A Model Predictive Control Method, Device and Electronic Equipment for Wave Power Generation Device

Abstract

The present application provides a model predictive control method, device and electronic device for a wave power generation device. The predicted time interval is divided into an optimization time interval and an extrapolation time interval. It is set that in the extrapolation time interval, the thrust is controlled by using the extrapolation control laws at each moment calculated in the extrapolation time interval, and according to the operation information such as the predicted values of the system state, the predicted values of the motor thrust and the predicted values of the wave excitation force at each moment in the extrapolation time interval, combined with the operation information of the system state and the predicted values of the wave excitation force at each moment in the optimization time interval, the predictive control sequence is processed to obtain the motor thrust at each moment in the optimization time interval, so that an ideal control scheme can be obtained by using the prediction sequence in a shorter optimization time interval, achieving the same control effect as the control scheme obtained by using the prediction sequence in a longer optimization time interval.

Description

Technical Field

[0001] The present application relates to the field of computer technologies, and in particular, to a model predictive control method, apparatus, and electronic device for a wave power generation device. Background Art

[0002] Currently, traditional model predictive control methods for wave power generation need to set a sufficiently long prediction interval (such as at least two wave periods) to achieve an ideal energy extraction efficiency. This requires solving a high-dimensional optimization problem at each moment, resulting in a large computational load and being unfavorable for the implementation of an actual controller. Summary of the Invention

[0003] To solve the above problems, an object of the embodiments of the present application is to provide a model predictive control method, apparatus, and electronic device for a wave power generation device.

[0004] In a first aspect, an embodiment of the present application provides a model predictive control method for a wave power generation device, including:

[0005] Obtaining the system state of the wave power generation device at the k-th moment and the predicted values of wave excitation forces at each moment from the k-th moment to the (k + N + M - 1)-th moment; where the time interval from the k-th moment to the (k + N - 1)-th moment is the optimization time interval; the time interval from the (k + N)-th moment to the (k + N + M - 1)-th moment is the extrapolation time interval;

[0006] According to the obtained system state of the wave power generation device at the k-th moment, the predicted values of wave excitation forces at each moment from the k-th moment to the (k + N - 1)-th moment, and the predicted values of motor thrusts at each moment from the k-th moment to the (k + N - 1)-th moment, obtaining the predicted values of the system state of the wave power generation system at each moment from the (k + 1)-th moment to the (k + N)-th moment;

[0007] According to the extrapolation control law, the predicted values of wave excitation forces at each moment from the (k + N)-th moment to the (k + M + N - 1)-th moment, and the predicted value of the system state at the (k + N)-th moment obtained, obtaining the predicted values of the system state at each moment within the extrapolation time interval and the predicted values of motor thrusts at each moment within the extrapolation time interval;

[0008] Using the system states, predicted values of motor thrusts, and predicted values of wave excitation forces at each moment within the optimization time interval, and the system states, predicted values of motor thrusts, and predicted values of wave excitation forces at each moment within the extrapolation time interval, obtaining an optimized evaluation index;

[0009] Using an optimization algorithm to process the predicted values of motor thrusts at each moment within the optimization time interval to obtain the optimal predicted values of motor thrusts at each moment within the optimization time interval.

[0010] In a second aspect, an embodiment of the present application further provides a model predictive control device for a wave power generation device, including:

[0011] An acquisition module, configured to acquire the system state of the wave power generation device at the k-th moment and the predicted values of wave excitation forces at each moment from the k-th moment to the (k + N + M - 1)-th moment; wherein, the time interval from the k-th moment to the (k + N - 1)-th moment is an optimization time interval; the time interval from the (k + N)-th moment to the (k + N + M - 1)-th moment is an extrapolation time interval;

[0012] A first processing module, configured to obtain predicted values of the system state of the wave power generation system at the (k + 1)-th moment to the (k + N)-th moment according to the system state of the wave power generation device at the k-th moment obtained, the predicted values of wave excitation forces at each moment from the k-th moment to the (k + N - 1)-th moment, and the predicted values of motor thrusts at each moment from the k-th moment to the (k + N - 1)-th moment;

[0013] A second processing module, configured to obtain predicted values of the system state at each moment within the extrapolation time interval and predicted values of motor thrusts at each moment within the extrapolation time interval according to an extrapolation control law, the predicted values of wave excitation forces at each moment from the (k + N)-th moment to the (k + M + N - 1)-th moment, and the predicted value of the system state at the (k + N)-th moment obtained;

[0014] A third processing module, configured to obtain an optimization evaluation index by using the system state, predicted values of motor thrusts, and predicted values of wave excitation forces at each moment within the optimization time interval, and the system state, predicted values of motor thrusts, and predicted values of wave excitation forces at each moment within the extrapolation time interval;

[0015] A fourth processing module, configured to process the predicted values of motor thrusts at each moment within the optimization time interval by using an optimization algorithm to obtain optimal predicted values of motor thrusts at each moment within the optimization time interval.

[0016] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, it executes the steps of the method described in the first aspect above.

[0017] In a fourth aspect, an embodiment of the present application further provides an electronic device, the electronic device includes a memory, a processor, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the processor to execute the steps of the method described in the first aspect above.

[0018] In the solutions provided in the first to fourth aspects of the embodiments of the present application, the predicted time interval is divided into an optimization time interval and an extrapolation time interval. It is set that in the extrapolation time interval, the thrust is controlled by using the extrapolation control laws at each moment calculated within the extrapolation time interval, and based on the operating information such as the predicted system state values, the predicted motor thrust values, and the predicted wave excitation force values at each moment within the extrapolation time interval, combined with the system state and the operating information of the predicted wave excitation force values at each moment within the optimization time interval, the predictive control sequence is processed to obtain the motor thrust at each moment within the optimization time interval. Compared with the related art where an ideal control scheme can only be obtained by using a predictive sequence within a relatively long optimization time interval, the extrapolation time interval is used to replace a part of the optimization time interval. Thus, an ideal control scheme can be obtained by using a predictive sequence within a shorter optimization time interval, achieving the same control effect as the control scheme obtained by using a predictive sequence within a longer optimization time interval, greatly reducing the computational amount, facilitating the implementation of the controller, and improving the control efficiency. To make the above objects, features, and advantages of the present application more obvious and understandable, the following provides preferred embodiments and, in conjunction with the accompanying drawings, detailed descriptions are as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following briefly introduces the drawings required for use in the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0020] Figure 1 FIG. shows a flowchart of a model predictive control method for a wave power generation device provided in Embodiment 1 of the present application;

[0021] Figure 2 FIG. shows a structural schematic diagram of a model predictive control device for a wave power generation device provided in Embodiment 2 of the present application;

[0022] Figure 3 FIG. shows a structural schematic diagram of an electronic device provided in Embodiment 3 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] In the description of the present application, it should be understood that the orientation or positional relationships indicated by terms such as "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. are based on the orientation or positional relationships shown in the drawings. These are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on the present application.

[0024] In addition, the terms "first" and "second" are only used for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0025] In the present application, unless otherwise clearly specified and limited, terms such as "mounted", "connected", "connected to", "fixed" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0026] Currently, traditional wave power generation model predictive control methods need to set a sufficiently long prediction interval (such as at least 2 wave periods) to achieve an ideal energy extraction efficiency. This requires solving a high-dimensional optimization problem at each moment, resulting in a large amount of calculation and being unfavorable for the implementation of an actual controller.

[0027] Based on this, for the model predictive control method, device, and electronic device of the wave power generation device proposed in the following embodiments of the present application, the predicted time interval is divided into an optimization time interval and an extrapolation time interval. It is set that in the extrapolation time interval, the thrust is controlled by using the extrapolation control law calculated at each moment in the extrapolation time interval, and according to the operation information such as the predicted values of the system state, motor thrust, and wave excitation force at each moment in the extrapolation time interval, combined with the operation information of the system state and the predicted value of the wave excitation force at each moment in the optimization time interval, the predicted control sequence is processed to obtain the motor thrust at each moment in the optimization time interval. Thus, an ideal control scheme can be obtained by using the prediction sequence in a shorter optimization time interval, and the same control effect as the control scheme obtained by using the prediction sequence in a longer optimization time interval can be achieved, greatly reducing the calculation amount, facilitating the implementation of the controller, and improving the control efficiency.

[0028] Before implementing the model predictive control method of the wave power generation device proposed in the present application, the following operations need to be performed first:

[0029] To implement the model predictive control method of the wave power generation device, some parameters need to be determined first, mainly including the optimization time interval N, the extrapolation time interval M, and the extrapolation control law μ. These parameters are selected according to the following process:

[0030] (1) According to information such as the size and motor parameters of the wave power generation device, determine the maximum operating range of the wave power generation device, including the maximum floating body position z m and the maximum floating body speed v m , and the maximum radiation force subsystem state ξ m ;

[0031] (2) For any extrapolation control law μ(*), select its corresponding extrapolation interval length M in the following manner: The value of M should be such that the wave power generation device starts from any system state within the maximum operating range and enters a steady state within M steps under the control of μ, and M should take the smallest possible value under the above conditions;

[0032] (3) Select the optimization interval length N in the following manner: The value of N should be as long as possible, while ensuring that the time required to solve the optimization problem of the above model predictive control on the actual controller is less than the required control update period;

[0033] (4) Select the extrapolation control law μ in the following manner: Its control form is selected as linear resonant control, that is:

[0034] μ(x i )=-R g v i +K g zi , i = k + N, …, k + N + M - 1

[0035] Among them, x i represents the predicted value of the system state at each moment within the extrapolation time interval; μ(x i ) represents the control thrust of the extrapolation control law for the predicted value of the system state at each moment within the extrapolation time interval; v i represents the floating body speed at each moment within the extrapolation time interval; z i represents the floating body position at each moment within the extrapolation time interval; R g represents the equivalent damping coefficient of the motor; K g represents the equivalent elastic coefficient of the motor.

[0036] (5) Select the values of R g and K g in the following manner: First, obtain the range of wave parameters according to the actual sea area conditions; then, search for the values of R g and K g : At each set of values of R g and K g , determine the values of N and M according to the above steps (1) to (3), and conduct numerical simulation to obtain the average efficiency within the above range of wave parameters; finally, select the combination of R g and K g with the highest average efficiency.

[0037] To make the above objects, features, and advantages of the present application more obvious and understandable, the following further details the present application in conjunction with the accompanying drawings and specific embodiments.

[0038] Embodiment 1

[0039] The execution subject of a wave power generation device model predictive control method proposed in this embodiment is the controller of the wave power generation device.

[0040] Refer to Figure 1 the flowchart of the wave power generation device model predictive control method shown, a wave power generation device model predictive control method includes the following specific steps:

[0041] Step 100, obtain the system state of the wave power generation device at the k-th moment and the predicted values of the wave excitation force at each moment from the k-th moment to the k + N + M - 1-th moment; among them, the k-th moment to the k + N - 1-th moment is the optimization time interval; the k + N-th moment to the k + N + M - 1-th moment is the extrapolation time interval.

[0042] In the above step 100, at the k-th moment, observe and obtain the current system state x k , and this state includes:

[0043] x k = [v k , z k , ξ k T

[0044] where v k is the floating body velocity, z k is the floating body position, and ξ k is the state of the radiation force subsystem; the method for observing this state is prior art. At the same time, obtain the predicted values w k , w k+1 , …, w k+N+M-1 .

[0045] where w k , w k+1 , …, w k+N-1 are the predicted values of the wave exciting force at times k, …, k + N - 1 respectively, and N is the optimization interval; w k+N , w k+N+1 , …, w k+N+M-1 are the predicted values of the wave exciting force at times k + N, …, k + N + M - 1 respectively, and M is the extrapolation interval. Here, the length of the prediction interval is N + M, where the first N-step interval is the optimization interval, which refers to the interval where the final optimized control sequence to be solved is located (the final thing to be solved is the first N-step control sequence); the latter M-step interval is the extrapolation interval, which refers to the interval where further forward prediction is performed using the extrapolation control law on the basis of the optimization interval (the control sequences of the latter M steps are replaced by the extrapolation control law, so there is no need to solve them).

[0046] Step 102: Obtain the predicted values of the system state of the wave power generation system from the (k + 1)-th moment to the (k + N)-th moment according to the obtained system state of the wave power generation device at the k-th moment, the predicted values of the wave exciting force at each moment from the k-th moment to the (k + N - 1)-th moment, and the predicted values of the motor thrust at each moment from the k-th moment to the (k + N - 1)-th moment.

[0047] In the above Step 102, first establish the discrete state model f(*) of the wave power generation system according to the following formula:

[0048] x k+1 = f(x k , u k , w k ) (1)

[0049] In Equation 1, x k+1 is the predicted value of the system state at the (k + 1)-th moment, x k is the system state at the k-th moment, u k is the predicted value of the motor thrust at the k-th moment, and w k ​is the wave excitation force at the k-th moment. The discrete state model can be linear or non-linear. The method for establishing the discrete state model is prior art.

[0050] The output energy model of the wave power generation system is established as follows:

[0051] R k = R(x k , u k , w k ) (2)

[0052] In Equation (2), R k is the system output energy between the k-th moment and the (k + 1)-th moment, x k is the system state at the k-th moment, u k is the motor thrust at the k-th moment, and w k is the wave excitation force at the k-th moment.

[0053] By iteratively using the single-step model of Equation (1) above, the predicted values of the system state of the wave power generation system from the (k + 1)-th moment to the (k + N)-th moment can be obtained:

[0054] x k+1 = f(x k , u k , w k )

[0055] x k+2 = f(x k+1 , u k+1 , w k+1 )

[0056] …

[0057] x k+N = f(x k+N-1 , u k+N-1 , w k+N-1 )

[0058] where x k+1 , x k+2 , …, x k+N represent the predicted values of the system state at the (k + 1), (k + 2), …, (k + N) moments, u k , u k+1 , …, u k+n-1 represent any predicted control sequence at the k, (k + 1), …, (k + N - 1) moments, and w k , w k+1 , …, w k+N-1 represent the predicted values of the wave excitation force at the k, (k + 1), …, (k + N - 1) moments.

[0059] The predicted values of the system state of the wave power generation system from the (k + 1)-th moment to the (k + N)-th moment should satisfy the following conditions:

[0060] -v max ≤ v i ≤ v max , i = k + 1, …, k + N

[0061] -z max ≤ z i ≤ z max , i = k + 1, …, k + N

[0062] -u max ≤ u i ≤ u max , i = k, …, k + N - 1

[0063] -du max ≤ u i+1 -u i ≤ du max , i = k, …, k + N - 2

[0064] -p max ≤ R(x i , u i , w i ) ≤ p max , i = k, …, k + N - 1

[0065] where v i is the floating body velocity at the i-th moment, and v max is the maximum floating body velocity; z i is the floating body position at the i-th moment, and z max is the maximum floating body position; u i is the motor thrust at the i-th moment, and u max is the maximum motor thrust; u i+1 is the motor thrust at the (i + 1)-th moment, and du max is the maximum motor thrust change rate; R(x i , u i , w i ) is the system output energy at the i-th moment, and p max is the maximum motor output power.

[0066] The maximum floating body velocity, the maximum floating body position, the maximum motor thrust, the maximum motor thrust change rate, and the maximum motor output power are all pre-cached in the controller.

[0067] The system state prediction model of the wave power generation system from the (k + 1)-th moment to the (k + N)-th moment provides the prediction of the system state at the given initial state x k , wave excitation force sequence w k , w k+1 , …, w k+N-1, and the predictive control sequence u k , u k+1 , …, u k+N-1 Under the condition, calculate the predicted values x k+1 , x k+2 , …, x k+N of the system state within the optimization interval.

[0068] Step 104: According to the extrapolation control law, the predicted values of the wave excitation force at each moment from the (k + N)-th moment to the (k + M + N - 1)-th moment, and the predicted value of the system state at the (k + N)-th moment obtained, obtain the predicted values of the system state at each moment within the extrapolation time interval and the predicted values of the motor thrust at each moment within the extrapolation time interval.

[0069] In the above step 104, the extrapolation control law satisfies the following formula 3:

[0070] μ(x i ) = -R g v i + K g z i , i = k + N, …, k + N + M - 1 (3)

[0071] wherein, x i represents the predicted values of the system state at each moment within the extrapolation time interval; μ(x i ) represents the control thrust of the extrapolation control law at each moment within the extrapolation time interval; v i represents the floating body speed at each moment within the extrapolation time interval; z i represents the floating body position at each moment within the extrapolation time interval; R g represents the equivalent damping coefficient of the motor; K g represents the equivalent elastic coefficient of the motor.

[0072] The equivalent damping coefficient of the motor is pre-stored in the controller.

[0073] Through the above extrapolation control law, the predicted values of the motor thrust at each moment within the extrapolation time interval can be calculated. Therefore, the predicted values of the motor thrust at each moment within the extrapolation time interval are known quantities, while the predicted values of the motor thrust at each moment within the optimization time interval are the quantities to be solved by this method.

[0074] Starting from the system state x k+N , further iteratively use the single-step model of formula 1 to obtain the predicted values of the system state within the extrapolation interval as shown in the following formula:

[0075] x k+N+1 = f(x k+N , μ(x k+N ), w k+N )

[0076] x k+N+2 = f(x k+N+1 , μ(x k+N+1 ), w k+N+1 )

[0077] …

[0078] x k+N+M = f(x k+N+M-1 , μ(u k+N+M-1 ), w k+N+M-1 )

[0079] wherein, x k+N+1 , x k+N+2 , …, x k+N+M are the predicted values of the system state at the (k + N + 1)th, (k + N + 2)th, …, (k + N + M)th moments, w k+N , w k+N+1 , …, w k+N+M-1 are the predicted values of the wave excitation force at the (k + N)th, (k + N + 1)th, …, (k + N + M - 1)th moments, and μ(*) is the extrapolation control law.

[0080] Step 106: Obtain an optimized evaluation index by using the system state, the predicted value of the motor thrust, and the predicted value of the wave excitation force at each moment within the optimized time interval, and the system state, the predicted value of the motor thrust, and the predicted value of the wave excitation force at each moment within the extrapolation time interval.

[0081] Specifically, Step 106 includes the following specific steps (1) to (3):

[0082] (1) Obtain a first evaluation index by using the system state, the motor thrust, and the predicted value of the wave excitation force at each moment within the optimized time interval:

[0083]

[0084] where E1 represents the first evaluation index; R i represents the output energy at the ith moment within the optimized time interval; x i is the system state at the ith moment; u i is the predicted value of the motor thrust at the ith moment; w i is the wave excitation force at the ith moment;

[0085] (2) Obtain a second evaluation index by using the system state, the motor thrust, and the predicted value of the wave excitation force at each moment within the extrapolation time interval:

[0086]

[0087] where E2 represents the second evaluation index; xi represents the system state at the \(i\)-th moment; \(u\) i represents the predicted value of the motor thrust at the \(i\)-th moment, \(w\) i represents the wave excitation force at the \(i\)-th moment;

[0088] (3) Using the obtained first evaluation index and the second evaluation index, obtain the optimized evaluation index \(E\):

[0089] \(E = E_1+E_2\)

[0090] As can be seen from the content of the above steps 100 to 106. For any control prediction control sequence \(u\) within the optimization interval k , \(u\) k+1 , …, \(u\) k+N-1 , the corresponding value of the total evaluation index can be calculated according to the above method, and the satisfaction of the corresponding total constraint conditions can be examined.

[0091] Here, consider an optimization problem: find the value of \(x\) such that \(f(x)\) is maximized and \(g(x)<0\). What the above process does is to clarify the specific expressions of the functions \(f\) and \(g\), that is, how to calculate the values of \(f(x)\) and \(g(x)\) according to any \(x\). Only in this way can the optimal value of \(x\) be solved. Combining with the wave power generation device model predictive control method proposed in this embodiment, \(x\) is the predicted value of the motor thrust at each moment in the optimization time interval, \(f\) is the total evaluation index \(E\), and \(g\) is the following constraint conditions:

[0092] -\(v\) max \(\leq v\) i \(\leq v\) max , \(i = k + 1,\ldots,k + N\)

[0093] -\(z\) max \(\leq z\) i \(\leq z\) max , \(i = k + 1,\ldots,k + N\)

[0094] -\(u\) max \(\leq u\) i \(\leq u\) max , \(i = k,\ldots,k + N - 1\)

[0095] -\(du\) max \(\leq u\) i+1 -\(u\) i \(\leq du\) max , \(i = k,\ldots,k + N - 2\)

[0096] -\(p\) max \(\leq R(x\) i , \(u\) i , \(w\) i )\(\leq p\) max , \(i = k,\ldots,k + N - 1\)

[0097] wherein, v i is the floating body velocity at the i-th moment, and v max is the maximum value of the floating body velocity; z i is the floating body position at the i-th moment, and z max is the maximum value of the floating body position; u i is the motor thrust at the i-th moment, and u max is the maximum value of the motor thrust; u i+1 is the motor thrust at the (i + 1)-th moment, and du max is the maximum value of the motor thrust change rate; R(x i , u i , w i ) is the system output energy at the i-th moment, and p max is the maximum value of the motor output power.

[0098] Step 108: Use an optimization algorithm to process the predicted motor thrust values at each moment within the optimization time interval to obtain the optimal predicted motor thrust values at each moment within the optimization time interval.

[0099] Here, in the process of using an optimization algorithm to process the predicted motor thrust values at each moment within the optimization time interval to obtain the optimal predicted motor thrust values at each moment within the optimization time interval, the optimization evaluation index E should be maximized and the above constraints should be satisfied.

[0100] The specific process of using an optimization algorithm to process the predicted motor thrust values at each moment within the optimization time interval to obtain the optimal predicted motor thrust values at each moment within the optimization time interval is prior art and will not be elaborated here.

[0101] In summary, this embodiment proposes a model predictive control method for a wave power generation device. The predicted time interval is divided into an optimization time interval and an extrapolation time interval. It is set that in the extrapolation time interval, the thrust is controlled by using the extrapolation control law calculated at each moment in the extrapolation time interval, and according to the predicted values of the system state, the predicted values of the motor thrust, and the predicted values of the wave excitation force at each moment in the extrapolation time interval, these operating information, combined with the system state and the predicted values of the wave excitation force at each moment in the optimization time interval, process the predictive control sequence to obtain the motor thrust at each moment in the optimization time interval. Compared with the related art where a long prediction sequence in the optimization time interval is required to obtain an ideal control scheme, using the extrapolation time interval to replace a part of the optimization time interval, so that an ideal control scheme can be obtained by using the prediction sequence in a short optimization time interval, and the same control effect as the control scheme obtained by using the prediction sequence in a long optimization time interval can be achieved, greatly reducing the computational amount, being easy to implement by the controller, and improving the control efficiency.

[0102] Embodiment 2

[0103] This embodiment proposes a model predictive control device for a wave power generation device, which is used to execute the model predictive control method for a wave power generation device proposed in the above Embodiment 1.

[0104] See Figure 2 As shown in the structural schematic diagram of a model predictive control device for a wave power generation device, this embodiment proposes a model predictive control device for a wave power generation device, including:

[0105] An acquisition module 200, configured to acquire the system state of the wave power generation device at the k-th moment and the predicted values of the wave excitation force at each moment from the k-th moment to the k+N+M-1-th moment; wherein, the k-th moment to the k+N-1-th moment is the optimization time interval; the k+N-th moment to the k+N+M-1-th moment is the extrapolation time interval;

[0106] A first processing module 202, configured to obtain the predicted values of the system state of the wave power generation system at the k+1-th moment to the k+N-th moment according to the system state of the wave power generation device at the k-th moment acquired, the predicted values of the wave excitation force at each moment from the k-th moment to the k+N-1-th moment, and the predicted values of the motor thrust at each moment from the k-th moment to the k+N-1-th moment;

[0107] A second processing module 204, configured to obtain the predicted values of the system state at each moment in the extrapolation time interval and the predicted values of the motor thrust at each moment in the extrapolation time interval according to the extrapolation control law, the predicted values of the wave excitation force at each moment from the k+N-th moment to the k+M+N-1-th moment, and the predicted value of the system state at the k+N-th moment obtained;

[0108] The third processing module 206 is configured to obtain an optimization evaluation index by using the system states, motor thrust prediction values, and wave excitation force prediction values at each moment within the optimized time interval, as well as the system states, motor thrust prediction values, and wave excitation force prediction values at each moment within the extrapolated time interval.

[0109] The fourth processing module 208 is configured to process the motor thrust prediction values at each moment within the optimized time interval by using an optimization algorithm to obtain the optimal motor thrust prediction values at each moment within the optimized time interval.

[0110] Specifically, the first processing module is configured to obtain the system state prediction values of the wave power generation system from the (k + 1)-th moment to the (k + N)-th moment according to the system state of the wave power generation device at the k-th moment obtained, the wave excitation force prediction values at each moment from the k-th moment to the (k + N - 1)-th moment, and the motor thrust prediction values at each moment from the k-th moment to the (k + N - 1)-th moment, including:

[0111] x k+1 = f(x k , u k , w k )

[0112] x k+2 = f(x k+1 , u k+1 , w k+1 )

[0113] …

[0114] x k+N = f(x k+N-1 , u k+N-1 , w k+N-1 )

[0115] Wherein, x k+1 , x k+2 , …, x k+N represent the system state prediction values at the (k + 1)-th, (k + 2)-th, …, (k + N)-th moments, u k , u k+1 , …, u k+N-1 represent any predicted control sequences at the k-th, (k + 1)-th, …, (k + N - 1)-th moments, and w k , w k+1 , …, w k+N-1 represent the wave excitation force prediction values at the k-th, (k + 1)-th, …, (k + N - 1)-th moments.

[0116] In summary, this embodiment proposes a model predictive control device for a wave power generation device, which divides the predicted time interval into an optimization time interval and an extrapolation time interval. It is set that in the extrapolation time interval, the thrust is controlled by using the extrapolation control laws at each moment calculated in the extrapolation time interval, and according to the predicted values of the system state, the predicted values of the motor thrust, and the predicted values of the wave excitation force at each moment in the extrapolation time interval, these operating information, combined with the operating information of the system state and the predicted values of the wave excitation force at each moment in the optimization time interval, processes the predictive control sequence to obtain the motor thrust at each moment in the optimization time interval. Compared with the related art where an ideal control scheme can only be obtained by using a prediction sequence in a relatively long optimization time interval, using the extrapolation time interval to replace a part of the optimization time interval, so that an ideal control scheme can be obtained by using a prediction sequence in a relatively short optimization time interval, and the same control effect as the control scheme obtained by using a prediction sequence in a relatively long optimization time interval can be achieved, greatly reducing the computational amount, being easy to implement by the controller, and improving the control efficiency.

[0117] Embodiment 3

[0118] This embodiment proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the wave power generation device model predictive control method described in Embodiment 1 above. For specific implementation, reference can be made to Method Embodiment 1, which will not be elaborated here.

[0119] In addition, referring to Figure 3 the structural schematic diagram of an electronic device shown, this embodiment also proposes an electronic device. The above electronic device includes a bus 51, a processor 52, a transceiver 53, a bus interface 54, a memory 55, and a user interface 56. The above electronic device includes a memory 55.

[0120] In this embodiment, the above electronic device further includes: one or more programs stored on the memory 55 and executable on the processor 52, which are configured to be executed by the above processor for performing the following steps (1) to step (5):

[0121] (1) Obtain the system state of the wave power generation device at the k-th moment and the predicted values of the wave excitation force at each moment from the k-th moment to the k+N+M-1-th moment; wherein, the k-th moment to the k+N-1-th moment is the optimization time interval; the k+N-th moment to the k+N+M-1-th moment is the extrapolation time interval;

[0122] (2) Obtain the predicted system state values of the wave power generation system from the (k + 1)-th moment to the (k + N)-th moment based on the system state of the wave power generation device at the k-th moment obtained, the predicted wave excitation force values at each moment from the k-th moment to the (k + N - 1)-th moment, and the predicted motor thrust values at each moment from the k-th moment to the (k + N - 1)-th moment;

[0123] (3) Obtain the predicted system state values at each moment within the extrapolation time interval and the predicted motor thrust values at each moment within the extrapolation time interval according to the extrapolation control law, the predicted wave excitation force values at each moment from the (k + N)-th moment to the (k + M + N - 1)-th moment, and the predicted system state value at the (k + N)-th moment obtained;

[0124] (4) Obtain an optimization evaluation index by using the system state, the predicted motor thrust values, and the predicted wave excitation force values at each moment within the optimization time interval, as well as the system state, the predicted motor thrust values, and the predicted wave excitation force values at each moment within the extrapolation time interval;

[0125] (5) Use an optimization algorithm to process the predicted motor thrust values at each moment within the optimization time interval to obtain the optimal predicted motor thrust values at each moment within the optimization time interval.

[0126] A transceiver 53 is configured to receive and transmit data under the control of a processor 52.

[0127] Among them, for the bus architecture (represented by bus 51), bus 51 may include any number of interconnected buses and bridges. Bus 51 links together various circuits including one or more processors represented by processor 52 and a memory represented by memory 55. Bus 51 can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art. Therefore, they will not be further described in this embodiment. A bus interface 54 provides an interface between bus 51 and transceiver 53. Transceiver 53 can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. For example: Transceiver 53 receives external data from other devices. Transceiver 53 is used to send the data processed by processor 52 to other devices. Depending on the nature of the computing system, a user interface 56 may also be provided, such as a keypad, a display, a speaker, a microphone, a joystick.

[0128] Processor 52 is responsible for managing bus 51 and general processing, such as running the general operating system as described above. And memory 55 can be used to store the data used by processor 52 when performing operations.

[0129] Optionally, the processor 52 may be, but is not limited to, a central processing unit, a single-chip microcomputer, a microprocessor, or a programmable logic device.

[0130] It can be understood that the memory 55 in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). The memory 55 of the systems and methods described in this embodiment is intended to include, but is not limited to, these and any other suitable types of memory.

[0131] In some embodiments, the memory 55 stores the following elements, executable modules, or data structures, or subsets thereof, or extended sets thereof: an operating system 551 and application programs 552.

[0132] Among them, the operating system 551 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., and is used to implement various basic services and process hardware-based tasks. The application programs 552 include various application programs, such as a media player and a browser, etc., and are used to implement various application services. The program for implementing the method of the embodiments of the present application may be included in the application programs 552.

[0133] In summary, this embodiment proposes a computer-readable storage medium and an electronic device. The predicted time interval is divided into an optimization time interval and an extrapolation time interval. It is set that in the extrapolation time interval, the thrust is controlled by using the extrapolation control laws at each moment calculated in the extrapolation time interval, and based on the operation information such as the predicted values of the system state, the predicted values of the motor thrust, and the predicted values of the wave excitation force at each moment in the extrapolation time interval, combined with the operation information of the system state and the predicted values of the wave excitation force at each moment in the optimization time interval, the predicted control sequence is processed to obtain the motor thrust at each moment in the optimization time interval. Compared with the related art where an ideal control scheme can only be obtained by using the prediction sequence in a relatively long optimization time interval, the extrapolation time interval is used to replace a part of the optimization time interval. Thus, an ideal control scheme can be obtained by using the prediction sequence in a shorter optimization time interval, and the same control effect as the control scheme obtained by using the prediction sequence in a longer optimization time interval can be achieved, greatly reducing the computational amount, facilitating the implementation of the controller, and improving the control efficiency.

[0134] As described above, the foregoing is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A model predictive control method for a wave power generation device, characterized in that, Including: Obtaining the system state of the wave power generation device at the k-th moment and the predicted values of wave excitation forces at each moment from the k-th moment to the (k + N + M - 1)-th moment; wherein, the time interval from the k-th moment to the (k + N - 1)-th moment is the optimization time interval; the time interval from the (k + N)-th moment to the (k + N + M - 1)-th moment is the extrapolation time interval; According to the obtained system state of the wave power generation device at the k-th moment, the predicted values of wave excitation forces at each moment from the k-th moment to the (k + N - 1)-th moment, and the predicted values of motor thrusts at each moment from the k-th moment to the (k + N - 1)-th moment, obtaining the predicted values of the system state of the wave power generation system at each moment from the (k + 1)-th moment to the (k + N)-th moment; According to the extrapolation control law, the predicted values of wave excitation forces at each moment from the (k + N)-th moment to the (k + M + N - 1)-th moment, and the predicted value of the system state at the (k + N)-th moment obtained, obtaining the predicted values of the system state at each moment within the extrapolation time interval and the predicted values of motor thrusts at each moment within the extrapolation time interval; Using the system state, the predicted values of motor thrusts, and the predicted values of wave excitation forces at each moment within the optimization time interval, as well as the system state, the predicted values of motor thrusts, and the predicted values of wave excitation forces at each moment within the extrapolation time interval, obtaining an optimization evaluation index; Using an optimization algorithm to process the predicted values of motor thrusts at each moment within the optimization time interval, obtaining the optimal predicted values of motor thrusts at each moment within the optimization time interval.

2. The method according to claim 1, characterized in that The obtaining the predicted values of the system state of the wave power generation system at each moment from the (k + 1)-th moment to the (k + N)-th moment according to the obtained system state of the wave power generation device at the k-th moment, the predicted values of wave excitation forces at each moment from the k-th moment to the (k + N - 1)-th moment, and the predicted values of motor thrusts at each moment from the k-th moment to the (k + N - 1)-th moment includes: x k+1 = f(x k , u k , w k ) x k+2 = f(x k+1 , u k+1 , w k+1 ) … x k+N = f(x k+N-1 , u k+N-1 , w k+N-1 ) where x k is the system state of the wave power generation device at the k-th moment, and x k+1 , x k+2 , …, x k+N represent the predicted values of the system state at the k+1, k+2, …, k+N moments, u k , u k+1 , …, u k+N-1 represent the predicted values of the motor thrust at the k, k+1, …, k+N-1 moments, w k , w k+1 , …, w k+N-1 represent the predicted values of the wave excitation force at the k, k+1, …, k+N-1 moments.

3. The method according to claim 1, wherein The extrapolation control law includes: The extrapolation control law satisfies the following formula: μ(x i ) = -R g v i +K g z i ,i = k + N,…, k + N + M - 1 Among them, x i represents the predicted value of the system state at each moment within the extrapolation time interval; μ(x i ) represents the thrust controlled by the extrapolation control law of the predicted value of the system state at each moment within the extrapolation time interval; v i represents the floating body speed at each moment within the extrapolation time interval; z i represents the floating body position at each moment within the extrapolation time interval; R g represents the equivalent damping coefficient of the motor; K g represents the equivalent elastic coefficient of the motor.

4. The method according to claim 3, wherein The obtaining the predicted values of the system state at each moment within the extrapolation time interval and the predicted values of motor thrusts at each moment within the extrapolation time interval according to the extrapolation control law, the predicted values of wave excitation forces at each moment from the (k + N)-th moment to the (k + M + N - 1)-th moment, and the predicted value of the system state at the (k + N)-th moment obtained includes: x k+N+1 = f(x k+N , μ(x k+N ), w k+N ) x k+N+2 = f(x k+N+1 , μ(x k+N+1 ), w k+N+1 ) … x k+N+M = f(x k+N+M-1 , μ(u k+N+M-1 ), w k+N+M-1 ) where, x k+N+1 , x k+N+2 , …, x k+N+M represent the predicted values of the system state at times k + N + 1, k + N + 2, …, k + N + M, and w k+N , w k+N+1 , …, w k+N+M-1 represent the predicted values of the wave exciting force at times k + N, k + N + 1, …, k + N + M - 1.

5. The method according to claim 1, wherein The obtaining the optimization evaluation index using the system state, the predicted values of motor thrusts, and the predicted values of wave excitation forces at each moment within the optimization time interval, as well as the system state, the predicted values of motor thrusts, and the predicted values of wave excitation forces at each moment within the extrapolation time interval includes: Using the system state, the predicted values of motor thrusts, and the predicted values of wave excitation forces at each moment within the optimization time interval to obtain a first evaluation index: Among them, E1 represents the first evaluation index; R i represents the output energy at the i-th moment within the optimization time interval; x i is the system state at the i-th moment; u i is the predicted value of the motor thrust at the i-th moment; w i is the wave excitation force at the i-th moment; Using the system state, the predicted values of motor thrusts, and the predicted values of wave excitation forces at each moment within the extrapolation time interval to obtain a second evaluation index: Among them, E2 represents the second evaluation index; x i represents the system state at the i-th moment, u i represents the predicted value of the motor thrust at the i-th moment; w i represents the wave excitation force at the i-th moment; Using the obtained first evaluation index and the second evaluation index to obtain an optimization evaluation index E: E = E1 + E2.

6. The method according to claim 2, wherein The predicted values of the system state of the wave power generation system at each moment from the (k + 1)-th moment to the (k + N)-th moment should satisfy the following conditions: -v max ≤v i ≤v max , i = k+1, …, k+N -z max ≤z i ≤z max , i = k + 1, …, k + N -u max ≤u i ≤u max ,where \(i = k,\ldots,k + N-1\) -du max ≤u i+1 -u i ≤du max , i = k,…,k + N - 2 -p max ≤R(x i ,u i ,w i )≤p max , i = k, …, k + N - 1 Among them, v i is the floating body speed at the i-th moment, and v max is the maximum floating body speed; z i is the floating body position at the i-th moment, and z max is the maximum floating body position; u i is the motor thrust at the i-th moment, and u max is the maximum motor thrust; u i+1 is the motor thrust at the (i + 1)-th moment, and du max is the maximum motor thrust change rate; R(x i , u i , w i ) is the system output energy at the i-th moment, and p max is the maximum motor output power.

7. A model predictive control device for a wave power generation device, characterized in that, Including: An acquisition module, configured to acquire the system state of the wave power generation device at the k-th moment and the predicted values of wave excitation forces at each moment from the k-th moment to the k+N+M-1-th moment; wherein, the time interval from the k-th moment to the k+N-1-th moment is the optimization time interval; the time interval from the k+N-th moment to the k+N+M-1-th moment is the extrapolation time interval; A first processing module, configured to obtain the predicted values of the system state of the wave power generation system at the k+1-th moment to the k+N-th moment according to the acquired system state of the wave power generation device at the k-th moment, the predicted values of wave excitation forces at each moment from the k-th moment to the k+N-1-th moment, and the predicted values of motor thrusts at each moment from the k-th moment to the k+N-1-th moment; A second processing module, configured to obtain the predicted values of the system state at each moment within the extrapolation time interval and the predicted values of motor thrusts at each moment within the extrapolation time interval according to the extrapolation control law, the predicted values of wave excitation forces at each moment from the k+N-th moment to the k+M+N-1-th moment, and the predicted value of the system state at the k+N-th moment obtained; A third processing module, configured to obtain an optimization evaluation index by using the system state, the predicted values of motor thrusts, and the predicted values of wave excitation forces at each moment within the optimization time interval, and the system state, the predicted values of motor thrusts, and the predicted values of wave excitation forces at each moment within the extrapolation time interval; A fourth processing module, configured to process the predicted values of motor thrusts at each moment within the optimization time interval by using an optimization algorithm to obtain the optimal predicted values of motor thrusts at each moment within the optimization time interval.

8. The device according to claim 7, characterized in that, The first processing module, configured to obtain the predicted values of the system state of the wave power generation system at the k+1-th moment to the k+N-th moment according to the acquired system state of the wave power generation device at the k-th moment, the predicted values of wave excitation forces at each moment from the k-th moment to the k+N-1-th moment, and the predicted values of motor thrusts at each moment from the k-th moment to the k+N-1-th moment, includes: x k+1 = f(x k , u k , w k ) x k+2 = f(x k+1 , u k+1 , w k+1 ) … x k+N = f(x k+N-1 , u k+N-1 , w k+N-1 ) where, x k is the system state of the wave power generation device at the k-th moment, x k+1 , x k+2 , …, x k+N represent the predicted values of the system state at the moments k+1, k+2, …, k+N, u k , u k+1 , …, u k+N-1 represent the predicted values of the motor thrust at the moments k, k+1, …, k+N-1, w k , w k+1 , …, w k+N-1 represent the predicted values of the wave excitation force at the moments k, k+1, …, k+N-1.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by a processor, it executes the steps of the method according to any one of claims 1-6 above.

10. An electronic device, characterized in that, The electronic device includes a memory, a processor, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the processor to execute the steps of the method according to any one of claims 1-6.

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