Abstract
A model predictive control (MPC) method based on recursive backpropagation (RBP) neural network and genetic algorithm (GA) is proposed for a class of nonlinear systems with time delays and uncertainties. In the offline modeling stage, a multistep-ahead predictor with GA-RBP neural network is designed, where GA-BP neural network is used as a one-step prediction model and GA is employed to train the initial weights and bias of the BP neural network. The incorporation of GA into RBP can reduce the possibility of the BP neural network falling into a local optimum instead of reaching global optimization. In the online optimizing stage, a multistep-ahead GA-RBP neural network predictor and an improved gradient descent method (IGDM) are proposed to efficiently solve the online optimization problem of nonlinear MPC by minimizing a modified quadratic criterion. The designed MPC strategy can avoid information loss while linearizing the controlled system and computing the Hessian matrix and its inverse matrix. Experimental results show that the proposed approach can reduce the computational burden and improve the performance of MPC (i.e., the maximum overshoots, calculation time, rise time, and RMSE tracking error value) for the solution of nonlinear controlled systems.
Highlights
As an advanced technique, model predictive control (MPC) including dynamic matrix control (DMC), generalized predictive control (GPC), and receding horizon control (RHC) has been commonly used in the industrial processes because of its ability to deal with constraints and time delays and adapt to various dynamic models and control objectives [1,2,3,4]
E development of nonlinear MPC has been motivated by inherent nonlinearity and production requirements in various operating areas. e nonlinear MPC has fascinated enormous attention for the last two decades, which can predict the future behavior of the system via constructing a nonlinear prediction model. e effectiveness of nonlinear MPC depends on the construction of an appropriate firstprinciple or empirical model from industrial or laboratory data and the derivation of an accurate online linear approximation of the nonlinear model for optimization and prediction [6,7,8]
Is paper proposes a novel strategy for nonlinear MPC, in which a recursive neural network is combined with the improved gradient descent method (IGDM) to model the nonlinear dynamic system with time delays and uncertainties and solve the online optimization problem. e receding horizon optimization problem of the nonlinear system is solved by using the proposed IGDM, where the control input sequence can be calculated by solving a modified cost function. e designed control strategy can avoid information loss while linearizing the nonlinear system and solve the online optimization problem on the implementation of MPC
Summary
Model predictive control (MPC) including dynamic matrix control (DMC), generalized predictive control (GPC), and receding horizon control (RHC) has been commonly used in the industrial processes because of its ability to deal with constraints and time delays and adapt to various dynamic models and control objectives [1,2,3,4] In this control strategy, the process behavior is predicted based on an approximate model, and the control sequence is obtained by solving a performance evaluation function. Is paper proposes a novel strategy for nonlinear MPC, in which a recursive neural network is combined with the improved gradient descent method (IGDM) to model the nonlinear dynamic system with time delays and uncertainties and solve the online optimization problem. (3) Combining the GA-RBP neural network predictor with online optimization based on IGDM, we propose a new online MPC algorithm for a class of nonlinear systems with time delays and uncertainties.
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