Article
Automation & Control Systems
Kexin Ding, Qiang Chen, Yurong Nan, Xiaoye Luo
Summary: This paper presents an adaptive fixed-time neural control scheme for a class of nonlinear uncertain systems with full-state constraints. A novel asymmetric hyperbolic barrier Lyapunov function (AHBLF) is introduced to handle the time-varying constraints of all the system states. An adaptive controller is designed to ensure that the tracking errors converge to the equilibrium point within a fixed time, while the system states remain within predefined time-varying boundaries. The proposed control scheme avoids the singularity problem and does not require prior knowledge of the gain function bounds.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Engineering, Mechanical
Jianfeng Wang, Ping Zhang, Yan Wang, Zhicheng Ji
Summary: This paper investigates the problem of adaptive optimal tracking control for full-state constrained strict-feedback nonlinear systems with input delay. A novel control approach is developed by combining the backstepping design technique and adaptive dynamic programming (ADP) theory. The approach utilizes Pade approximation to handle input delay and barrier Lyapunov functions for state constraints. Neural networks are employed to approximate unknown functions. An adaptive backstepping feedforward controller is developed to convert the tracking task into an equivalent regulation problem. A critic network is constructed within the ADP framework to obtain the optimal control. The resulting controller consists of feedforward and feedback parts, while ensuring that all signals are uniformly ultimately bounded in the closed-loop system.
NONLINEAR DYNAMICS
(2023)
Article
Computer Science, Artificial Intelligence
Chun Xin, Yuan-Xin Li, Choon Ki Ahn
Summary: This paper proposes a novel command filtered backstepping adaptive controller to address the adaptive neural asymptotic tracking issue for uncertain non-strict feedback systems subject to full-state constraints. The control scheme not only deals with full-state constraints effectively but also avoids the "explosion of complexity" issue. The stability analysis proves that the tracking error asymptotically converges to zero, all the variables in the controlled systems are bounded, and all the states are constrained.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Lei Liu, Wei Zhao, Yan-Jun Liu, Shaocheng Tong, Yue-Ying Wang
Summary: This article proposes an adaptive finite-time neural control method which successfully solves multiple objective constraints, finite-time stability, singularity problem by introducing a new Lyapunov function and using a neural network to approximate unknown functions, achieving good tracking effects, and not violating constraint boundaries.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Xuebo Yang, Xiaolong Zheng
Summary: This paper proposes a novel control scheme for position tracking control of an induction motor with completely unknown nonlinearities, utilizing the gradient descent algorithm, adaptive backstepping technique, neural networks, and extended differentiators. The proposed control strategy shows advantages over direct adaptive NN control strategies in simulation examples by providing training for all parameters of NNs and ensuring convergence of both NN approximation error and system tracking error with the help of the gradient descent algorithm and Lyapunov stability criterion.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yuhao Zhou, Xin Wang, Rui Xu
Summary: This paper investigates adaptive tracking control for a class of nonlinear multi-input and multi-output (MIMO) state-constrained systems with input delay and saturation. Neural network is used to approximate the unknown nonlinear uncertainties, and a barrier Lyapunov function is introduced to prevent constraint violation. Smooth non-affine approximate function and a novel auxiliary system are utilized to address input saturation with time delay. Adaptive neural tracking control is developed by combining the command filtering backstepping approach, which effectively avoids the explosion of differentiation and reduces computational burden. The introduced filtering error compensating system significantly improves the system tracking performance. Simulation results are presented to verify the feasibility of the proposed strategy.
Article
Automation & Control Systems
Huifang Min, Shengyuan Xu, Yongmin Li, Zhengqiang Zhang
Summary: This article extends adaptive tracking control to more general nonlinear systems with multiple uncertainties, addressing serious uncertainties by combining different techniques to handle parameter nonlinearities and proposing universal adaptive control strategies.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Qian Wang, Chuang Gao, Yang Cui, Li-Bing Wu
Summary: This paper aims to design an adaptive controller for uncertain strict-feedback stochastic nonlinear systems with dead zone and input saturation. The construction of an asymmetric barrier Lyapunov function is proposed to relax the requirements for initial conditions. To simplify the controller design process, a dead zone-based model of saturation is implemented and neural networks are used to approximate uncertain nonlinear functions and construct an observer to handle the presence of immeasurable state variables. By applying the backstepping technique, a smooth tracking controller with adaptive law is proposed to ensure all the signals in the closed-loop system are semi-globally uniformly ultimately bounded while minimizing the tracking error.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Kun Wang, Xiaoping Liu, Yuanwei Jing
Summary: This paper addresses a finite-time tracking control issue for a class of nonlinear systems with asymmetric time-varying output constraints and input nonlinearities. A novel finite-time command filtered backstepping approach is presented to guarantee finite-time convergence of tracking errors, reduce computation complexity, and ensure output variables are restricted in compact bounding sets. The proposed controller is applied to robot manipulator systems, validating the effectiveness and practicability of the developed control strategy in a simulation example.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dapeng Li, Honggui Han, Junfei Qiao
Summary: This article introduces an adaptive neural learning method for effectively controlling a category of nonlinear strict-feedback systems. By utilizing techniques such as barrier Lyapunov functions and radial basis function neural networks, the method achieves the boundedness of closed-loop system signals and full-state variable constraints while learning unknown functions. Simulation results demonstrate the advantages of this method in terms of tracking accuracy, convergence rate, and computational expense.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Jianhui Wang, Chen Wang, Zhi Liu, C. L. Philip Chen, Chunliang Zhang
Summary: This paper investigates the issue of practical fixed-time control for a category of uncertain nonlinear systems with input dead-zone constraint. An extended radial basis function neural networks (ERBFNNs) adaptive event-triggered control method is developed to compensate for input dead zone and schedule the update of control signals. Based on the fixed-time stability theorem, a practical fixed-time event-triggered controller is established.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Jiaxin Zhang, Kewen Li, Yongming Li
Summary: This paper investigates an adaptive neural-network output feedback optimal control problem for a class of strict-feedback nonlinear systems with unknown internal dynamics, input saturation, and state constraints. Neural networks are employed for approximating the unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states. By utilizing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function, the virtual and actual optimal controllers are successfully developed under the backstepping design framework. Additionally, a simplified reinforcement learning algorithm is designed to achieve optimal control effectively by deriving updating laws from the negative gradient of a simple positive function, ensuring bounded signals in the closed-loop system and allowing the output to follow the reference signal within a bounded error.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Computer Science, Artificial Intelligence
Sihui Zhou, Shuai Sui, Shaocheng Tong
Summary: This paper investigates an adaptive neural network (NN) optimal control problem for a permanent magnet synchronous motor (PMSM) system. The proposed control strategy using barrier performance index functions and barrier Lyapunov functions ensures the stability of the closed-loop system and guarantees the state variables of the PMSM are within given bounds while minimizing the performance index functions. The effectiveness of the developed NN adaptive optimal controller is verified through computer simulation results.
Article
Computer Science, Artificial Intelligence
Junchang Zhai, Huanqing Wang, Jiaqing Tao
Summary: This paper proposes a disturbance-observer-based adaptive neural control approach for addressing the issues of long input delay and dead-zone in nonlinear systems. By introducing an auxiliary system and dynamic surface control, the unknown time-varying external disturbance and approximation error are successfully estimated, improving the disturbance rejection performance of the closed-loop system.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Mechanical
Yiming Guo, Xiaojun Xing, Xiwei Wu, Cihang Wu, Bing Xiao
Summary: This paper investigates the problem of path-following control for parafoil dynamic systems in time-varying wind disturbance. An adaptive path-following controller is developed using the barrier Lyapunov function and backstepping method, considering the yaw rate constraint. The controller ensures the attenuation of disturbances caused by time-varying wind and modeling uncertainties, while avoiding violation of practical constraints on the yaw rate of the parafoil system. Experimental tests demonstrate the performance of the proposed path-following controller for parafoil systems.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Dong-Juan Li, Da-Peng Li
IEEE TRANSACTIONS ON CYBERNETICS
(2018)
Article
Automation & Control Systems
Shu-Min Lu, Da-Peng Li, Yan-Jun Liu
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2019)
Article
Automation & Control Systems
Da-Peng Li, Yan-Jun Liu, Shaocheng Tong, C. L. Philip Chen, Dong-Juan Li
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Article
Automation & Control Systems
Da-Peng Li, Dong-Juan Li
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Dapeng Li, C. L. Philip Chen, Yan-Jun Liu, Shaocheng Tong
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2019)
Article
Automation & Control Systems
Dapeng Li, Lei Liu, Yan-Jun Liu, Shaocheng Tong, C. L. Philip Chen
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Article
Computer Science, Artificial Intelligence
Dapeng Li, Lei Liu, Yan-Jun Liu, Shaocheng Tong, C. L. Philip Chen
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2020)
Article
Automation & Control Systems
Lei Liu, Yan-Jun Liu, Dapeng Li, Shaocheng Tong, Zhanshan Wang
IEEE TRANSACTIONS ON CYBERNETICS
(2020)
Article
Automation & Control Systems
Tingting Gao, Yan-Jun Liu, Dapeng Li, Shaocheng Tong, Tieshan Li
Summary: An adaptive neural network control scheme is developed for a class of stochastic nonlinear systems with time-varying full state constraints in this paper, using RBF NNs and tan-TVBLFs to approximate unknown terms and ensure constraints are never violated. The Lyapunov stability theory is used to prove the effectiveness of the control scheme in maintaining system stability and satisfying constraints.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Dapeng Li, Honggui Han, Junfei Qiao
Summary: This article presents an adaptive output feedback control strategy for nonlinear full-state-constrained systems with unmeasured states. By constructing a stable state observer to estimate the unmeasured states and using nonlinear mappings to directly satisfy full-state constraints, the feasibility conditions for intermediate controllers are avoided. The stability of the closed-loop system is proven using the Lyapunov theorem, and simulation results confirm the effectiveness of the developed strategy.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Yan-Jun Liu, Wei Zhao, Lei Liu, Dapeng Li, Shaocheng Tong, C. L. Philip Chen
Summary: This article investigates the problem of tracking control for a class of nonlinear time-varying full state constrained systems. The intelligent controller and adaptive law are developed by constructing the time-varying asymmetric barrier Lyapunov function (BLF) and combining it with the backstepping algorithm. Neural networks (NNs) are used to approximate the uncertain function. This article considers constraint boundaries that are both related to state and time, making the design of the control algorithm more complex and difficult. The effectiveness of the control algorithm is verified through numerical simulation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Fengyi Yuan, Yan-Jun Liu, Lei Liu, Jie Lan, Dapeng Li, Shaocheng Tong, C. L. Philip Chen
Summary: This article presents an adaptive tracking control scheme for nonlinear multiagent systems under a directed graph and state constraints. The integral barrier Lyapunov functionals (iBLFs) are introduced to relax the limitations of existing methods and provide solutions for state constraints and coupling terms of communication errors. An adaptive distributed controller is designed based on iBLF and backstepping method. The scheme ensures that the output trajectory of followers matches that of the leader and also satisfies the state constraints and coupling terms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Dapeng Li, Honggui Han, Junfei Qiao
Summary: This article introduces an adaptive neural learning method for effectively controlling a category of nonlinear strict-feedback systems. By utilizing techniques such as barrier Lyapunov functions and radial basis function neural networks, the method achieves the boundedness of closed-loop system signals and full-state variable constraints while learning unknown functions. Simulation results demonstrate the advantages of this method in terms of tracking accuracy, convergence rate, and computational expense.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Junfei Qiao, Dapeng Li, Honggui Han
Summary: Wastewater treatment process (WWTP) is an important method to reduce environmental pollution and improve water resources recycling efficiency. An adaptive neural controller is proposed to achieve satisfactory control performance for WWTPs, considering the complexities, uncertainties, nonlinearities, and multitime delays. The effectiveness and practicability of the proposed control method are verified using a benchmark simulation model.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
