Adaptive Fixed-Time Control for MIMO Nonlinear Systems With Asymmetric Output Constraints Using Universal Barrier Functions

In this note, we propose a novel adaptive fixed-time control scheme for output tracking problems of a class of multi-input multi-output (MIMO) nonlinear systems with asymmetric output constraint requirements, using a new universal barrier function. It is universal in the sense that the proposed scheme is a general one that also works for systems with symmetric constraints or without constraint requirements, without changing the control structure. Novel adaptive estimations and analysis are introduced to address system uncertainties in the fixed-time convergence settings. We show that under the proposed novel control scheme, each element in the system output tracking error vector of the MIMO nonlinear system can converge into small sets near zero with fixed-time convergence rate, while the asymmetric output constraint requirements on each element of the output tracking error are satisfied at all time. The proposed scheme can effectively deal with unmatched system uncertainties and uncertain gain functions. In the end, a simulation example on a two-degree-of-freedom robot manipulator is presented to demonstrate the efficacy of the proposed scheme.

[1]  Wenjie Si,et al.  Adaptive neural control for MIMO stochastic nonlinear pure-feedback systems with input saturation and full-state constraints , 2018, Neurocomputing.

[2]  Andrey Polyakov,et al.  Nonlinear Feedback Design for Fixed-Time Stabilization of Linear Control Systems , 2012, IEEE Transactions on Automatic Control.

[3]  Zongxia Jiao,et al.  Adaptive Backstepping Control of Spacecraft Rendezvous and Proximity Operations With Input Saturation and Full-State Constraint , 2017, IEEE Transactions on Industrial Electronics.

[4]  Yongduan Song,et al.  Neuroadaptive Robotic Control Under Time-Varying Asymmetric Motion Constraints: A Feasibility-Condition-Free Approach , 2020, IEEE Transactions on Cybernetics.

[5]  Yan Lin,et al.  Decentralized adaptive tracking control for a class of interconnected nonlinear time-varying systems , 2015, Autom..

[6]  Yongduan Song,et al.  Removing the Feasibility Conditions Imposed on Tracking Control Designs for State-Constrained Strict-Feedback Systems , 2019, IEEE Transactions on Automatic Control.

[7]  David J. Hill,et al.  Prescribed-Time Consensus and Containment Control of Networked Multiagent Systems , 2019, IEEE Transactions on Cybernetics.

[8]  Xidong Tang,et al.  Adaptive actuator failure compensation for nonlinear MIMO systems with an aircraft control application , 2007, Autom..

[9]  Yongduan Song,et al.  Leader-following control of high-order multi-agent systems under directed graphs: Pre-specified finite time approach , 2018, Autom..

[10]  Yongduan Song,et al.  Time-varying feedback for regulation of normal-form nonlinear systems in prescribed finite time , 2017, Autom..

[11]  Yongduan Song,et al.  Neuro-Adaptive Control With Given Performance Specifications for Strict Feedback Systems Under Full-State Constraints , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Zongyu Zuo,et al.  Nonsingular fixed-time consensus tracking for second-order multi-agent networks , 2015, Autom..

[13]  Yongduan Song,et al.  Time‐varying feedback for stabilization in prescribed finite time , 2019 .

[14]  Deyuan Meng,et al.  Signed-average consensus for networks of agents: a nonlinear fixed-time convergence protocol , 2016 .

[15]  Zhong-Ping Jiang,et al.  Robust adaptive path following of underactuated ships , 2004, Autom..

[16]  Yongduan Song,et al.  Robust Adaptive Fault-Tolerant Control of Mobile Robots With Varying Center of Mass , 2018, IEEE Transactions on Industrial Electronics.

[17]  Yongduan Song,et al.  Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Yongduan Song,et al.  Adaptive Fault-Tolerant PI Tracking Control With Guaranteed Transient and Steady-State Performance , 2017, IEEE Transactions on Automatic Control.

[19]  Jian-Xin Xu,et al.  State-Constrained Iterative Learning Control for a Class Of MIMO Systems , 2013, IEEE Transactions on Automatic Control.

[20]  Yuanqing Xia,et al.  Attitude stabilization of rigid spacecraft with finite‐time convergence , 2011 .

[21]  Yongduan Song,et al.  Neuroadaptive Fault-Tolerant Control of Nonlinear Systems Under Output Constraints and Actuation Faults , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Wei He,et al.  Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function. , 2017, IEEE transactions on cybernetics.

[23]  Z. Zuo,et al.  Non-singular fixed-time terminal sliding mode control of non-linear systems , 2015 .

[24]  Hongwei Xia,et al.  Barrier Lyapunov function-based adaptive control for hypersonic flight vehicles , 2017, Nonlinear Dynamics.

[25]  Yongduan Song,et al.  Design of adaptive finite-time controllers for nonlinear uncertain systems based on given transient specifications , 2016, Autom..

[26]  Keng Peng Tee,et al.  Control of fully actuated ocean surface vessels using a class of feedforward approximators , 2006, IEEE Transactions on Control Systems Technology.

[27]  Dennis S. Bernstein,et al.  Finite-Time Stability of Continuous Autonomous Systems , 2000, SIAM J. Control. Optim..

[28]  Jian-Xin Xu,et al.  Iterative learning control for output-constrained systems with both parametric and nonparametric uncertainties , 2013, Autom..

[29]  Guang-Hong Yang,et al.  Robust Adaptive Fault-Tolerant Control for a Class of Unknown Nonlinear Systems , 2017, IEEE Transactions on Industrial Electronics.

[30]  Xu Jin,et al.  Fault tolerant finite-time leader-follower formation control for autonomous surface vessels with LOS range and angle constraints , 2016, Autom..

[31]  Shaocheng Tong,et al.  Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems , 2017, Autom..

[32]  S. Bhat,et al.  Continuous finite-time stabilization of the translational and rotational double integrators , 1998, IEEE Trans. Autom. Control..

[33]  Lihua Xie,et al.  Adaptive Trajectory Tracking Control of a Fully Actuated Surface Vessel With Asymmetrically Constrained Input and Output , 2018, IEEE Transactions on Control Systems Technology.

[34]  Zhihong Man,et al.  Continuous finite-time control for robotic manipulators with terminal sliding mode , 2003, Autom..

[35]  Zhengtao Ding,et al.  Fixed-Time Consensus Tracking for Multiagent Systems With High-Order Integrator Dynamics , 2018, IEEE Transactions on Automatic Control.

[36]  Ding Zhai,et al.  Prescribed Performance Switched Adaptive Dynamic Surface Control of Switched Nonlinear Systems With Average Dwell Time , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[37]  Hai Lin,et al.  Platoon Formation Control With Prescribed Performance Guarantees for USVs , 2018, IEEE Transactions on Industrial Electronics.

[38]  Zong-Yao Sun,et al.  A new approach to stabilisation of a class of nonlinear systems with an output constraint , 2018, Int. J. Control.

[39]  Francis Eng Hock Tay,et al.  Barrier Lyapunov Functions for the control of output-constrained nonlinear systems , 2009, Autom..

[40]  Xu Jin,et al.  Fault-tolerant iterative learning control for mobile robots non-repetitive trajectory tracking with output constraints , 2018, Autom..

[41]  Bing Chen,et al.  Adaptive neural tracking control for a class of stochastic nonlinear systems , 2014 .