Control of a nonholonomic mobile robot using neural networks

A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping and stability is guaranteed by Lyapunov theory. This control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. Moreover, the NN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. Online NN weight tuning algorithms do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.     

Robust Practical Point Stabilization of a Nonholonomic Mobile Robot Using Neural Networks

A control structure that makes possible the integration of a kinematiccontroller and a neural network (NN) computed-torque controller fornonholonomic mobile robots is presented. A combined kinematic/torque controllaw is developed and stability is guaranteed by Lyapunov theory. Thiscontrol algorithm is applied to the practical point stabilization problemi.e., stabilization to a small neighborhood of the origin. The NN controllercan deal with unmodeled bounded disturbances and/or unstructured unmodeleddynamics in the vehicle. On-line NN weight tuning algorithms that do notrequire off-line learning yet guarantee small tracking errors and boundedcontrol signals are utilized.     

Neural-network control of mobile manipulators

In this paper, a neural network (NN)-based methodology is developed for the motion control of mobile manipulators subject to kinematic constraints. The dynamics of the mobile manipulator is assumed to be completely unknown, and is identified online by the NN estimators. No preliminary learning stage of NN weights is required. The controller is capable of disturbance-rejection in the presence of unmodeled bounded disturbances. The tracking stability of the closed-loop system, the convergence of the NN weight-updating process and boundedness of NN weight estimation errors are all guaranteed. Experimental tests on a 4-DOF manipulator arm illustrate that the proposed controller significantly improves the performance in comparison with conventional robust control.     

Adaptive Neuro-fuzzy Network Control for a Mobile Robot

A control structure that makes possible the integration of a kinematic controller and a neuro-fuzzy network (NFN) dynamic controller for mobile robots is presented. A combined kinematic/dynamic control law is developed using backstepping and stability is guaranteed by Lyapunov theory. The NFN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamic in the mobile robot. On-line NFN parameter tuning algorithms do no require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.     

Asymptotic Adaptive Neural Network Tracking Control of Nonholonomic Mobile Robot Formations

In this paper, asymptotically stable control laws are developed for leader–follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation. First, a kinematic controller is developed around control strategies for single mobile robots and the idea of virtual leaders. The virtual leader is replaced with a physical mobile robot leader, and an auxiliary velocity control law is developed in order to prove the global asymptotic stability of the followers which in turn allows the local asymptotic stability of the entire formation. A novel approach is taken in the development of the dynamical controller such that the torque control inputs for the follower robots include the dynamics of the follower robot as well as the dynamics of its leader, and two cases are considered—the case when the robot dynamics are known and the case when they are unknown. In the first case, a robust adaptive control term is utilized to account for unmodeled dynamics. For the latter, a robust adaptive term is augmented with a NN control law to achieve asymptotic tracking performance in contrast with most NN controllers where a bounded tracking error result is shown. Additionally, the NN approximation error is assumed to be a function of tracking errors instead of a constant upper bound, which is commonly found in the literature. The stability of the follower robots as well as the entire formation is demonstrated in each case using Lyapunov methods and numerical results are provided.     

Motion Control of a Nonholonomic Mobile Manipulator in Task Space

In this paper, the motion control of a mobile manipulator subjected to nonholonomic constraints is investigated. The control objective is to design a computed‐torque controller based on the coupled dynamics of the mobile manipulator. The proposed controller achieves the capability of simultaneous tracking of a reference velocity for the mobile base and a reference trajectory for the end‐effector. The aforementioned reference velocity and trajectory are defined in the task space, such task setting imitates the actual working conditions of a mobile manipulator and thus makes the control problem practical. To solve this tracking problem, a steering velocity is firstly designed based on the first‐order kinematic model of the nonholonomic mobile base via dynamic feedback linearization. The main merit of the proposed steering velocity design is that it directly utilizes the reference velocity set in the task space without requiring the knowledge of a reference orientation. A torque controller is subsequently developed based on a proposed Lyapunov function which explicitly considers the coupled dynamics of the mobile manipulator to ensure the mobile base and end‐effector track the reference velocity and trajectory respectively. This proposed computed‐torque controller is able to realize asymptotic stability of both the base velocity tracking error and the end‐effector motion tracking error. Simulations are conducted to demonstrate the effectiveness of the proposed controller.     

Hybrid trigonometric compound function neural networks for tracking control of a nonholonomic mobile robot

The purpose of this paper is to propose a hybrid trigonometric compound function neural network (NN) to improve the NN-based tracking control performance of a nonholonomic mobile robot with nonlinear disturbances. In the mobile robot control system, two NN controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the tracking control of the mobile robot. The neuron functions of the hidden layer in the three-layer feedforward network structure consist of the compound cosine function and the compound sine function combining a cosine or a sine function with a unipolar sigmoid function. The main advantages of this NN-based mobile robot control system are better real-time control capability and control accuracy by use of the proposed NN controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances of dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of a nonholonomic mobile robot has better real-time control capability and control accuracy than the compound cosine function NN control system of a nonholonomic mobile robot and then verify the effectiveness of the proposed hybrid trigonometric compound function NN controller for improving the tracking control performance of a nonholonomic mobile robot with nonlinear disturbances.     

带有未知参数和有界干扰的移动机器人轨迹跟踪控

针对模型参数未知和存在有界干扰的非完整移动机器人的轨迹跟踪控制问题,本文提出了一种鲁棒自适应轨迹跟踪控制器方法.非完整移动机器人的控制难点在于它的运动学系统是欠驱动的.针对这一难点,本文利用横截函数的思想,引入新的辅助控制器,使得非完整移动机器人系统不再是一个欠驱动系统,缩减了控制器设计的难度,进而利用非线性自适应算法和参数映射方法构造李雅谱诺夫函数.通过李雅普诺夫方法设计控制器和参数自适应器,从而使得非完整移动机器人的跟随误差任意小,即可以任意小的误差来跟随任意给定的参考轨迹.仿真结果证明了方法的有效性.     

非完整移动机器人的反步跟踪控制方法

移动机器人是典型的非完整系统,对其进行运动控制是一个热点且是一项具有挑战性的工作.目前的大部分研究都是基于运动学模型的,而在实际应用中,动力学特性不可忽视.针对轨迹跟踪这一典型控制任务,文中对一种轮式移动机器人进行了动力学建模.利用反步技术,提出了一种控制结构,综合了速度控制器和力矩控制器.该控制律不但能够得到稳定跟踪所需要的速度,而且同时能够计算出驱动移动机器人行进的电机力矩.仿真结果表明,该控制律可以良好地工作.控制结构的通用性保证了其他控制方法拓展的可能性.     

非完整移动机械手鲁棒自适应模糊控制

考虑系统存在的参数、外界扰动和未建模动态等不确定性,研究非完整移动机械手的鲁棒自适应控制器设计方法.基于用旋量理论建立的非完整移动机械手的动力学模型,设计了移动平台子系统的运动控制器,然后应用非线性反步控制技术和模糊逻辑系统的通用逼近性,用参数化线性模糊逻辑系统逼近非完整移动机械手动力学模型中的不确定项,基于Lyapu...     

Robust tracking control of nonholonomic dynamic systems with application to the bi-steerable mobile robot

A tracking controller for nonholonomic dynamic systems is proposed which allows global tracking of arbitrary reference trajectories and renders the closed loop system robust with respect to bounded disturbances. The controller is based on [Chwa, D. (2004). Sliding-mode tracking control of nonholonomic wheeled mobile robots in polar coordinates. IEEE Transactions on Control Systems Technology, 12(4), 637-644] and shows several generalizations and improvements. The control law for tracking of general nonholonomic systems using inverse kinematic models (IKM) and sliding surfaces is stated. Conditions are proven under which robust tracking is achieved for a specific system. Tracking control is applied to the bi-steerable mobile robot, and simulation results are presented.     

Design and Implementation of an Inverse Dynamics Controller for Uncertain Nonholonomic Robotic Systems

This paper addresses the trajectory tracking control problem of nonholonomic robotic systems in the presence of modeling uncertainties. A tracking controller is proposed such that it combines the inverse dynamics control technique and an adaptive robust PID control strategy to preserve robustness to both parametric and nonparametric uncertainties. A SPR-Lypunov stability analysis demonstrates that tracking errors are uniformly ultimately bounded (UUB) and exponentially converge to a small ball containing the origin. The proposed inverse dynamics tracking controller is successfully applied to a nonholonomic wheeled mobile robot (WMR) and experimental results are presented to validate the effectiveness of the proposed controller.     

Cooperative adaptive consensus tracking for multiple nonholonomic mobile robots

This paper addresses the cooperative adaptive consensus tracking for a group of multiple nonholonomic mobile robots, where the nonholonomic robot model is assumed to be a canonical vehicle having two actuated wheels and one passive wheel. By integrating a kinematic controller and a torque controller for the nonholonomic robotic system, a cooperative adaptive consensus tracking strategy is developed for the uncertain dynamic models using Lyapunov-like analysis in combination with backstepping approach and sliding mode technique. A key feature of the developed adaptive consensus tracking algorithm is the introduction of a directed network topology into the control constraints based on algebraic graph theory to characterise the communication interaction among robots, which plays an important role in realising the cooperative consensus tracking with respect to a specific common reference trajectory. Furthermore, a novel framework is proposed for developing a unified methodology for the convergence analysis of the closed-loop control systems, which can fully ensure the desired adaptive consensus tracking for multiple nonholonomic mobile robots. Subsequently, illustrative examples and numerical simulations are provided to demonstrate and visualise the theoretical results.     

An Intelligent Robust Tracking Control for a Class of Electrically Driven Mobile Robots

This paper addresses the problem of designing robust tracking control for a class of uncertain wheeled mobile robots actuated by brushed direct current motors. This class of electrically‐driven mechanical systems consists of the robot kinematics, the robot dynamics, and the wheel actuator dynamics. Via the backstepping technique, an intelligent robust tracking control scheme that integrates a kinematic controller and an adaptive neural network‐based (or fuzzy‐based) controller is developed such that all of the states and signals of the closed‐loop system are bounded and the tracking error can be made as small as possible. Two adaptive approximation systems are constructed to learn the behaviors of unknown mechanical and electrical dynamics. The effects of both the approximation errors and the unmodeled time‐varying perturbations in the input and virtual‐input weighting matrices are counteracted by suitably tuning the control gains. Consequently, the robust control scheme developed here can be employed to handle a broader class of electrically‐driven wheeled mobile robots in the presence of high‐degree time‐varying uncertainties. Finally, a simulation example is given to demonstrate the effectiveness of the developed control scheme.     

Robust adaptive neural network control for environmental boundary tracking by mobile robots

The paper addresses the problem of environmental boundary tracking for the nonholonomic mobile robot with uncertain dynamics and external disturbances. To do environmental boundary tracking, a reference velocity is designed for the nonholonomic mobile robot. In this paper, a radial basis function neural network (NN) is used to approximate a nonlinear function containing the uncertain model terms and the elements of the Hessian matrix of the environmental concentration function. Then, the NN approximator is combined with a robust control to construct a robust adaptive NN control for the mobile robot to track the desired environment boundary. It is proved that the tracking error can be guaranteed to converge to zero in the ultimate. Simulation results are presented to illustrate the stability of the robust adaptive control. Copyright © 2011 John Wiley & Sons, Ltd.     

Neural adaptive PID formation control of car-like mobile robots without velocity measurements

A virtual leader–follower formation control of a group of car-like mobile robots is addressed in this paper. First, the kinematic and dynamic models of car-like robots are transformed into a second-order leader–follower formation model which inherits all structural properties of the robot dynamic model. Then, a new observer-based proportional–integral-derivative formation controller is proposed to force that all robots construct a desired formation with respect to a predefined virtual leader. To improve the formation tracking and observation performance, the integral action is incorporated into the design of the observer–controller scheme. Adaptive robust and neural network techniques are also employed to compensate uncertain parameters, unmodeled dynamics, and external disturbances. Lyapunov’s direct method is utilized to show that the formation tracking and observation errors are semi-globally uniformly ultimately bounded. Then, the proposed controller is extended to the leader–follower formation of a team of tractor–trailer systems. Finally, simulation results illustrate the efficiency of the proposed controller.     

Robust adaptive control of proper systems

To date, all the adaptive control algorithms have been proposed only for strictly proper systems. In this paper, a scheme is proposed to design an adaptive controller for proper systems. To study the robustness of the adaptive controller, both additive and multiplicative types of unmodeled dynamics are considered and can also be allowed to be proper, or even improper. Global bounded input bounded output stability is established. The achievement of a small in the mean tracking error and perfect tracking/rejection of deterministic trajectories/disturbances in the absence of system unmodeled dynamics are discussed. The results are also verified by simulation studies     

含未标定摄像机参数的非完整移动机器人的自适应动力学跟踪控制

结合一类非完整移动机器人的运动学模型和链式转换,在质心与几何中心重合的情况下,研究含有未知参量的非完整移动机器人的跟踪控制问题.首先,利用针孔摄像机模型提出一种基于视觉伺服的运动学跟踪误差模型;然后在此模型下,将动态反馈、Back-stepping技巧与自适应控制相结合,设计一个区别于以往处理方法、含有两个动态反馈的自适应跟踪控制器,从而实现动力学系统的全局渐近轨迹跟踪,并通过李亚普诺夫方法严格证明闭环系统的稳定性和估计参数的有界性;最后,利用Matlab仿真验证所提出的控制器的有效性.     

Robust adaptive control of uncertain force/motion constrained nonholonomic mobile manipulators

In this paper, force/motion tracking control is investigated for nonholonomic mobile manipulators with unknown parameters and disturbances under uncertain holonomic constraints. The nonholonomic mobile manipulator is transformed into a reduced chained form, and then, robust adaptive force/motion control with hybrid variable signals is proposed to compensate for parametric uncertainties and suppress bounded disturbances. The control scheme guarantees that the outputs of the dynamic system track some bounded auxiliary signals, which subsequently drive the kinematic system to the desired trajectory/force. Simulation studies on the control of a wheeled mobile manipulator are used to show the effectiveness of the proposed scheme.     

Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators

An integration of fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The fuzzy controller is based on artificial potential fields using analytic harmonic functions, a navigation technique common used in robot control. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.