Journal
SWARM AND EVOLUTIONARY COMPUTATION
Volume 44, Issue -, Pages 320-334Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.swevo.2018.04.008
Keywords
Neural network; Extremal optimization; Multivariable nonlinear systems; Evolutionary algorithm
Funding
- Zhejiang Provincial Natural Science Foundation of China [LY16F030011, LZ16E050002]
- National Key RD Plan of China [2017YFB0802203]
- National Natural Science Foundation of China [U1736203, 61732021, 61472165]
- Guangdong Provincial Special Funds for Applied Technology Research and Development and Transformation of Important Scientific and Technological Achieve [2016B010124009]
- Zhejiang Province Science and Technology Planning Project [2014C31093, 2015C31157]
- Guangzhou Key Laboratory of Data Security and Privacy Preserving
- Guangdong Key Laboratory of Data Security and Privacy Preserving
- National Joint Engineering Research Center of Network Security Detection and Protection Technology
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The connection weights parameters play important roles in adjusting the performance of PID neural network (PIDNN) for complex control systems. However, how to obtain an optimal set of initial values of these connection weight parameters in a multivariable PIDNN called MPIDNN is still an open issue for system designers and engineers. This paper formulates this issue as a typical constrained optimization problem firstly by minimizing the cumulative sum of the product of exponential time and the system errors, and a real-time penalty function for overshoots of the system outputs, and then proposes an adaptive population extremal optimization-based MPIDNN method called PEO-MPIDNN for the optimal control issue of multivariable nonlinear control systems. The simulation results for two typical multivariable nonlinear control systems have demonstrated the superiority of the proposed PEO-MPIDNN to real-coded genetic algorithm (RCGA) and particle swarm optimization (PSO)-based MPIDNN, traditional MPIDNN with back propagation algorithm, and population extremal optimization based multivariable PID control algorithm in terms of transient-state, steady-state, and robust control performance.
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