Development of fuzzy logics: from universal logic tools to natural pragmatics and non-standard scales

Tarassov, Valery B.

doi:10.1016/j.procs.2017.11.325

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…is problem can be solved by optimal control methods, including linear matrix inequality (LMI) approach [47,48], Lyapunov-Krasovskii functional [49,50], and fuzzy logic model [51,52], which are proved to be robust MPC controllers for the constraint process in practice.…”

Section: Discussionmentioning

confidence: 99%

Model Predictive Control of Nonlinear System Based on GA‐RBP Neural Network and Improved Gradient Descent Method

Wang

Qing

2021

Complexity

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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.

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