Parameter Tuning of Generalized Predictive Control Based on Improved Sparrow Search Algorithm
For variable air volume air-conditioning(VAV)system with multiple input multiple output(MIMO),a generalized predictive control(GPC)parameters tuning method based on improved sparrow search algorithm(ISSA)was proposed.Firstly,aiming at the complicated relationship between many parameters of GPC controller and system performance,sparrow search algo-rithm(SSA)was used for parameter tuning to improve the performance of the control system.Secondly,aiming at the problem that traditional SSA has a long convergence period when the fitness function is relatively complex,an ISSA algorithm based on nonlinear population size increase and decrease was proposed to shorten the convergence period of the algorithm.At the same time,the event-triggered mechanism(ETM)was introduced to avoid triggering the ISSA algorithm into local optimality.Finally,the effectiveness and robustness of the proposed algorithm was verified by a semi-physical experiment platform.Experimental results show that,compared with the traditional SSA algorithm,ISSA convergence time can be reduced by 18.61%,and the adjustment time of GPC control system can be reduced by 87.5%and 90%after parameter tuning by ISSA.
万方数据
维普
