Optimized Takagi–Sugeno Fuzzy Mixed H2/H∞ Robust Controller Design Based on CPSOGSA Optimization Algorithm for Hydraulic Turbine Governing System

Abstract

The hydraulic turbine governing system (HTGS) is a complex nonlinear system that regulates the rotational speed and power of a hydro-generator set. In this work, an incremental form of an HTGS nonlinear model was established and the Takagi–Sugeno (T-S) fuzzy linearization and mixed H2/H∞ robust control theory was applied to the design of an HTGS controller. A T-S fuzzy H2/H∞ controller for an HTGS based on modified hybrid particle swarm optimization and gravitational search algorithm integrated with chaotic maps (CPSOGSA) is proposed in this paper. The T-S fuzzy model of an HTGS that integrates multiple-state space equations was established by linearizing numerous equilibrium points. The linear matrix inequality (LMI) toolbox in MATLAB was used to solve the mixed H2/H∞ feedback coefficients using the CPSOGSA intelligent algorithm to optimize the weighting matrix in the process so that each mixed H2/H∞ feedback coefficients in the fuzzy control were optimized under the constraints to improve the performance of the controller. The simulation results show that this method allows the HTGS to perform well in suppressing system frequency deviations. In addition, the robustness of the method to system parameter variations is also verified.

Design of Intelligent Nonlinear H2/H∞ Robust Control Strategy of Diesel Generator-Based CPSOGSA Optimization Algorithm

“…The artificial intelligence CPSOGSA algorithm is a hybrid particle swarm optimization and gravitational search algorithm combined with chaotic mapping [50]. Moreover, the CP-SOGSA is superior and more robust than other methods (such as MSA, GWO, WOA, and so on) [51].…”