Diesel engine rotating speed active disturbance rejection control method based on nonlinear scaling

Abstract

The invention discloses a diesel engine rotating speed active disturbance rejection control method based on nonlinear scaling, which comprises the following steps: step 1, constructing a diesel engine rotating speed dynamic model, wherein the diesel engine rotating speed dynamic model comprises total disturbance f (x, t); step 2, constructing a nonlinear scaling function g (z) to scale and reconstruct the tracking error ex; step 3, constructing a lifting order expansion state observer based on the variable xm reconstructed in the step 2, outputting an estimated value # imgabs0 # of the total disturbance f (x, t), and feeding back the estimated value # imgabs0 # of the total disturbance f (x, t) to the diesel engine rotating speed dynamic model in the step 1 as the total disturbance f (x, t); 4, a low-gain control law is constructed, u (t) is output and fed back to the diesel engine rotating speed dynamic model in the step 1, and a closed-loop system is formed. According to the invention, high-frequency noise interference can be effectively suppressed and control input fluctuation can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of diesel engine electronic control, and in particular to a diesel engine speed self-disturbance rejection control method based on nonlinear scaling, which is particularly suitable for precise control of diesel engine speed. Background Art

[0002] Diesel engines are an important research field in the machinery industry. Due to their advantages such as high thermal efficiency, high torque, good economy, wide power range, quick start, easy maintenance, safe operation and long service life, they are widely used in non-road generator sets, road vehicles, ships, agricultural machinery and engineering machinery, and are the main power source for these equipment. Among them, the speed control performance of diesel generators is directly related to the operating stability and efficiency of the system. However, as a typical nonlinear power system, diesel engines face many challenges in their control, including the difficulty in measuring load torque and the significant high-frequency noise that often accompanies speed measurement.

[0003] In power generation applications, diesel generator sets need to cope with complex and variable power loads. When external loads change suddenly or random loads change suddenly, traditional control methods usually passively rely on speed deviations for adjustment, resulting in slow speed response and difficulty in meeting fast and precise control requirements. In addition, traditional speed control schemes usually require staged calibration of control parameters for steady-state and transient conditions, which requires a large calibration workload and poor parameter versatility. The same set of control parameters may cause over-response at low speeds, or even cause wandering.

[0004] At present, the proportional-integral-differential controller is widely used in the field of engineering control because of its simplicity and reliability, and it also plays an important role in the speed control problem. For example, in the research work of Intelligent robust PI adaptive control strategy for speed control of EV(s), the author uses nonlinear least squares method to obtain the variable parameters of the controlled object online, and combines it with the PI controller to achieve the ability to adapt to the changes in model parameters. Another example is "A sudden load speed control strategy for diesel engines for power generation based on PID control" with application number CN202311437487.5. This invention compares the sum of the control outputs of the PID (proportional-integral-differential) controller with the set threshold, combines the positive and negative speed deviation and the speed change rate, and switches the mode of freezing or not for the integral I item, which can effectively reduce the speed overshoot after sudden unloading and shorten the speed recovery time.

[0005] However, the PID controller is based on linear feedback and fails to utilize the system's dynamic model information, so the control accuracy is limited when dealing with nonlinear systems such as diesel engines. At the same time, in order to improve the anti-disturbance ability, it is usually necessary to increase the control gain, but this often causes violent fluctuations in the control input under steady-state conditions, resulting in increased energy consumption and aggravated noise problems.

[0006] In addition to PID controllers, adaptive control, model predictive control (MPC), neural network control, and anti-disturbance control have also been widely studied for speed control. In the research work of Fast Nonlinear Model Predictive Control on FPGA Using Particle Swarm Optimization, the authors used a nonlinear MPC algorithm to solve the nonlinear interference problem in the diesel engine speed model. The experimental results show that this method performs well in transient tracking and disturbance suppression. Another example is the "A Marine Diesel Engine BP Neural Network Combustion-Speed ​​Dual Closed-Loop Control Method" with application number CN201910045064.6. This invention provides a diesel engine BP neural network combustion-speed dual closed-loop control algorithm, which reduces fuel consumption and reduces working noise. However, due to the limitation of computing resources of embedded control systems, the real-time operation of the MPC algorithm still has certain challenges, which also limits the model size of the BP neural network, further limiting the control accuracy.

[0007] Active disturbance rejection control (ADRC) estimates the total disturbance of the system (including internal unmodeled dynamics and external disturbances) in real time through an extended state observer (ESO), showing good robustness and is a potential solution for diesel engine speed control. In the research work of New design of active disturbance rejection control for nonlinear uncertain systems with unknown control input gain, the author designed a new active disturbance rejection control (ADRC) method suitable for nonlinear uncertain systems with unknown control input gain. This method only relies on the information of the control direction, that is, only the positive and negative information of the control gain is required, without determining the specific value of the control gain, so as to effectively deal with the major uncertainty problems caused by the unknown input gain. Another example is "A diesel engine speed cascade active disturbance rejection control system, diesel engine and locomotive" with application number CN202210550397.6. The invention focuses on introducing a diesel engine speed cascade active disturbance rejection control system, including a feedforward controller, an inner loop active disturbance rejection controller and an outer loop active disturbance rejection controller connected in sequence, which provides a basic paradigm for the design of an active disturbance rejection controller for diesel engine speed problems.

[0008] However, existing ADRC controllers generally use fixed control parameters, resulting in a significant trade-off between anti-disturbance capability and control input fluctuations, making it difficult to take both into account at the same time. Therefore, it is urgent to develop a new diesel engine speed control method that can effectively reduce control input fluctuations while enhancing anti-disturbance capability, while meeting the requirements of real-time algorithm operation, thereby comprehensively improving the operating performance and economy of the diesel engine. Summary of the invention

[0009] The purpose of the present invention is to provide a diesel engine speed auto-disturbance control method based on nonlinear scaling, in view of the fact that the conventional diesel engine speed control method in the prior art has significant limitations in suppressing disturbances and reducing control input fluctuations, and conventional control methods (such as PID and MPC) cannot balance the contradiction between disturbance suppression capability and control input fluctuations. In addition, the existing ADRC method still faces the trade-off problem of disturbance suppression and control input fluctuations when the parameters are constant.

[0010] The technical solution adopted to achieve the purpose of the present invention is:

[0011] A diesel engine speed auto-disturbance rejection control method based on nonlinear scaling comprises the following steps:

[0012] Step 1: construct a diesel engine speed dynamic model: the controlled output of the diesel engine speed dynamic model is x(t), x(t)=n(t), n(t) is the diesel engine speed, and the controlled input is u(t), u(t)=M i (t), M i (t) is the indicated torque of the diesel engine, and the diesel engine speed dynamic model includes the total disturbance f(x, t);

[0013] Step 2: Construct a nonlinear scaling function g(z) to adjust the tracking error e x Scaling and reconstruction, e x =x(t)-r(t), r(t) is the reference diesel engine speed, and the reconstructed state variable x m =g(z)e x +r(t);

[0014] Step 3: Reconstruct the variable x based on step 2 m Construct a lifting and lowering extended state observer to output an estimate of the total disturbance f(x,t) Feedback to the diesel engine speed dynamic model in step 1 as the total disturbance f(x,t);

[0015] Step 4: Get the estimated value of the total disturbance f(x,t) based on the nonlinear scaling function of step 2 and step 3 Construct a low-gain control law, output u(t), and feed it back to the diesel engine speed dynamic model in step 1 to form a closed-loop system.

[0016] In the above technical solution, the diesel engine speed dynamic model in step 1 is:

[0017]

[0018] Where: x(t) is the controlled output, x(t) = n(t), n(t) is the diesel engine speed; u(t) is the control input, b0 is the control gain, J is the moment of inertia; m1, m2 and m3 are three coefficients; f(x,t) is the total disturbance.

[0019] In the above technical solution, the nonlinear scaling function in step 2 is:

[0020]

[0021] in, E is the allowable speed error fluctuation range, and c is the parameter to be calibrated.

[0022] In the above technical solution, in step 3, the ascending-descending order extended state observer is:

[0023]

[0024] Among them, β1 and β2 are parameters related to the observer bandwidth. is the total disturbance estimated by the ascending-descending extended state observer, is the estimated value of the first-order derivative of the total disturbance f(x,t), ξ1 and ξ2 are intermediate variables.

[0025] In the above technical solution, β1=ω0 2 , β2=2ω o ,ω o represents the bandwidth of the rising and falling order extended state observer.

[0026] In the above technical solution, in step 4, the low gain control law is:

[0027]

[0028] Obtained from step 3, k p is the gain of the proportional controller in the low gain control law.

[0029] In the above technical solution, k p =ω c ,ω cis the pole position of the closed-loop system after pole configuration.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. Introduction of nonlinear scaling function: This invention introduces a continuously differentiable nonlinear scaling function for the first time in diesel engine speed control. By dynamically adjusting the feedback control gain, it effectively suppresses high-frequency noise interference and reduces control input fluctuations. Compared with the traditional linear feedback mechanism, the nonlinear scaling function can adaptively adjust the scaling amplitude according to the error size, thereby achieving a balance between steady-state control accuracy and dynamic response performance.

[0032] 2. Design of diesel engine speed self-disturbance rejection control (NSADRC) based on nonlinear scaling: The total disturbance of the system and its first-order derivative are accurately estimated by the raising and lowering order extended state observer (SARESO) based on nonlinear scaling function, and the state variables scaled by the scaling function are introduced to further reduce the observer's sensitivity to noise. Compared with the traditional full-order or reduced-order extended state observer, the SARESO designed in this invention improves the estimation precision and accuracy while reducing the high-frequency noise in the total disturbance estimation value under steady-state conditions, and ultimately reduces the fluctuation of the control input.

[0033] 3. Application of low-gain control strategy: This paper proposes a low-gain control law that dynamically adjusts the control input amplitude through a nonlinear scaling function combined with the disturbance estimation results of the ascending-descending extended state observer, effectively reducing the oscillation of the steady-state control input while maintaining excellent anti-disturbance performance under transient conditions. This strategy breaks through the trade-off limitation of traditional control methods between disturbance suppression and control input fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Shown is an overall block diagram of the present invention.

[0035] Figure 2 Shown are the nonlinear scaling function and its first derivative. DETAILED DESCRIPTION

[0036] The present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0037] Example 1

[0038] like Figure 1 As shown, a diesel engine speed auto-disturbance rejection control method based on nonlinear scaling includes the following steps:

[0039] Step 1: construct a diesel engine speed dynamic model, wherein the controlled output of the diesel engine speed dynamic model is the diesel engine speed n(t), and the controlled input is u(t), where u(t)=M i (t), M i (t) is the indicated torque of the diesel engine; the diesel engine speed dynamic model includes the total disturbance f(x, t);

[0040] Step 2: Construct a nonlinear scaling function g(z) to adjust the tracking error e x Scaling and reconstruction, e x =x(t)-r(t), r(t) is the reference diesel engine speed, and the reconstructed state variable x m =g(z)e x +r(t);

[0041] Step 3: Reconstruct the variable x based on step 2 m Construct a lifting and lowering extended state observer to output an estimate of the total disturbance f(x,t) The diesel engine speed dynamic model fed back to step 1 is used as the total disturbance f(x,t):

[0042] Step 4: Get the estimated value of the total disturbance f(x,t) based on the nonlinear scaling function of step 2 and step 3 Construct a low-gain control law, output u(t), and feed it back to the diesel engine speed dynamic model in step 1 to form a closed-loop system.

[0043] Example 2

[0044] like Figure 1 As shown, a diesel engine speed auto-disturbance rejection control method based on nonlinear scaling includes the following steps:

[0045] Step 1: Construct a diesel engine speed dynamic model:

[0046] The diesel engine speed dynamic model includes unmodeled dynamic disturbances and external disturbances. Specifically, the diesel engine speed dynamic model is expressed as:

[0047]

[0048] Where J is the moment of inertia, representing the inertia of the crankshaft and the rigidly connected shaft system, M i (t) is the indicated torque of the diesel engine, M fri (n,t) is the friction torque inside the diesel engine, M load (t) is the external load torque, n(t) is the diesel engine speed, t is the time, The first derivative representing the diesel engine speed, that is, the rate of change of the diesel engine speed will be determined by the moment of inertia, the diesel engine indicated torque, the friction torque, and the external load torque.

[0049] The friction torque M inside the diesel engine fri (n, t), the data measured in the experiment is used to fit the data using the nonlinear least squares method. The friction torque is:

[0050] M fri (n,t)=M kn (n)+M u (n,t)

[0051] Among them, M kn (n) is the quadratic polynomial fitting model of the known part of friction torque, M u (n, t) is the deviation part of the friction torque, which represents the difference between the experimental measurement value and the fitting value. kn (n) is shown in the following formula:

[0052] M kn (n) = m1 + m2n + m3n 2

[0053] Among them, m1, m2 and m3 are the three coefficients after fitting the known part of the friction torque model into a quadratic function

[0054] The friction torque M fri Substitute (n, t) into the diesel engine speed dynamic model, expand it, and simplify it to get:

[0055]

[0056] The control tasks of diesel engine speed control mainly include the following objectives: (1) Speed ​​tracking: Design a suitable diesel engine indicated torque M i (t), so that the diesel engine speed n(t) accurately tracks the predetermined reference signal r(t), that is, the reference diesel engine speed; (2) Fluctuation suppression: In steady-state operation, the control input M is minimized as much as possible. i (t) fluctuation amplitude; (3) anti-disturbance performance: under external load torque M load (t) Mutation and the existence of internal model parameters M u When (n, t) is uncertain, the deviation and steady-state fluctuation of the diesel engine speed n(t) are minimized as much as possible. In order to improve the processing capability of unmodeled dynamic disturbances and external disturbances, the diesel engine dynamics model is expanded to include the total disturbance f(x, t):

[0057]

[0058] Among them, the controlled output x(t) is the diesel engine speed n(t), that is, x(t) = n(t), and the control input u(t) of the system is the diesel engine indicated torque M i(t), i.e., u(t) = M i (t), control gain Dynamic model of the known part The total unknown disturbance, which consists of the disturbances unmodeled inside the system and the difficult-to-measure load torque outside the system, is f(x, t). The reconstructed dynamic model provides a basis for the subsequent design of the controller. At the same time, by clarifying the disturbance source and observable variables, it provides support for constructing an Extended State Observer (ESO).

[0059] Step 2, Design of the non-linear scaling function: To reduce the volatility of the control input u(t), a family of continuously differentiable non-linear scaling functions g(z) is designed to scale and reconstruct the tracking error e x where e x = x(t) - r(t), and the expression of g(z) is:

[0060]

[0061] where is the independent variable of the non-linear scaling function, E is the allowable range of rotational speed error fluctuations, and c is a parameter to be calibrated.

[0062] Figure 2 shows how the parameter c to be calibrated affects the change of the non-linear scaling function curve.

[0063] g(z) can effectively solve the problem of large fluctuations in the control input u(t) under steady-state conditions. g(z) effectively reduces the noise level in the rotational speed signal by simultaneously reducing the feedback control gain and reconstructing the rotational speed signal fed back to the Extended State Observer. When |z| < 1, i.e., when the tracking error |e x | < E, the value range of g(z) is between 0 and 1, and the closer the tracking error e x is to 0, the smaller the value of g(z); when |z| = 1, the derivative of g(z) exists and is 0, which provides the possibility for smooth switching between the steady-state and transient modes. Figure 2 shows the function curve of g(z) and the change of its first derivative.

[0064] By introducing the non-linear scaling function g(z), the present invention reduces the gain of the proportional controller when the tracking error e x is small, thus significantly reducing the fluctuations of the steady-state control input u(t).

[0065] Step 3, Design of the order-elevating and order-reducing Extended State Observer:

[0066] The present invention proposes a lifting-order extended state observer for real-time estimation of the total system disturbance f(x,t) and its first derivative. The observer combines a non-linear scaling function to effectively reduce the influence of noise on the observation accuracy and improve the dynamic response ability of the system.

[0067] State variable reconstruction: Introduce a non-linear scaling function g(z) to scale the system tracking error e x to reduce the noise level. The reconstructed error e xm and the reconstructed state variable x m are respectively expressed as:

[0068] e xm = g(z)e x

[0069] x m = g(z)e x + r(t)

[0070] where e x is the system tracking error, r is the target speed reference signal; g(z) is the non-linear scaling function, and x m is the reconstructed state variable. When the error e x is within the allowable fluctuation range (i.e., |e x | < E), the scaling function g(z) effectively reduces the high-frequency noise in the tracking error; when |e x | ≥ E, g(z) = 1 and the state variable is not affected by the scaling.

[0071] Construction of the lifting-order extended state observer (ARESO) based on the non-linear scaling function:

[0072] In the lifting-order extended state observer, the total disturbance f and its derivative f d are estimated as state variables. The dynamic equations of the observer are defined as follows:

[0073]

[0074] and are the estimated values of the total disturbance f(x,t) and its first derivative f d respectively. β1 = ω0 2 , β2 = 2ω o are the parameters related to the observer bandwidth. ω o represents the bandwidth of the lifting-order extended state observer.

[0075] Elimination of the state variable derivative:

[0076] To avoid directly using the state variable derivative (such as ) caused by high-frequency noise, the present invention eliminates this influence by introducing intermediate variables ξ1 and ξ2.

[0077]

[0078] And further through mathematical transformation, the final form of the following ascending and descending extended state observer is obtained:

[0079]

[0080] Through the design of the above-mentioned ascending and descending extended state observer, the present invention not only effectively reduces the impact of noise on disturbance estimation, but also significantly improves the disturbance response capability of the system. This improvement is largely due to the introduction of the nonlinear scaling function g(z), which plays a core role in the entire observer design.

[0081] Specifically, the nonlinear scaling function g(z) is used to calculate the tracking error e x Dynamic scaling is performed so that when the error is small, the amplitude of the scaled signal is significantly reduced, thereby reducing the interference of high-frequency noise on the estimation result. This scaling mechanism ensures that the extended state observer can avoid noise amplification while ensuring estimation accuracy. In addition, the continuous differentiable characteristics of the scaling function and the design of continuous and zero derivatives at the switching points of transient and steady-state conditions provide a smooth transition when the error approaches the boundary of the allowable fluctuation range, further improving the stability of the system.

[0082] By nonlinearly reconstructing the state variables and error signals, the nonlinear scaling function g(z) limits the high-frequency noise to the input of the observer, avoiding the transmission and amplification of the noise inside the observer. Combined with the scaling characteristics of g(z), the observer can reduce the control gain within a small error range, significantly reducing the control input fluctuation while maintaining good disturbance suppression capabilities.

[0083] In addition, the introduction of nonlinear scaling function also optimizes the performance of the observer in the dynamic response stage, allowing the system to quickly return to a stable state when facing external disturbances or load mutations. Its combination with the low-gain control strategy not only further reduces the oscillation amplitude of the control input, but also improves the overall robustness and efficiency of the control system.

[0084] In summary, the application of the nonlinear scaling function g(z) is the key to the design of the up-down order extended state observer of the present invention, which plays an indispensable and important role in noise reduction, stability improvement and dynamic performance optimization. This innovative design provides a more efficient and robust solution for diesel engine speed control. The observer can be combined with a low-gain control strategy to further optimize the control performance.

[0085] Step 4, low gain control law design:

[0086] The present invention proposes a low-gain control law, which aims to significantly reduce the fluctuation amplitude of the control input while ensuring the disturbance suppression capability, thereby improving the stability and economy of the diesel engine speed control.

[0087] Based on the diesel engine dynamics model, the low gain control law (LGC) designed by the present invention can be expressed as:

[0088]

[0089] Among them, u(t) is the control input, which is fed back to the diesel engine speed dynamic model in step 1 to form a closed-loop system from the reference speed to the actual diesel engine speed, k p =ω c , k p is the gain of the proportional controller in the low-gain control law, ω c is the pole position of the closed-loop system after pole configuration, and its position will directly affect the stability of the closed-loop system. Total disturbance estimated by the raising and lowering order extended state observer.

[0090] The low-gain control law is calculated by changing the controller gain k p Multiplying with the nonlinear scaling function g(z) effectively reduces the control gain when the error is small, thereby reducing the fluctuation of the steady-state control input.

[0091] In the design part of the low-gain control law, the introduction of the nonlinear scaling function enables the control gain to be dynamically adjusted within the error range, effectively reducing the control input oscillation amplitude during steady-state operation, reducing energy consumption and reducing noise; when the external load changes suddenly or the disturbance is large, the control law can quickly increase the gain to ensure that the system has a high disturbance suppression capability and achieves fast and stable dynamic response; the combination of the low-gain control strategy and the rise-fall order extended state observer further enhances the system's adaptability to nonlinear characteristics and uncertain disturbances.

[0092] The above is only a preferred embodiment of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A diesel engine speed auto-disturbance rejection control method based on nonlinear scaling, characterized in that: The following steps are involved: Step 1: construct a diesel engine speed dynamic model: the controlled output of the diesel engine speed dynamic model is x(t), x(t)=n(t), n(t) is the diesel engine speed, and the controlled input is u(t), u(t)=M i (t), M i (t) is the indicated torque of the diesel engine, and the diesel engine speed dynamic model includes the total disturbance f(x, t); Step 2: Construct a nonlinear scaling function g(z) to adjust the tracking error e x Scaling and reconstruction, e x =x(t)-r(t), r(t) is the reference diesel engine speed, and the reconstructed state variable x m =g(z)e x +r(t); Step 3: Reconstruct the variable x based on step 2 m Construct a lifting and lowering extended state observer, output the estimated value f of the total disturbance f(x, t), and feed it back to the diesel engine speed dynamic model in step 1 as the total disturbance f(x, t); Step 4: Get the estimated value of the total disturbance f(x,t) based on the nonlinear scaling function of step 2 and step 3 Construct a low-gain control law, output u(t), and feed it back to the diesel engine speed dynamic model in step 1 to form a closed-loop system.

2. The diesel engine speed auto-disturbance rejection control method based on nonlinear scaling according to claim 1, characterized in that: The diesel engine speed dynamic model in step 1 is: Where: x(t) is the controlled output, x(t) = n(t), n(t) is the diesel engine speed; u(t) is the control input, b0 is the control gain, J is the moment of inertia; m1, m2 and m3 are three coefficients; f(x,t) is the total disturbance.

3. The diesel engine speed auto-disturbance rejection control method based on nonlinear scaling according to claim 1, characterized in that: The nonlinear scaling function in step 2 is: in, E is the allowable speed error fluctuation range, and c is the parameter to be calibrated.

4. The diesel engine speed auto-disturbance rejection control method based on nonlinear scaling according to claim 1, characterized in that: In step 3, the ascending-descending order extended state observer is: Among them, β1 and β2 are parameters related to the observer bandwidth. is the total disturbance estimated by the ascending-descending extended state observer, is the estimated value of the first-order derivative of the total disturbance f(x,t), ξ1 and ξ2 are intermediate variables.

5. The diesel engine speed auto-disturbance rejection control method based on nonlinear scaling according to claim 4, characterized in that: β1=ω0 2 , β2=2ω o ,ω o represents the bandwidth of the rising and falling order extended state observer.

6. The diesel engine speed auto-disturbance rejection control method based on nonlinear scaling according to claim 1, characterized in that: In step 4, the low gain control law is: Obtained from step 3, k p is the gain of the proportional controller in the low gain control law.

7. The diesel engine speed auto-disturbance rejection control method based on nonlinear scaling according to claim 6, characterized in that: k p =ω c ,ω c is the pole position of the closed-loop system after pole configuration.

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