| Model predictive control(MPC)is an advanced control theory method that obtains optimal control signals by continuously predicting the future state of the system,with its ability to handle multiple input and multiple output coupled problems and its high prediction accuracy.The robustness of electromechanical systems has been enhanced and production costs have been reduced.As a result,it is receiving more and more attention.However,the pursuit of better control results in defined application scenarios of the model means more variables to be considered and more times to solve the optimization problem.The direct consequence of this is that the complexity of solving the optimization problem will increase significantly and the frequency of solving the problem will increase significantly,resulting in the waste of computational and communication resources.To address the above problems,this paper starts from the principle of model predictive control algorithm and designs the application rules of the optimal control sequence by combining the hybrid integral-differential event-triggered mechanism,so as to achieve the purpose of reducing the solution frequency.In addition,by designing a suitable adaptive prediction horizon shrinkage scheme,the algorithm reduces the complexity of solving the optimization problem each time by gradually shortening the prediction horizon when solving the optimal control problem,so as to achieve the purpose of reducing the computational resource consumption.The specific research includes the following aspects:Firstly,for linear time-invariant systems,an integrated event-triggered model predictive control scheme is designed by considering both integral and differential information of the error between the optimal state and the actual state.Then,through strict theoretical derivation,the parameter conditions to avoid Zeno effect are obtained,and sufficient conditions to ensure the feasibility of the algorithm and the stability of the system are given.Based on the theoretical analysis results,the classical car-springdamping system and satellite system in electromechanical systems are used to simulate and verify the control effect of the designed algorithm compared with the traditional event triggering scheme and the traditional model predictive control scheme.The results show that the proposed method can significantly reduce the consumption of online computing resources and communication resources while ensuring the control performance.Then,for nonlinear discrete systems,an integral self-triggering mechanism based on system state error is designed.Secondly,according to the relationship between the optimal state and the terminal domain,the adaptive scheme of the prediction time domain is designed,that is,the prediction time domain is shortened as the optimal state approaches the terminal domain.The integrated adaptive prediction horizon selftriggering model predictive control algorithm is analyzed theoretically.The effectiveness of the algorithm is verified by the simulation and comparison of the robot system.It shows that the strategy can reduce the consumption of communication and computing resources by reducing the complexity of optimization problems and reducing the number of solving optimization problems.Finally,for nonlinear discrete systems,an integral-differential self-triggering mechanism based on system state error and a predictive horizon contraction mechanism based on the relationship between the optimal state of the system and the terminal domain are designed.In other words,the trigger times will be relatively reduced in the whole control process,and the real-time prediction horizon will be adaptively shortened as the optimal state gets closer to the terminal.On the premise of theoretical analysis,the algorithm is verified by simulation through the robot chassis model,which shows that the application of this method can significantly reduce the controller’s online calculation and data transmission load without affecting the steady-state control performance. |
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