模型不确定非线性系统的自适应模糊Backstepping预测控制

郑兰¹, 周卫东¹, 廖成毅¹, 程华²

(1. 哈尔滨工程大学 自动化学院, 150001 哈尔滨; 2. 齐齐哈尔建华机械有限公司, 161006 黑龙江 齐齐哈尔)



摘要:

为解决一类模型不确定严格反馈非线性系统的跟踪控制问题,提出一种使闭环系统稳定且滚动时域性能指标在线最小化的自适应模糊反步预测控制策略.模糊系统用来逼近该设计过程中的未知非线性项,自适应参数直接用来估计最优逼近权值向量范数的平方,从而只有一个自适应参数需要在线调节;同时考虑模糊基函数的性质,所设计的控制律与自适应律均不含模糊基函数项,理论证明该方法设计的控制器保证闭环系统所有信号是半全局有界的,并且跟踪误差收敛于零的某一邻域.该方法所设计的控制器形式简单,计算量小,更易于实际应用,仿真算例验证提出算法的有效性.

关键词:  模型不确定  滚动时域  模糊自适应控制  反步设计  预测控制

DOI：10.11918/j.issn.0367-6234.2014.11.018

分类号:TP273

基金项目:国家自然科学基金(61102107; 61374208).



Adaptive fuzzy backstepping predictive control for a class for nonlinear systems with model uncertainty

ZHENG Lan¹, ZHOU Weidong¹, LIAO Chengyi¹, CHENG Hua²

(1. College of Automation, Harbin Engineering University, 150001 Harbin, China; 2. Qiqihaer jianhua machinery co., LTD, 161006 Qiqihaer, Heilongjiang, China)

Abstract:

To overcome the tracking control problem for a class of strict-feedback nonlinear system with model uncertain, an adaptive fuzzy backstepping predication control algorithm which can make the closed-loop system stable and minimize the receding horizon guaranteed cost on-line is proposed. Fuzzy logic systems are employed to approximate the unknown term in the design process. As the adaptive parameter are directly used to estimate the norm of the optimal approximation weight vector, only one parameter need to be tuned on-line. Considering the property of the fuzzy basis function, the designed control laws and adaptive laws do not contain the fuzzy basis function term. Theoretically, it is proved that the using the constructed controller can guarantee that all signals in closed-loop are semi-globally uniformly ultimately bounded, and the tracking error convergence to a small neighborhood of the origin. As the form of the controller designed in this way is simplicity and the computation is small, this control strategy is easily realized in practice. Finally, the simulation results demonstrate the feasibility of the proposed scheme.

Key words:  model uncertain  receding horizon  fuzzy adaptive control  backstepping design  prediction control