Parallel Constrained Predictive Control based on the Improved Particle Swarm Optimization for Nonlinear Fast Dynamic Systems
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Abstract
As the nonlinear predictive control model (NMPC) has evolved so far, most studies are confined to the slow dynamic nonlinear method, the study difficulty for the general nonlinear systems is mainly derived from optimization algorithm analysis. In fact, most reality control systems are nonlinear and are likely to have limitations. This paper proposed the population selection based improved particle swarm optimization (PS-IPSO) to minimize the computational time of the NMPC algorithm. In the PS-IPSO, the population selection step based on the ranking of population accordance with _tness function evaluation is implemented.
Via simulation results, the improved algorithm's effectiveness is determined by applying it to the highly nonlinear fast dynamic single rotary inverted pendulum (SRIP)system. The solution presented in the paper is computationally feasible for smaller sampling times
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