Adaptive finite‐time parameter optimal control for stochastic nonlinear systems with input nonlinearities using genetic algorithm

Abstract In the article, a fuzzy finite‐time parameter optimal control problem based on genetic algorithm (GA) is considered for stochastic nonlinear systems (SNSs) with input delay and dead zone. Type‐I fuzzy logic systems (Type‐I FLSs) are applied to estimate the uncertain functions in the considered system. The GA is first introduced into the parameter optimal control strategy of strict‐feedback SNSs. Specifically, this article discusses the GA‐based parameter optimal control techniques, wherein the controller gain is determined by solving an offline optimization problem. By designing a novel auxiliary system, the troubles of input delay and dead zone are effectively addressed. By combining the GA‐based parameter optimal control approach with the backstepping technique, a fuzzy finite‐time GA‐based parameter optimal backstepping control strategy is proposed. It guarantees that all states remain finite‐time bounded under the proposed controllers. Meanwhile, the tracking error converges to an interval near zero within finite time. Eventually, a practical simulation example is given to illustrate the validity of the academic results.