Abstract
Setting sights on the problem of input-output constraints in most industrial systems, an implicit generalized predictive control algorithm based on an improved particle swarm optimization algorithm (PSO) is presented in this paper. PSO has the advantages of high precision and fast convergence speed in solving constraint problems. In order to effectively avoid the problems of premature and slow operation in the later stage, combined with the idea of the entropy of system (SR), a new weight attenuation strategy and local jump out optimization strategy are introduced into PSO. The velocity update mechanism is cancelled, and the algorithm is adjusted respectively in the iterative process and after falling into local optimization. The improved PSO is used to optimize the performance index in predictive control. The combination of PSO and gradient optimization for rolling-horizon improves the optimization effect of the algorithm. The simulation results show that the system overshoot is reduced by about 7.5% and the settling time is reduced by about 6% compared with the implicit generalized predictive control algorithm based on particle swarm optimization algorithm (PSO-IGPC).
Highlights
In the development of the industrial field in recent years, predictive control has been widely used in IT, electric power, aerospace, automobile, and other fields because of its wide application range of predictive model, rolling-horizon, and good control effect [1,2,3,4].In 1987, Clarke and Mohtadi propose the generalized predictive control algorithm (GPC) [5].GPC retains the essential characteristics of model algorithm control (MAC) and dynamic matrix control (DMC)
On the basis of removing the speed term, the weight attenuation strategy combined with SR and the threshold judgment mechanism are added, respectively
The improved algorithm is simulated with MATLAB software and compared with other algorithms
Summary
In the development of the industrial field in recent years, predictive control has been widely used in IT, electric power, aerospace, automobile, and other fields because of its wide application range of predictive model, rolling-horizon, and good control effect [1,2,3,4]. Considering that there are many constraints in the actual industrial process, and the traditional GPC usually finds the local optimal solution when solving such problems [27,28], to improve the control effect, the author combines the PSO with the implicit generalized predictive control. It improves the GPC control effect by enhancing PSO optimization near the constraint limit to form a new control algorithm, which is called SPPSO-IGPC.
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