Finite-Time-Disturbance-Observer-Based Tracking Control of DC Motors With Velocity Constraint

2015 ◽  
Author(s):  
Z. L. Dong ◽  
J. Y. Yao ◽  
Z. B. Xu ◽  
D. W. Ma
Keyword(s):  
Lyapunov Function ◽  
Finite Time ◽  
Disturbance Observer ◽  
Design Procedure ◽  
Tracking Performance ◽  
Backstepping Design ◽  
Barrier Lyapunov Function ◽  
Dc Motors ◽  
Asymptotic Tracking ◽  
Velocity Constraint

The integration design of high tracking performance and velocity constraint for dc motors is present in this paper. When designing advanced controllers for dc motors, it would be best to take the constraint of output velocity into consideration due to performance and/or physical limitations. A barrier Lyapunov function, which grows to infinity when its arguments approach some pre-set limits, is utilized to prevent constraint violation. By ensuring the stabilization of the barrier Lyapunov function, and imposing a hard-bound on associated error signals through the steps of the backstepping design procedure, the system output velocity constraint is guaranteed to be not transgressed. However, potential disturbances, including unmodelled dynamic effects, parametric uncertainties and external disturbances may destroy above theoretical results and degrade the tracking performance. In this paper, a finite time disturbance observer is employed and integrated with the barrier Lyapunov function based backstepping design to achieve the asymptotic tracking without the violation of the velocity constraint meanwhile overcoming the disturbances. Extensive simulation results are provided to illustrate the performance of the proposed control strategy.

Barrier Lyapunov function-based robot control with an augmented neural network approximator

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zuguo Zhang ◽  
Qingcong Wu ◽  
Xiong Li ◽  
Conghui Liang
Keyword(s):  
Neural Network ◽  
Lyapunov Function ◽  
Control Algorithm ◽  
Tracking Performance ◽  
Content Type ◽  
Barrier Lyapunov Function ◽  
Output Error ◽  
Control Scheme ◽  
Stringent Constraint ◽  
Continuous Dynamic

Purpose Considering the complexity of dynamic and friction modeling, this paper aims to develop an adaptive trajectory tracking control scheme for robot manipulators in a universal unmodeled method, avoiding complicated modeling processes. Design/methodology/approach An augmented neural network (NN) constituted of radial basis function neural networks (RBFNNs) and additional sigmoid-jump activation function (SJF) neurons is introduced to approximate complicated dynamics of the system: the RBFNNs estimate the continuous dynamic term and SJF neurons handle the discontinuous friction torques. Moreover, the control algorithm is designed based on Barrier Lyapunov Function (BLF) to constrain output error. Findings Lyapunov stability analysis demonstrates the exponential stability of the closed-loop system and guarantees the tracking errors within predefined boundaries. The introduction of SJFs alleviates the limitation of RBFNNs on discontinuous function approximation. Owing to the fast learning speed of RBFNNs and jump response of SJFs, this modified NN approximator can reconstruct the system model accurately at a low compute cost, and thereby better tracking performance can be obtained. Experiments conducted on a manipulator verify the improvement and superiority of the proposed scheme in tracking performance and uncertainty compensation compared to a standard NN control scheme. Originality/value An enhanced NN approximator constituted of RBFNN and additional SJF neurons is presented which can compensate the continuous dynamic and discontinuous friction simultaneously. This control algorithm has potential usages in high-performance robots with unknown dynamic and variable friction. Furthermore, it is the first time to combine the augmented NN approximator with BLF. After more exact model compensation, a smaller tracking error is realized and a more stringent constraint of output error can be implemented. The proposed control scheme is applicable to some constraint occasion like an exoskeleton and surgical robot.

scholarly journals Adaptive Control of a Class of Switched Nonlinear System with Partial State Constraints Using a Barrier Lyapunov Function

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Enchang Cui ◽  
Yuanwei Jing ◽  
Xiaoting Gao
Keyword(s):  
Lyapunov Function ◽  
Adaptive Controller ◽  
State Constraint ◽  
Adaptive Tracking Control ◽  
Barrier Lyapunov Function ◽  
Arbitrary Switching ◽  
Switched Nonlinear System ◽  
Asymptotic Tracking ◽  
Partial State ◽  
And Control

This paper discusses partial state constraint adaptive tracking control problem of switched nonlinear systems with uncertain parameters. In order to ensure boundedness of the outputs and prevent the states from violating the constraints, a barrier Lyapunov function (BLF) is employed. Based on backstepping method, an adaptive controller for the switched system is designed. Furthermore, the state-constrained asymptotic tracking under arbitrary switching is performed. The closed-loop signals keep bounded when the initial states and control parameters are given. Finally, examples and simulation results are reported to illustrate the effectiveness of the proposed controller.

Tracking Control for Switched Nonlinear Systems Subject to Time-Varying Output Constraints

2012 ◽  
Author(s):  
Ben Niu ◽  
Georgi M. Dimirovski ◽  
Jun Zhao
Keyword(s):  
Nonlinear Systems ◽  
Lyapunov Function ◽  
Tracking Control ◽  
Closed Loop ◽  
Time Varying ◽  
Switched Nonlinear Systems ◽  
Feedback Form ◽  
Barrier Lyapunov Function ◽  
Output Constraints ◽  
Asymptotic Tracking

In this paper, we address the tracking control problem for switched nonlinear systems in strict-feedback form with time-varying output constraints. To prevent the output from violating the time-varying constraints, we employ a Barrier Lyapunov Function, which relies explicitly on time. Based on the simultaneous domination assumption, we design a controller for the switched system, which guarantees that asymptotic tracking is achieved without transgression of the constraints and all closed-loop signals remain bounded under arbitrary switchings. The effectiveness of the proposed results is illustrated using a numerical example.

scholarly journals Robust Prescribed Performance Control of Uncertain Vehicle Lateral System under State Constraints

2021 ◽  
Vol 2083 (3) ◽  
pp. 032029
Author(s):  
Jing Yu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Lyapunov Function ◽  
Finite Time ◽  
Tracking Control ◽  
State Constraints ◽  
Tracking Error ◽  
Lateral Control ◽  
Barrier Lyapunov Function ◽  
The Neural Network

Abstract In the study of the zero-error tracking control problem for vehicle lateral control systems under full-state constraints and nonparametric uncertainties, the zero-error tracking control problem is presented in this paper. A neural adaptive tracking control scheme is proposed by combining the error transformation of the vehicle lateral control system with the barrier Lyapunov function, which realizes that the tracking error of the vehicle lateral control converges to a prescribed compact set at a controllable or specified convergence rate in a specified finite time. The scheme has the following significant characteristics: 1) Based on the Nussbaum gain, the preset new energy finite-time control algorithm, the tracking error of the vehicle lateral control system with non-parametric uncertainty and external disturbance decreases to zero with t → ∞. In addition, it also has the control ability to cope with the presence or even unknown moment of inertia of the system. 2) Barrier Lyapunov function (BLF) ensures the bounded input of the neural network during the whole system envelope, and ensures the stable learning and approximation of the neural network. Furthermore, the bounded stability of the closed-loop system is proved by Lyapunov analysis. Finally, the effectiveness and superiority of the proposed control method are verified by simulation.

scholarly journals A Novel Continuous Nonsingular Finite–Time Control for Underwater Robot Manipulators

2021 ◽  
Vol 9 (3) ◽  
pp. 269
Author(s):  
Zengcheng Zhou ◽  
Guoyuan Tang ◽  
Ruikun Xu ◽  
Lijun Han ◽  
Maolin Cheng
Keyword(s):  
Finite Time ◽  
Disturbance Observer ◽  
Control Method ◽  
Sliding Mode ◽  
Robot Manipulators ◽  
Lyapunov Theory ◽  
Tracking Performance ◽  
Ocean Current ◽  
Underwater Robot ◽  
Parameter Uncertainties

In this paper, the tracking control problem of underwater robot manipulators is investigated under the influence of the lumped disturbances, including unknown ocean current disturbances and parameter uncertainties. The proposed novel continuous nonsingular finite–time (CNFT) control method is twofold. Firstly, the modified adaptive super–twisting algorithm (ASTA) is proposed with a nonsingular fast terminal sliding mode (NFTSM) manifold to guarantee the finite–time convergence both in the sliding mode phase and the reaching phase. Secondly, a higher–order super–twisting disturbance observer (HOSTDO) is exploited to attenuate the effects of the lumped disturbances. Considering the time–varying gain matrix of the closed–loop control system, the bounded stability is strictly proved via the Lyapunov theory. Hence, the superiority of the proposed controller is singularity–free, fast convergence, chattering–free, high steady–state tracking performance, and good robustness by resorting to the methods of CNFT control and ASTA in combination with a disturbance observer. Finally, numerical simulations are conducted on a two degree–of–freedom (DOF) underwater robot manipulator to demonstrate the effectiveness and high tracking performance of the designed controller.

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