A Sliding Mode Robust Control Method for Discrete Time-delay Uncertain Systems

Abstract

The present invention discloses a sliding mode robust control method of a discrete time delay uncertain system. A multi-population chaotic simulated annealing particle swarm optimization algorithm is used to optimize a support vector regression machine (SVR) structure parameter, a combined kernel function support vector regression machine is used to give a system prediction model, a control law is solved through a novel sliding mode reaching law, and the reachability of a sliding mode can be guaranteed. The method is used for the robust control of uncertain discrete system with state time delay.

Description

technical field [0001] The invention relates to a sliding mode robust control method of a discrete time-delay uncertain system, belonging to the technical field of robust control of the time-delay uncertain discrete system. Background technique [0002] In engineering practice, due to the errors in the modeling process and the interference in the operation process, these uncertain factors deeply affect the final control effect of the discrete control system. In addition, with the increasingly complex structure of the actual discrete control system, time delay inevitably exists in the process of signal transmission, calculation and solution, remote control, etc. It may cause system instability and other consequences. Therefore, the analysis and design of robust control algorithms for time-delay discrete uncertain systems has become an engineering application problem to be solved urgently. [0003] Sliding mode control has strong robustness to uncertain factors such as param...

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Problems solved by technology

Among them, the particle swarm optimization algorithm has the advantages of simple structure, easy implementation, and fast convergence speed, but it is easy to fall into local extremum and cannot find the global optimal solution.

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