CAJ | 학술논문

针对非线性多入多出(MIMO)系统,提出一种基于最小二乘支持向量机(LSSVM)和混沌优化的预测控制策略.预测模型足预测控制的三要素之一.本文给出了基于混沌优化的Chaos-LSSVM算法,在可行域内反复搜索,从而得到最优的LSSVM算法参数,以及最优的LSSVM模型.在线优化是另一个要素.提出了基于变尺度混沌优化的MSC-MPC(变尺度混沌-模型预测控制)算法,可根据控制误差的大小,决定是否缩小搜索范围,从而迅速收敛到最优解.该算法计算简单,容易实现,避免了同类方法复杂的求导、求逆运算.仿真结果显示:Chaos-LSSVM算法和MSC-MPC算法分别具有良好的建模、控制性能.
침대비선성다입다출(MIMO)계통,제출일충기우최소이승지지향량궤(LSSVM)화혼돈우화적예측공제책략.예측모형족예측공제적삼요소지일.본문급출료기우혼돈우화적Chaos-LSSVM산법,재가행역내반복수색,종이득도최우적LSSVM산법삼수,이급최우적LSSVM모형.재선우화시령일개요소.제출료기우변척도혼돈우화적MSC-MPC(변척도혼돈-모형예측공제)산법,가근거공제오차적대소,결정시부축소수색범위,종이신속수렴도최우해.해산법계산간단,용역실현,피면료동류방법복잡적구도、구역운산.방진결과현시:Chaos-LSSVM산법화MSC-MPC산법분별구유량호적건모、공제성능.
Aimed at nonlinear multi-input multi-output (MIMO) system, a predictive control strategy based on least squares support vector machines (LSSVMs) and chaos optimization is proposed. Predictive model is one of the three main factors of predictive control. Chaos-LSSVM algorithm based on chaos optimization is presented to obtain optimal LSSVM parameters and the model by iterative search in the feasible region. Online optimization is another essential factor. MSC-MPC (mutative scale chaos-model predictive control) algorithm based on mutative scale chaos optimization is developed, which can decide whether to reduce the search scope according to the size of control error, thus it can converge to the optimal solution rapidly. The algorithm is easy to compute and implement, and avoids the complicated derivation and inversion of other similar methods. The simulation results show that Chaos-LSSVM algorithm and MSC-MPC algorithm have good modeling and control performance, respectively.