Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators

A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy navigation to reasoning about obstacle avoidance using APF is described, namely, the intelligent dynamic motion planning. An integration of a robust controller and a modified Elman neural networks (MENNs) approximation-based computed-torque controller is proposed to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The MENN weights are tuned online, with no off-line learning phase required. The stability of the overall closed-loop system, composed by the nonlinear robot dynamics and the robust neuro-fuzzy controller, is guaranteed by the Lyapunov theory. The purpose of the robust 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.     

Robust control of mobile manipulators

A mobile manipulator is basically a manipulator mounted on a mobile vehicle. This arrangement has some advantages over stationary robots and mobile robots, such as the infinite workspace and the ability to avoid singularities. However, the control problem becomes a sophisticated one. This is due to the nonlinear and nonholonomic constraints governing the motion of the vehicle. Moreover, the dynamics of the manipulator and the vehicle are highly coupled; ground-surface irregularities, for example, affect the motion of the end effector kinematically and dynamically. A mobile manipulator is expected to pass through different environmental conditions, a fact which calls for a robust control scheme. Unfortunately, the robust control problem for nonholonomic systems is not well defined yet. Since the ultimate goal of control is to control the motion of the manipulator's end effector, it is proposed in this article to tackle the robustness issue by designing a manipulator decoupling controller. This controller aims at rejecting disturbances arising from the motion of the vehicle. © 1996 John Wiley & Sons, Inc.     

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.     

Robust motion/force control of nonholonomic mobile manipulators using hybrid joints

In this paper, robust force/motion control strategies are presented for mobile manipulators under both holonomic and non-holonomic constraints in the presence of uncertainties and disturbances. The proposed control strategies guarantee that the system motion converges to the desired manifold with prescribed performance and constraint force control is developed using the passivity of hybrid joints rather than force feedback control. Experiment results validate that not only the states of the system asymptotically converge to the desired trajectory, but also the constraint force asymptotically converges to the desired force.     

Robust adaptive control of redundant manipulators

This paper presents an adaptive scheme for the motion control of kinematically redundant manipulators. The proposed controller is very general and computationally efficient since it does not require knowledge of either the mathematical model or the parameter values of the robot dynamics, and is implemented without calculation of the robot inverse dynamics or inverse kinematic transformation. It is shown that the control strategy is globally stable in the presence of bounded disturbances, and that in the absence of disturbances the size of the residual tracking errors can be made arbitrarily small. The performance of the controller is illustrated through computer simulations with a nine degree-of-freedom (DOF) compound manipulator consisting of a relatively small, fast six-DOF manipulator mounted on a large three-DOF positioning device. These simulations demonstrate that the proposed scheme provides accurate and robust trajectory tracking and, moreover, permits the available redundancy to be utilized so that a high bandwidth response can be achieved over a large workspace.     

Robust microprocessor control of robot manipulators

In this paper the problem of robust tracking for robot manipulators in the presence of uncertainty and input constraints is studied. Using the theory of uncertain dynamical systems, robust non-linear control strategies, with guaranteed tracking properties that can be quantified given bounds on the extent of model uncertainty, sensor noise, input disturbances, etc., are derived. A torque optimization strategy is also utilized to optimize the joint torques in the event of actuator saturation. The algorithm has been implemented using a single Motorola 12 MHz MC68000 microprocessor on a three-link revolute joint manipulator constructed by the School of Mechanical Engineering at Cornell University. Experimental results are presented here showing the feasibility and performance of the control stragegy.     

Robust damping control of mobile manipulators

A novel robust control technique, robust damping control (RDC), is introduced. An RDC controller is further developed for the motion control of a mobile manipulator subject to kinematic constraints. The knowledge of dynamic parameters of the mobile manipulator is assumed to be completely unknown. The proposed RDC controller is capable of disturbance-rejection in the presence of unknown bounded disturbance, without requiring the knowledge of its bound. The stability of the closed-loop system is guaranteed. The controller has a simple structure and can be easily implemented in applications. Experimental tests on a 2-DOF robotic manipulator illustrate that the proposed control is significantly better than conventional robust control.     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

Robust neural control for robotic manipulators

A robust neural control scheme for mechanical manipulators is presented. The design basically consists of an adaptive neural controller which implements a feedback linearization control law for a generic manipulator with unknown parameters, and a sliding-mode control which robustifies the design and compensates for the neural approximation errors. It is proved that the resulting closed-loop system is stable and that the trajectory-tracking control objective is achieved. Some simulation results are also provided to evaluate the design.     

Robust adaptive control for mobile manipulators

This paper addresses the trajectory tracking control of a nonholonomic wheeled mobile manipulator with parameter uncertainties and disturbances. The proposed algorithm adopts a robust adaptive control strategy where parametric uncertainties are compensated by adaptive update techniques and the disturbances are suppressed. A kinematic controller is first designed to make the robot follow a desired end-effector and platform trajectories in task space coordinates simultaneously. Then, an adaptive control scheme is proposed, which ensures that the trajectories are accurately tracked even in the presence of external disturbances and uncertainties. The system stability and the convergence of tracking errors to zero are rigorously proven using Lyapunov theory. Simulations results are given to illustrate the effectiveness of the proposed robust adaptive control law in comparison with a sliding mode controller.     

Robust adaptive control for robot manipulators

A composite adaptive control law for robot manipulators in task space, which uses both the tracking error and the prediction error to drive parameter estimation, is developed in this paper. It is shown that global stability and convergence can be achieved for the adaptive control algorithm in the ideal case, and furthermore that the algorithm can be easily modified by using parameter projection to achieve robustness with respect to a class of unmodelled dynamics. In addition, the algorithm has the advantage that no requirement is needed for the inverse of the jacobian matrix or for the bounded inverse of the estimated inertia matrix. A simulation example is provided for performance demonstration.     

非完整移动机械臂的鲁棒跟踪控制

针对非完整移动机械臂惯性参数的不确定性,采用滑模控制为其设计了输出跟踪控制器。首先给出了包括驱动电机动态特性的非完整移动机械臂的简化动态模型,然后通过微分同胚和输入变换将其分解为4个低阶子系统,并给出了其输出跟踪的滑模控制器设计方法。仿真实验表明,所设计的鲁棒控制器能很好地跟踪给定轨迹。     

Robust adaptive decentralized control of robot manipulators

The authors proposes a robust adaptive decentralized control algorithm for trajectory tracking of robot manipulators. The controller is designed based on a Lyapunov method, which consists of a PD (proportional plus derivative) feedback part and a dynamic compensation part. It is shown that, without any prior knowledge of manipulator or payload parameters and possibly under deterioration of parameter variation with time or state-independent input disturbances, the tracking error is bound to converge to zero asymptotically. In particular, the algorithm does not require explicit system parameter estimation and therefore makes the controller structurally simple and computationally easy. Moreover, the controller is implemented in a decentralized manner, i.e. a subcontroller is independently and locally equipped at each joint servoloop. To illustrate the performance of the controller, a numerical simulation example is provided     

Robust fuzzy model-following control of robot manipulators

A robust fuzzy model-following control system is proposed for the control of robot manipulators. The application field to n-link robot manipulators with torque disturbance and measurement noise is addressed. The control objective is obtained by tailoring a nominal adaptation process of parameters to implement appropriate function approximation and facilitating a self-tuning mechanism on the consequent membership functions to overcome the equivalent uncertainty. A novel fuzzy system with self-tuning mechanism provides robust property and the rule-base in the form of “IF situation THEN the control input”. The proposed multilayer fuzzy logic controller can improve both transient and stability margins without a priori knowledge about the dynamic model or parameters of the robotic system. Using the Lyapunov stability method, the uniform ultimate boundedness of tracking error has been proved. The performance is demonstrated by simulating the control of a two-link robot in various situations     

Robust control of planar dual-arm cooperative manipulators

A robust control method is developed for a planar dual-arm manipulator system. Contact and friction constraints for grasp conditions are considered. An optimization algorithm is developed such that a minimization of the energy consumed by the participating arms subject to equality and inequality constraints of grasp and friction constraints. The necessary Karush–Kun–Tucker conditions are implemented to characterize admissible solutions. A robust controller is proposed using a switching-sliding algorithm for modeling imprecision and disturbances. The switching-sliding mode is then replaced by a saturation function that results in the elimination of the fundamental cause for control chatter. The formulation presents a control algorithm that is well suited for dual-arm cooperative manipulators.     

Robust tracking control for rigid robotic manipulators

The problem of robust tracking control using a nominal feedback controller and a variable structure compensator for a rigid robotic manipulator with uncertain dynamics is addressed in this note. It is shown that the effects of large system uncertainties can be eliminated and asymptotic convergence of the output tracking error can be guaranteed by using a variable structure compensator in the closed loop feedback control system for the rigid robotic manipulator     

Adaptive unified motion control of mobile manipulators

This paper presents a unified motion controller for mobile manipulators which not only solves the problems of point stabilization and trajectory tracking but also the path following problem. The control problem is solved based on the kinematic model of the robot. Then, a dynamic compensation is considered based on a dynamic model with inputs being the reference velocities to the mobile platform and the manipulator joints. An adaptive controller for on-line updating the robot dynamics is also proposed. Stability and robustness of the complete control system are proved through the Lyapunov method. The performance of the proposed controller is shown through real experiments.     

Robust fuzzy tracking control for robotic manipulators

In this paper, a stable adaptive fuzzy-based tracking control is developed for robot systems with parameter uncertainties and external disturbance. First, a fuzzy logic system is introduced to approximate the unknown robotic dynamics by using adaptive algorithm. Next, the effect of system uncertainties and external disturbance is removed by employing an integral sliding mode control algorithm. Consequently, a hybrid fuzzy adaptive robust controller is developed such that the resulting closed-loop robot system is stable and the trajectory tracking performance is guaranteed. The proposed controller is appropriate for the robust tracking of robotic systems with system uncertainties. The validity of the control scheme is shown by computer simulation of a two-link robotic manipulator.     

Robust control of robot manipulators with parametric uncertainty

This paper proposes a robust control law for n-link robot manipulators with parametric uncertainty whose upper bound is not assumed to be known. The proposed robust control based on the Corless-Leitmann approach includes a simple estimation law for the upper bound on the parametric uncertainty and an additional control input to be updated as a function of the estimated value. Using the Lyapunov stability theory, the uniform ultimate boundedness of the tracking error is proved     

Robust control of robot manipulators: A survey

This paper presents an overview of robust control schemes for robot manipulators. The survey summarizes the vast literature on the subject. The different modelling assumptions used in current control algorithms are thoroughly reviewed. The survey includes models of actuator dynamics and joint flexibility. The different control schemes are organized in the following six categories: linear schemes, passivity-based schemes, Lyapunov-based schemes, sliding mode control schemes, non-linear H omega schemes and robust adaptive control schemes. Connections and comparisons are made between the various algorithms.