Teaching and learning optimal sliding-mode control method on basis of personality coefficient adjustment

Abstract

The invention discloses a teaching and learning optimal sliding-mode control method on the basis of personality coefficient adjustment. Correction rules include increasing personality coefficients ofstudents when the students make progress in mutual learning procedures; decreasing the personality coefficients of the students if the students gain to a certain extent in self-learning procedures. The teaching and learning optimal sliding-mode control method has the advantages that an improved teaching and learning algorithm on the basis of personality coefficient adjustment is proposed and can be used for designable parameters of optimal sliding-mode controllers, accordingly, chattering phenomena of sliding-mode control can be weakened, the performance of sliding-mode control systems can beimproved, and the practical feasibility of the teaching and learning optimal sliding-mode control method can be improved; in addition, an incentive measures for each personality coefficient is set inthe improved teaching and learning algorithm pertinently for the condition of reduction of the search speeds of algorithms in late periods, and the personality coefficients of the students can be corrected when the students make progress in learning procedures; the local search capability of teaching and learning algorithms can be enhanced by the aid of the personality coefficients and incentive measures for the personality coefficients, the global convergence can be improved, the convergence speeds of the algorithms in the late periods can be increased, and prematurity phenomena can be effectively prevented.

Description

technical field [0001] The invention relates to a control system optimization technology, in particular to a teaching and learning optimization sliding mode control method based on character coefficient adjustment. Background technique [0002] Intelligent optimization algorithm is a kind of heuristic algorithm developed in recent decades. Representative intelligent optimization algorithms include genetic algorithm, particle swarm optimization algorithm, artificial neural network, simulated annealing algorithm, etc. [0003] With the continuous advancement of science and technology, people have put forward higher requirements for efficient optimization technology and precise intelligent computing, which requires continuous research on new intelligent algorithms on the one hand, and continuous research on The existing intelligent algorithm is improved and perfected. At the same time, broadening the application field of intelligent algorithms can not only bring practical bene...

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Problems solved by technology

However, when the system reaches the sliding mode surface, it is often necessary to consider the effects of hysteresis, inertia, and discrete systems. The discontinuous switching characteristics ...

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