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Adaptive finite-time parameter optimal control for stochastic nonlinear systems with input nonlinearities using genetic algorithm
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DOI：10.1002/asjc.70046.png)
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Abstract
En 中文
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.
Keywords:
auxiliary system
genetic algorithm
parameter optimization method
stochastic nonlinear systems
type-I fuzzy logic systems