Article
Automation & Control Systems
Liangliang Chen, Chuanjiang Li, Yanchao Sun, Guangfu Ma
Summary: This paper investigates distributed finite-time error constrained tracking control for multiple uncertain Euler-Lagrange systems under directed topology. The research develops a control algorithm using backstepping method and neural network estimation to ensure finite-time stability of the systems. The proposed control strategy shows advantages in numerical simulations compared to existing methods.
INTERNATIONAL JOURNAL OF CONTROL
(2021)
Article
Automation & Control Systems
Yanghe Feng, Zhong Liu
Summary: This article proposes a boundary control scheme for attenuating vibration in an Euler-Bernoulli beam system considering imprecise system parameters, asymmetric input saturation, and external period disturbance. The control scheme uses the backstepping technique and consists of parameter adaptive laws and iterative learning terms. A functional auxiliary system is also devised to compensate for the influence of input nonlinearity. Simulation experiments in MATLAB demonstrate the effectiveness of the suggested controllers, with simulation diagrams showing better control performance for the boundary controller based on parameter adaptive law with iteration term compared to the one without.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Haitao Liu, Guangshuo Du, Xuehong Tian, Lanping Zou
Summary: This article investigates distributed tracking control for multiple Euler-Lagrange systems in the presence of external disturbances and input saturation. Various challenges such as full-state constraints, communication delays, and unmeasured velocities are addressed with novel solutions including an event-triggered scheme, anti-saturation compensation algorithm, adaptive law for external disturbances, and high-gain observer for unmeasured velocities. The stability of the closed-loop system is proven through theorem analysis and the effectiveness of the control strategy is verified through numerical simulations.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Jian Li, Wenqing Xu, Zhaojing Wu
Summary: This paper addresses the fault-tolerant tracking control problem for a class of uncertain robotic systems with time-varying output constraints. The system considers both actuator fault and disturbances, and the dynamic matrices may not be parameterized by unknown parameters or have known nominal parts. Moreover, the reference trajectories and output constraint functions may not be twice continuously differentiable without any time derivatives of them being available for feedback. In this paper, a powerful adaptive control methodology is proposed by incorporating adaptive dynamic compensation technique into the backstepping framework based on Barrier Lyapunov functions. An adaptive state feedback controller is designed with smart choices of adaptive law and virtual controls, which guarantees boundedness of all the states of the closed-loop system and practical tracking of the reference trajectory without violating the output constraints.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Engineering, Mechanical
Kun Jiang, Xuxi Zhang
Summary: An adaptive tracking control scheme based on event-triggered strategy is developed for nonlinear non-strict-feedback systems with input saturation and incompletely known control gain function in this paper. Neural networks are used to deal with unknown nonlinearity and non-lower triangular structure of the systems. The controller is designed using the hyperbolic tangent function to smooth sharp corners, and a relative threshold event-triggered rule is introduced to save communication burden.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Junchang Zhai, Huanqing Wang, Jiaqing Tao, Zuowei He
Summary: This paper proposes a finite time adaptive tracking control scheme based on neural networks for non-strict-feedback uncertain non-linear systems. By introducing an auxiliary system and radial basis function neural networks, the unknown non-linear functions and uncertainties are approximated, and an effective finite time adaptive neural tracking controller is obtained. Simulation results demonstrate the superiority of the proposed scheme.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2023)
Article
Mathematics, Applied
Yanyan Fan, Zhenlin Jin, Xiaoyuan Luo, Baosu Guo
Summary: This paper studies the problem of robust finite-time consensus for Euler-Lagrange multi-agent systems subject to switching topologies and uncertainties. An integral sliding mode control scheme is proposed to achieve good disturbance rejection and finite-time consensus.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Engineering, Aerospace
Erjiang Liu, Ye Yan, Yueneng Yang
Summary: The paper proposes a neural network approximation-based backstepping sliding mode control approach (NN-BSMC) to solve the attitude tracking control problem for spacecraft in the presence of inertial uncertainties, external disturbances, and input saturation. The control scheme combines sliding mode control and backstepping technique to handle the uncertainties and input saturation. A neural network approximator is used to estimate the dynamics uncertainty of the spacecraft, and adaptive laws are designed to update the network weight and estimate the unknown bound of approximation error. The control law does not require prior knowledge of the uncertainty bounds and ensures complete compensation for the uncertainty. The global stability of the closed-loop system and the asymptotical convergence of the attitude tracking errors are proven using a Lyapunov-based approach. Numerical simulations demonstrate the effectiveness and robustness of the proposed controllers, showing improved performance compared to the sliding mode controller.
Article
Automation & Control Systems
Xiaojing Qi, Wenhui Liu, Junwei Lu
Summary: This article studies the observer-based adaptive finite-time prescribed performance control issue for a category of nonlinear systems with external disturbances and input delay. Fuzzy logic systems are used to model the unknown nonlinear terms in the system, and the Pade approximation method is utilized to handle the input delay problem. A fuzzy state observer is established to estimate the unmeasurable states in the system. By using the finite-time Lyapunov control theory and the backstepping technique, a finite-time adaptive fuzzy controller is designed to guarantee the convergence of the tracking error and the boundedness of all signals in the closed-loop system. A simulation example is provided to verify the validity and feasibility of the control method.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jiaming Zhu, Yuequan Yang, Tianping Zhang, Zhiqiang Cao
Summary: In this paper, a self-limiting control term is defined to guarantee the boundedness of variables, and it is applied to a finite-time stability control problem. By adding self-limiting terms to the controller and virtual control laws, the boundedness of the overall system state is guaranteed. Unknown continuous functions are estimated using neural networks. Simulation examples illustrate the theoretical results.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Mathematics, Applied
Zhi-Ye Zhao, Xiao-Zheng Jin, Xiao-Ming Wu, Hai Wang, Jing Chi
Summary: This paper addresses the problem of robust fixed-time trajectory tracking control for a class of nonlinear Euler-Lagrange systems with exogenous disturbances and uncertain dynamics. A neural network-based adaptive estimation algorithm is used to approximate the continuous uncertain dynamics, while an adaptive fixed-time sliding mode control law is proposed to remedy the negative influence of uncertain dynamics and exogenous disturbances. The efficiency of the developed NN-based adaptive fixed-time control strategy is validated through simulation results.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Automation & Control Systems
Stephen Chen, Rafael Vazquez, Miroslav Krstic
Summary: We propose a novel methodology for designing output-feedback backstepping bilateral boundary controllers for an unstable 1D diffusion-reaction partial differential equation (PDE) with spatially varying reaction. By using folding transforms, the parabolic PDE can be converted into a 2 x 2 coupled PDE system with coupling through compatibility conditions. The invertibility of the transformations guarantees the exponential stabilization of the trivial solution of the PDE system by the state-feedback controllers. Additionally, a state observer is designed for two collocated measurements at an arbitrary interior point to generate exponentially stable state estimates.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Automation & Control Systems
Xingxiu He, Maobin Lu
Summary: In this paper, the trajectory tracking control problem of a class of uncertain Euler-Lagrange systems subject to disturbances is investigated. Unlike existing approaches, a class of dynamic output feedback control laws that depend on the tracking error of the position and velocity are proposed instead of assuming the measurability of the reference trajectory's position, velocity, and acceleration. By characterizing the reference trajectory and disturbances with an exosystem, an internal model is designed to learn the desired feedforward input, allowing the tracking of the reference trajectory despite unknown system parameters and disturbances. The effectiveness of this approach is demonstrated through its application to trajectory tracking control of a three-link cylindrical robot arm.
Article
Automation & Control Systems
Arturo Zavala-Rio, Griselda Zamora-Gomez, Tonametl Sanchez, Fernando Reyes-Cortes
Summary: This article develops a generalized proportional-derivative type scheme for the finite-time and exponential tracking continuous control of Euler-Lagrange systems with input constraints. The design allows for the choice among multiple saturating structures and the shaping of error correction terms through control parameters to achieve the desired convergence type. Compared to previous finite-time approaches, the required exponential weights satisfy generalized comparative conditions, allowing for a wider spectrum of finite-time convergent closed-loop trajectories and unconventional exponential convergence.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Engineering, Marine
Dongbo Hou, Wei Cao, Wenjun Wang, Yiguo Li, Cong Wang
Summary: This paper addresses the issue of position tracking control of autonomous underwater gliders in the presence of model uncertainties and external disturbances. An adaptive control system based on filtered backstepping is proposed, utilizing neural networks and sliding mode control to enhance robustness. Lyapunov stability theory is employed to demonstrate boundedness of all signals and convergence of tracking errors to a neighborhood of zero. The presented controller offers practical implementation without requiring knowledge of glider parameters and external environmental disturbances. Simulation results verify the effectiveness and robustness of the proposed control system.
