提出一种基于粒子群算法混合优化的广义预测控制器(generalized predictive control based on particleswarm optimization,简称PSOGPC),将粒子群优化算法(particle swarm optimization,简称PSO)引入到广义预测控制的滚动寻优过程中,有...提出一种基于粒子群算法混合优化的广义预测控制器(generalized predictive control based on particleswarm optimization,简称PSOGPC),将粒子群优化算法(particle swarm optimization,简称PSO)引入到广义预测控制的滚动寻优过程中,有效解决了广义预测控制在被控对象存在约束时难以获得最优预测控制输入及求解复杂的问题。并对普通粒子群优化算法进行了改进,提高了优化过程的求解精度和收敛速度。多种约束情况和对电厂锅炉的主汽温控制系统的仿真结果表明了该方法的有效性和优良的控制性能。展开更多
A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and th...A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.展开更多
对于多输入多输出系统,在控制系统设计时首先要对被控变量和操纵变量进行控制结构选择.Bristol提出的相关增益矩阵(Relative gain array,RGA)法,以及学者们后来提出的各种改进方法,都只适用于稳定系统.本文针对不稳定系统,基于多变量广...对于多输入多输出系统,在控制系统设计时首先要对被控变量和操纵变量进行控制结构选择.Bristol提出的相关增益矩阵(Relative gain array,RGA)法,以及学者们后来提出的各种改进方法,都只适用于稳定系统.本文针对不稳定系统,基于多变量广义预测控制(Generalized predictive control,GPC)的闭环控制律提出了一种控制结构的变量匹配准则.通过对预测时域、控制时域等各个参数的优化选择,使系统闭环稳定;由闭环控制律得到被控变量期望值与操纵变量的相关性矩阵,以此得出控制结构的变量配对方案.通过实例研究表明,对于开环不稳定系统,该方法可以得出正确的变量配对结果.展开更多
文摘提出一种基于粒子群算法混合优化的广义预测控制器(generalized predictive control based on particleswarm optimization,简称PSOGPC),将粒子群优化算法(particle swarm optimization,简称PSO)引入到广义预测控制的滚动寻优过程中,有效解决了广义预测控制在被控对象存在约束时难以获得最优预测控制输入及求解复杂的问题。并对普通粒子群优化算法进行了改进,提高了优化过程的求解精度和收敛速度。多种约束情况和对电厂锅炉的主汽温控制系统的仿真结果表明了该方法的有效性和优良的控制性能。
基金This Project was supported by the National Natural Science Foundation of China (60374037 and 60574036)the Opening Project Foundation of National Lab of Industrial Control Technology (0708008).
文摘A constrained generalized predictive control （GPC） algorithm based on the T-S fuzzy model is presented for the nonlinear system. First, a Takagi-Sugeno （T-S） fuzzy model based on the fuzzy cluster algorithm and the orthogonalleast square method is constructed to approach the nonlinear system. Since its consequence is linear, it can divide the nonlinear system into a number of linear or nearly linear subsystems. For this T-S fuzzy model, a GPC algorithm with input constraints is presented. This strategy takes into account all the constraints of the control signal and its increment, and does not require the calculation of the Diophantine equations. So it needs only a small computer memory and the computational speed is high. The simulation results show a good performance for the nonlinear systems.
文摘对于多输入多输出系统,在控制系统设计时首先要对被控变量和操纵变量进行控制结构选择.Bristol提出的相关增益矩阵(Relative gain array,RGA)法,以及学者们后来提出的各种改进方法,都只适用于稳定系统.本文针对不稳定系统,基于多变量广义预测控制(Generalized predictive control,GPC)的闭环控制律提出了一种控制结构的变量匹配准则.通过对预测时域、控制时域等各个参数的优化选择,使系统闭环稳定;由闭环控制律得到被控变量期望值与操纵变量的相关性矩阵,以此得出控制结构的变量配对方案.通过实例研究表明,对于开环不稳定系统,该方法可以得出正确的变量配对结果.
