxygen concentration, and the tracking error value of the dissolved oxygen concentration at the current sampling moment into the control rate equation for solution.

[0147] In some alternative embodiments, for the above step S21, calculating the change rate of the dissolved oxygen concentration at the current sampling moment of the system can be achieved in the following manner:

[0148] Adopt an improved projection algorithm to calculate the estimated value of the change rate of the dissolved oxygen concentration as the value of the change rate of the dissolved oxygen concentration.

[0149] Specifically, the estimated value of the change rate of the dissolved oxygen concentration is calculated by the following formula:

[0150]

[0151] In the above formula (20), y(k) represents the simulated output value of the dissolved oxygen concentration at the k-th sampling moment, y(k - 1) represents the simulated output value of the dissolved oxygen concentration at the (k - 1)-th sampling moment, Δu(k - 1) represents the change amount of the dissolved oxygen concentration control input at the (k - 1)-th sampling moment, represents the estimated value of the change rate of the dissolved oxygen concentration at the (k - 1)-th sampling moment, μ is a weighting factor and μ > 0, and other parameters have been described above, and are not elaborated in the embodiments of the present invention here.

[0152] The following formula is used to correct the equation (12) about φ c (k) Find the extreme value and obtain the estimated value of the rate of change of dissolved oxygen concentration:

[0153]

[0154] In the above formula (21), η∈(0,1] represents the step size factor, Indicates (φ c (k)), and other parameters have been described above and will not be described in detail in this embodiment of the present invention.

[0155] It should be noted that since the sewage treatment process is a typical time-varying system with large parameter changes, in order to make formula (13) have a stronger ability to track time-varying parameters, Or |Δu(k-1)|≤ε, The value of is a constant, where ε is a sufficiently small positive number, and the value of ε can be 10 -5 , the specific value can be set according to the actual situation, optional, for The initial value of is not limited in this embodiment of the present invention.

[0156] Accordingly, by combining formula (3), formula (20) and formula (21), the linear dynamic change model of dissolved oxygen concentration can also be expressed as follows:

[0157]

[0158] In the above formula (22), y(k) represents the simulated output value of dissolved oxygen concentration at the kth sampling time of the sewage treatment simulation tank, Represents φ c (k), and other parameters have been described above and will not be described in detail in this embodiment of the present invention.

[0159] It should be noted that the Δy between each two sampling moments d The value of (k) can be set according to the actual situation, so Δy d (k) can be considered as known a priori, when Δy d When (k) is a constant, the above formula (19) can be transformed into:

[0160]

[0161] φ c (k) Use of estimates and will Substituting into formula (23), we can get:

[0162]

[0163] In some alternative embodiments, controlling the input variation amount at the current moment and the input value of the dissolved oxygen concentration at the current moment, and determining the input value of the dissolved oxygen concentration at the next moment of the sewage treatment simulation tank includes:

[0164] Taking the sum of the input variation amount of the dissolved oxygen concentration at the current moment and the input value of the dissolved oxygen concentration at the current moment as the input value of the dissolved oxygen concentration at the next moment of the sewage treatment simulation tank.

[0165] To prove that the control system of the dissolved oxygen concentration has good stability, the following assumptions are proposed:

[0166] Hypothesis 3: For a given bounded expected output value y d (k + 1) of the dissolved oxygen concentration, there always exists a bounded u(k) such that the output of the system approaches y d (k + 1) under the drive of this input value of the dissolved oxygen concentration control;

[0167] Hypothesis 4: For any sampling moment k, when Δu(k) ≠ 0, the sign of the system pseudo partial derivative φ c (k) remains unchanged;

[0168] Among them, Hypothesis 3 is a necessary condition for the controllability of the system. Hypothesis 4 indicates that when the input of the dissolved oxygen concentration control increases, the corresponding output of the dissolved oxygen concentration of the system should not decrease, that is, it is considered the quasi-linear characteristic of the system. Obviously, the dissolved oxygen concentration control system satisfies Hypothesis 3 and Hypothesis 4. Based on this, when the dissolved oxygen concentration control system simultaneously satisfies Hypothesis 1, Hypothesis 2, Hypothesis 3, and Hypothesis 4, when y d (k + 1) = y d = const, based on formula (24), the following can be obtained:

[0169] (1) The system output tracking error converges, and

[0170] (2) The system has input-output stability, that is, the system is BIBO stable, that is, the output y(k) and the input u(k) are bounded.

[0171] The following is the proof process of Hypothesis 3 and Hypothesis 4:

[0172] Based on formula (14), the following one-step forward sliding mode function can be obtained:

[0173] s(k + 1) = e(k + 1) + TE(k); (25)

[0174] Substituting formula (15) into formula (25), and considering formula (3), formula (16) and the following can be obtained:

[0175]

[0176] Transposing formula 26 gives:

[0177] s(k + 1)-s(k)=-mT|s(k)| λ sgn(s(k))-nTs(k);

[0178] Also, since both m and n are greater than 0, therefore:

[0179] When s(k)>0, s(k + 1)-s(k)<0; (27)

[0180] When s(k)<0, s(k + 1)-s(k)>0; (28)

[0181] Combining formula (27) and formula (28), the following formula set 29 can be obtained:

[0182] s(k + 1)<s(k), s(k)>0;

[0183] s(k + 1)>s(k), s(k)<0; (29)

[0184] Therefore, s(k) decreases monotonically, and formula (29) is a necessary and sufficient condition for the existence of discrete quasi-sliding mode states. In other words, under the action of formula (24), the tracking error of the dissolved oxygen concentration of the system can converge to the neighborhood of zero, that is, {e(k)} is bounded. Combining with Theorem 1, it can be known that e(k) can converge to zero within a finite time, that is

[0185] The tracking error of the dissolved oxygen concentration is formula (15). Given that {y d (k)} is bounded and it has been proven that {e(k)} is bounded, so {y(k)} is bounded. From the zero dynamic characteristics of the asymptotic stability of the system, there exist constants a, c, k0 that satisfy:

[0186]

[0187] That is, {u(k)} is bounded.

[0188] In addition, for βsgn(e(k)) in formula (24), due to the discontinuity of the sign function, it will bring relatively large jitter. To reduce the chattering phenomenon, the following function is used to replace the sign function:

[0189]

[0190] where σ>0. To balance robustness and chattering, σ = 2 is selected here.

[0191] To illustrate the control process of the dissolved oxygen concentration control system more clearly, a specific example is used for illustration.

[0192] The dissolved oxygen concentration control system includes a sewage treatment tank and dissolved oxygen concentration control equipment. The sewage treatment tank and the dissolved oxygen concentration control equipment are electrically connected. Still referring to Figure 1 As shown, the sewage treatment tank includes a biochemical reaction tank and a secondary sedimentation tank. Among them, the biochemical reaction tank consists of 5 units. The first 2 units are anoxic tanks, mainly carrying out denitrification reactions to reduce nitrate nitrogen to nitrogen gas; the latter 3 units are aerobic tanks, mainly carrying out nitrification reactions to oxidize ammonia nitrogen into nitrates. The sewage after passing through the biochemical reaction tank enters the secondary sedimentation tank. The secondary sedimentation tank is divided into 10 layers. Its main function is to separate mud and water by precipitation. The upper clear water is discharged after precipitation, and part of the lower sludge is sent to the biochemical reaction tank to participate in the reaction, and part is discharged from the system.

[0193] The sampling period is 15 minutes, and the total operation time is 2 weeks. The influent data of the first week is used to stabilize the system, and the dynamic data of the second week is used to test the controller performance. Set the following control parameters: The controller parameters are set as: T = 10 -5 days, m = 0.1, n = 0.1, α = 10, β = 15, q1 = 3, q2 = 5, λ = 0.6, φ c (0) = 0.2, η = 0.8, μ = 1. The lower and upper limits of the actuator constraint are set as u min = 0 and u max = 360;

[0194] Formulate the following indicators to evaluate the system control effect:

[0195]

[0196] Among them, ISE is the integral of squared error, IAE is the integral of absolute error, and Dev max is the maximum deviation.

[0197] Example 1:

[0198] The influent flow data in the second week on sunny and rainy days is as Figure 4 shown, and the key component concentration (easily biodegradable substrate S s and ammonia nitrogen S NH ) data is as Figure 5 shown.

[0199] When the DO concentration (dissolved oxygen concentration) in the fifth unit is maintained at 1.6 mg / L to 2.4 mg / L, the effluent water quality meets the standard. Based on this, the DO reference trajectory is set as S O5ref = 2 mg / L, and the performance of the dissolved oxygen concentration control equipment is tested on sunny and rainy days respectively. Figure 6 andFigure 7 For the DO concentration control effect on sunny days, where Figure 6 is the DO concentration curve, Figure 7 is the DO tracking error curve; Figure 8 and Figure 9 For the DO concentration control effect on rainy days, where Figure 8 is the DO concentration curve, Figure 9 is the DO tracking error curve;

[0200] The dissolved oxygen concentration control system can ensure that the DO concentration tracks the set value under different weather conditions, and the control effect is better than that of the default traditional controller. The control error of the dissolved oxygen concentration control system is within ±0.01 mg / L. Refer to Table 1 and Table 2 below. Table 1 and Table 2 show the control effects of different control algorithms on DO concentration on sunny days and rainy days from three evaluation indexes of ISE, IAE, and Dev max Starting from these three evaluation indexes, it can be seen that the dissolved oxygen concentration control system is superior to other control algorithms in all indexes. Therefore, the proposed control method has better steady-state performance and is applicable to sewage treatment plants with strong external disturbances.

[0201] Table 1:

[0202]

[0203] Table 2:

[0204]

[0205]

[0206] Example 2:

[0207] The external disturbances received by the dissolved oxygen concentration control system are random and non-periodic. Referring to the influent flow rate data and key component concentration data shown in Figure 10 , the control effects of DO and the tracking errors of DO are analyzed below using three different types of reference trajectories:

[0208] (1) Time-invariant reference trajectory S O5ref = 2 mg / L.

[0209] (2) Time-varying reference trajectory. From the 0th day to the 1st day, S O5ref = 2 mg / L; from the 1st day to the 1.5th day, S O5ref = 2.3 mg / L; from the 1.5th day to the 2nd day, S O5ref = 1.8 mg / L; from the 2nd day to the 2.5th day, S O5ref = 2.2 mg / L; from the 2.5th day to the 3rd day, S O5ref = 2 mg / L.

[0210] (3) Adopt Figure 11 the actual reference trajectory of the DO concentration shown.

[0211] For the control system of the dissolved oxygen concentration provided by the embodiment of the present invention, the control effects on the DO concentration under three reference trajectories are as Figure 12 shown. As can be seen from Figure 12 , the control system of the dissolved oxygen concentration can effectively suppress the non-periodic disturbance in the sewage treatment process and has strong robustness. In addition, Table 3 shows the control effects of the DO concentration under different reference trajectories from three evaluation indexes of ISE, IAE and Dev max . As can be seen from Table 3, the control system of the dissolved oxygen concentration has good control performance, can accurately track the time-varying reference trajectory, and has good dynamic performance.

[0212] Table 3:

[0213]

[0214] Based on the same inventive concept, the embodiment of the present invention also provides a control method for the dissolved oxygen concentration. The flow is as Figure 14 shown, and includes the following steps:

[0215] Step S141: Construct a linear dynamic change model of the dissolved oxygen concentration, a first sliding mode controller and a rate-of-change controller; based on the linear dynamic change model of the dissolved oxygen concentration, the first sliding mode controller and the rate-of-change controller, determine the control rate equation and solve the control rate equation; the control rate equation is used to calculate the change amount of the dissolved oxygen concentration control input at the current sampling moment, and the change amount of the dissolved oxygen concentration control input at the current sampling moment represents the difference between the dissolved oxygen concentration control input values at the next sampling moment and the current sampling moment;

[0216] Step S142: Obtain the solution result of the control rate equation as the change amount of the dissolved oxygen concentration control input at the current sampling moment of the sewage treatment tank, and determine the dissolved oxygen concentration control input value at the next sampling moment of the sewage treatment tank according to the change amount of the dissolved oxygen concentration control input at the current sampling moment and the dissolved oxygen concentration control input value at the current sampling moment.

[0217] Unless otherwise specifically stated, terms such as "processing", "computing", "calculating", "determining", "displaying", etc. can refer to the actions and / or processes of one or more processing or computing systems, or similar devices, which operate on and transform data represented as a physical (e.g., electronic) quantity within the registers or memories of the processing system into other data similarly represented as a physical quantity within the memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different technologies and methods. For example, the data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0218] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in a process can be rearranged without departing from the scope of the present disclosure. The appended method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy recited.

[0219] In the above detailed description, various features are combined in a single embodiment to simplify the present disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the present invention lies in less than all of the features of the single disclosed embodiment. Accordingly, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing alone as a separate preferred embodiment of the present invention.

[0220] Those skilled in the art should also understand that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments herein can be implemented as electronic hardware, computer software, or combinations thereof. To clearly illustrate the interchangeability of hardware and software, the above description of the various illustrative components, blocks, modules, circuits, and steps has been generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and the design constraints imposed on the overall system. Skilled artisans may implement the described functionality in a flexible manner for each particular application, but such implementation decisions should not be construed as departing from the scope of the present disclosure.

[0221] The steps of the methods or algorithms described in connection with the embodiments of this specification may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software modules may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and the storage medium may also exist as discrete components in a user terminal.

[0222] For a software implementation, the techniques described in this application may be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes may be stored in a memory unit and executed by a processor. The memory unit may be implemented within the processor or outside the processor, and in the latter case, it is communicatively coupled to the processor by various means, which are well known in the art.

[0223] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but those of ordinary skill in the art should recognize that the various embodiments may be further combined and arranged. Accordingly, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Further, with respect to the term "comprising" used in the specification or claims, the word is intended to be construed in a manner similar to the term "including" as interpreted when used as a transitional word in a claim. Additionally, any use of the term "or" in the claims or specification is intended to mean "non-exclusive or".

Claims

1. A control system for dissolved oxygen concentration, including a sewage treatment tank, characterized in that, The system further includes: a dissolved oxygen concentration control device; The dissolved oxygen concentration control device is electrically connected to the sewage treatment tank; The dissolved oxygen concentration control device is used to construct a linear dynamic change model of dissolved oxygen concentration, a first sliding mode controller, and a rate of change controller; based on the linear dynamic change model of dissolved oxygen concentration, the first sliding mode controller, and the rate of change controller, determine a control rate equation and solve the control rate equation; the control rate equation is used to calculate the change amount of the dissolved oxygen concentration control input at the current sampling moment, and the change amount of the dissolved oxygen concentration control input at the current sampling moment represents the difference between the dissolved oxygen concentration control input value at the next sampling moment and the dissolved oxygen concentration control input value at the current sampling moment; The sewage treatment tank includes a dissolved oxygen concentration control input module, and the dissolved oxygen concentration control input module is used to obtain the solution result of the control rate equation as the change amount of the dissolved oxygen concentration control input at the current sampling moment of the sewage treatment tank, and determine the dissolved oxygen concentration control input value at the next sampling moment of the sewage treatment tank according to the change amount of the dissolved oxygen concentration control input at the current sampling moment and the dissolved oxygen concentration control input value at the current sampling moment.

2. The system according to claim 1, wherein The linear dynamic change model of dissolved oxygen concentration is constructed by the following method: Under the Lipschitz continuous condition and the first preset condition, construct a nonlinear dynamic change model of dissolved oxygen concentration; the first preset condition is that the partial derivative of the nonlinear dynamic change model of dissolved oxygen concentration with respect to the dissolved oxygen concentration control input value is continuous. Convert the nonlinear dynamic change model of dissolved oxygen concentration to obtain a linear dynamic change model of dissolved oxygen concentration, and the linear dynamic change model of dissolved oxygen concentration is: Δy(k + 1) = φ c (k)Δu(k); In the above formula, k represents the k-th sampling time, y(k) represents the simulated output value of the dissolved oxygen concentration at the k-th sampling time in the sewage treatment simulation pool, and φ c (k) represents the change rate of the dissolved oxygen concentration at the k-th sampling time, and Δu(k) represents the change amount of the control input of the dissolved oxygen concentration at the k-th sampling time in the sewage treatment simulation pool.

3. The system according to claim 2, wherein The first sliding mode controller is constructed by the following method: Adopt fast integration to construct a second sliding mode controller in continuous time, and construct an integral error term based on the second sliding mode controller; Construct a dissolved oxygen concentration tracking error function; the dissolved oxygen concentration tracking error function is used to determine the dissolved oxygen concentration tracking error value at the current sampling moment, and the dissolved oxygen concentration tracking error value represents the difference between the simulated output value of the dissolved oxygen concentration and the expected output value of the dissolved oxygen concentration; According to the preset sampling period, the integral error term, the dissolved oxygen concentration tracking error function, and the linear dynamic change model of dissolved oxygen concentration, construct a first sliding mode controller, and the first sliding mode controller is used to calculate the sliding mode surface at each sampling moment.

4. The system according to claim 3, wherein The integral error term is: In the above formula, E(k) represents the integral error term, e(k) represents the dissolved oxygen concentration tracking error value at the kth sampling moment, α is a positive number, k represents the kth sampling moment, p1 and p2 are positive odd numbers satisfying p1>p2>0, and βsgn(e) represents the fast integration term.

5. The system according to claim 4, wherein The first sliding mode controller is: In the above formula, s(k) represents the sliding mode surface at the k-th sampling moment, and Δy d (k) represents the change in the expected output of the dissolved oxygen concentration at the k-th sampling moment.

6. The system according to claim 5, wherein The rate of change controller is: s(k + 1)= s(k)- mT|s(k)| λ sgn(s(k))- nTs(k); In the above formula, m and n represent positive constants, 0<λ<1; T represents the sampling period.

7. The system according to claim 6, wherein Based on the linear dynamic change model of dissolved oxygen concentration, the first sliding mode controller, and the rate of change controller, determining the control rate equation includes: Combining the formulas of the dissolved oxygen concentration linear dynamic change model, the first sliding mode controller, and the rate-of-change controller, a control rate equation is obtained. The control rate equation is as follows:

8. The system according to claim 2, wherein Solving the control rate equation includes: Calculating the rate of change of the dissolved oxygen concentration at the current sampling moment of the system; Calculating the tracking error value of the dissolved oxygen concentration at the current sampling moment of the system; Obtaining the expected output change amount of the dissolved oxygen concentration at the current sampling moment of the system; Substituting the rate of change of the dissolved oxygen concentration, the expected output change amount of the dissolved oxygen concentration, and the tracking error value of the dissolved oxygen concentration at the current sampling moment into the control rate equation for solution.

9. The system according to claim 8, wherein Calculating the rate of change of the dissolved oxygen concentration at the current sampling moment of the system includes: Using an improved projection algorithm to calculate the estimated value of the rate of change of the dissolved oxygen concentration as the value of the rate of change of the dissolved oxygen concentration.

10. The system according to claim 1, wherein Determining the dissolved oxygen concentration control input value at the next moment of the sewage treatment simulation tank according to the dissolved oxygen concentration control input change amount at the current moment and the dissolved oxygen concentration control input value at the current moment includes: Taking the sum of the dissolved oxygen concentration control input change amount at the current moment and the dissolved oxygen concentration control input value at the current moment as the dissolved oxygen concentration control input value at the next moment of the sewage treatment simulation tank.

11. A method for controlling the dissolved oxygen concentration, characterized in that, Includes: Constructing a dissolved oxygen concentration linear dynamic change model, a first sliding mode controller, and a rate-of-change controller; Based on the dissolved oxygen concentration linear dynamic change model, the first sliding mode controller, and the rate-of-change controller, determining a control rate equation and solving the control rate equation; the control rate equation is used to calculate the dissolved oxygen concentration control input change amount at the current sampling moment, and the dissolved oxygen concentration control input change amount at the current sampling moment represents the difference between the dissolved oxygen concentration control input values at the next sampling moment and the current sampling moment; Obtaining the solution result of the control rate equation as the dissolved oxygen concentration control input change amount at the current sampling moment of the sewage treatment tank, and determining the dissolved oxygen concentration control input value at the next sampling moment of the sewage treatment tank according to the dissolved oxygen concentration control input change amount at the current sampling moment and the dissolved oxygen concentration control input value at the current sampling moment.

12. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the control method of the dissolved oxygen concentration described in claim 11 is implemented.

13. An apparatus for controlling the dissolved oxygen concentration, characterized in that, Includes: A memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the control method of the dissolved oxygen concentration described in claim 11 is implemented.

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