具有死区输入和输出受限系统的动态面控制
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摘要
对存在死区输入和输出受限的动态非线性系统,给出一种基于动态面的控制策略.设计适当的待逼近未知连续函数,处理动态不确定性和非仿射函数的影响.引入对称障碍Lyapunov函数(barrier Lyapunov function, BLF),保证了BLF有界,实现输出受限.利用Lyapunov稳定性定理,分析得到闭环系统半全局一致最终有界,且满足输出约束.仿真算例验证了该策略的有效性.
An adaptive dynamic surface control strategy is proposed for a class of unmodeled dynamics systems with dead-zone input and output constraint. By designing the proper unknown continuous functions, the dynamic uncertainties and non-affine function are handled. Based on a barrier Lyapunov function(BLF), the boundedness of BLF can be guaranteed and the system output can be constrained. Using Lyapunov Stability theorem, an analysis shows the closed-loop systems are semi-globally uniform and ultimately bounded, and the output constraint is ensured. Simulation example shows the effectiveness of the strategy.
An adaptive dynamic surface control strategy is proposed for a class of unmodeled dynamics systems with dead-zone input and output constraint. By designing the proper unknown continuous functions, the dynamic uncertainties and non-affine function are handled. Based on a barrier Lyapunov function(BLF), the boundedness of BLF can be guaranteed and the system output can be constrained. Using Lyapunov Stability theorem, an analysis shows the closed-loop systems are semi-globally uniform and ultimately bounded, and the output constraint is ensured. Simulation example shows the effectiveness of the strategy.
引文
[1] TEE K P, GE S S, TAY E H. Barrier Lyapunov functions for the control of output-constrained nonlinear systems [J]. Automatica, 2009, 45(4): 918-927.
[2] TEE K P, REN B B, GE S S. Control of nonlinear systems with time-varying output constraints [J]. Automatica, 2011, 47(11): 2511-2516.
[3] REN B B, GE S S, TEE K P, et al. Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function [J]. IEEE Trans on Neural Networks, 2010, 21(8): 1339-1345.
[4] NIU Ben, ZHAO Jun. Barrier Lyapunov functions for the output tracking control of constrained nonlinear switched systems [J]. Syst Control Lett, 2013, 62(10): 963-971.
[5] KIM B S, YOO S J. Adaptive control of nonlinear pure-feedback systems with output constraints: integral barrier Lyapunov functional approach [J]. Int J of Control Autom Syst, 2015, 13(1): 249-256.
[6] NIU Ben, LIU Lina, LIU Yanyan. Adaptive backsteppingbased fuzzy tracking control scheme for outputconstrained nonlinear switched lower triangular systems with timedelays [J]. Neurocomputing 2016, 175: 759-767.
[7] JIANG Zhongping, PRALY L. Design of robust adaptive controllers for nonlinear systems with dynamics uncertainties [J]. Automatica, 1998, 34(7): 825-840.
[8] 张天平, 高志远. 具有动态不确定性的自适应动态面控制 [J]. 控制与决策, 2013, 28(10): 1541-1546, 1553.
[9] TONG Shaocheng, WANG Tong, LI Yongming, et al. Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics [J]. IEEE Trans Cybern, 2014, 44(6): 910-921.
[10] ZHANG Tianping, WANG Ningning, WANG Qin, et al. Adaptive neural control of constrained strict-feedback nonlinear systems with input unmodeled dynamics [J]. Neurocomputing, 2018, 272: 596-605.
[11] NUSSBAUM R D. Some remarks on a conjecture in parameter adaptive control [J]. Syst Contr Lett, 1983, 3(5): 243-246.
[12] GAO Huating, ZHANG Tianping, XIA Xiaonan. Adaptive neural control of stochastic nonlinear systems with unmodeled dynamics and time-varying state delays [J]. J Franklin Inst, 2014, 351(6): 3182-3199.
[2] TEE K P, REN B B, GE S S. Control of nonlinear systems with time-varying output constraints [J]. Automatica, 2011, 47(11): 2511-2516.
[3] REN B B, GE S S, TEE K P, et al. Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function [J]. IEEE Trans on Neural Networks, 2010, 21(8): 1339-1345.
[4] NIU Ben, ZHAO Jun. Barrier Lyapunov functions for the output tracking control of constrained nonlinear switched systems [J]. Syst Control Lett, 2013, 62(10): 963-971.
[5] KIM B S, YOO S J. Adaptive control of nonlinear pure-feedback systems with output constraints: integral barrier Lyapunov functional approach [J]. Int J of Control Autom Syst, 2015, 13(1): 249-256.
[6] NIU Ben, LIU Lina, LIU Yanyan. Adaptive backsteppingbased fuzzy tracking control scheme for outputconstrained nonlinear switched lower triangular systems with timedelays [J]. Neurocomputing 2016, 175: 759-767.
[7] JIANG Zhongping, PRALY L. Design of robust adaptive controllers for nonlinear systems with dynamics uncertainties [J]. Automatica, 1998, 34(7): 825-840.
[8] 张天平, 高志远. 具有动态不确定性的自适应动态面控制 [J]. 控制与决策, 2013, 28(10): 1541-1546, 1553.
[9] TONG Shaocheng, WANG Tong, LI Yongming, et al. Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics [J]. IEEE Trans Cybern, 2014, 44(6): 910-921.
[10] ZHANG Tianping, WANG Ningning, WANG Qin, et al. Adaptive neural control of constrained strict-feedback nonlinear systems with input unmodeled dynamics [J]. Neurocomputing, 2018, 272: 596-605.
[11] NUSSBAUM R D. Some remarks on a conjecture in parameter adaptive control [J]. Syst Contr Lett, 1983, 3(5): 243-246.
[12] GAO Huating, ZHANG Tianping, XIA Xiaonan. Adaptive neural control of stochastic nonlinear systems with unmodeled dynamics and time-varying state delays [J]. J Franklin Inst, 2014, 351(6): 3182-3199.