Robust control of robot manipulators with fast load adaptation

Many adaptive robot controllers have been proposed in the literature to solve manipulator trajectory tracking problems for high-speed operations in the presence of parameter uncertainties. However, most of these controllers stem from the applications of the existing adaptive control theory, which is traditionally focused on tracking slowly time-varying parameters. In fact, manipulator dynamics have fast transient processes for high-speed operations and load changes are abrupt. These observations motivate the present research to incorporate change detection techniques into self-tuning schemes for tracking abrupt load variations and achieving fast load adaptation. To this end, a robustly global stabilizing controller for a robot model with parametric and non-parametric uncertainies is developed based on the Lyapunov second method, and it is then made adaptive via the self-tuning regulator concept. The two-model approach to online change detection in load is used and the estimation algorithm is reinitialized once load changes are detected. This allows a much faster adaptive identification of load parameters than the ordinary forgetting factor approach. Simulation results demonstrate that the proposed controller achieves better tracking accuracy than the existing adaptive and non-adaptive controllers.     

PD manipulator controller with fuzzy adaptive gravity compensation

This paper presents a PD manipulator controller with fuzzy adaptive gravity compensation. The main idea is to use a fuzzy adaptive controller to compensate for the gravity term of the robotic manipulator. This controller is designed by using Lyapunov's stability theorem, which guarantees system stability. Simulation is implemented on a two‐link manipulator by using MATALAB and SIMULINK. The results show that this fuzzy adaptive controller makes the manipulator trajectory converge to a desired position. Compared with other proposed fuzzy adaptive manipulator controllers, the PD manipulator controller with fuzzy adaptive gravity compensation is conceptually and structurally simpler and guarantees zero position error. ©2000 John Wiley & Sons, Inc.     

Robust nonlinear control of a planar 2-DOF parallel manipulator with redundant actuation

A new robust nonlinear controller is presented and applied to a planar 2-DOF parallel manipulator with redundant actuation. The robust nonlinear controller is designed by combining the nonlinear PD (NPD) control with the robust dynamics compensation. The NPD control is used to eliminate the trajectory disturbances, unmodeled dynamics and nonlinear friction, and the robust control is used to restrain the model uncertainties of the parallel manipulator. The proposed controller is proven to guarantee the uniform ultimate boundedness of the closed-loop system by the Lyapunov theory. The trajectory tracking experiment with the robust nonlinear controller is implemented on an actual planar 2-DOF parallel manipulator with redundant actuation. The experimental results are compared with the augmented PD (APD) controller, and the proposed controller shows much better trajectory tracking accuracy.     

Adaptive tracking control of manipulators: Theory and experiments

This paper considers the trajectory tracking problem for uncertain robot manipulators and proposes two adaptive controllers as solutions to this problem. The first controller is derived under the assumption that the manipulator state is measurable, while the second strategy is developed for those applications in which only position measurements are available. The adaptive schemes are very general and computationally efficient since they do not require knowledge of either the mathematical model or the parameter values of the manipulator dynamics, and are implemented without calculation of the robot inverse dynamics or inverse kinematic transformation. It is shown that the control strategies ensure uniform boundedness of all signals in the presence of bounded disturbances, and that the ultimate size of the tracking errors can be made arbitrarily small. Experimental results are presented for a PUMA 560 manipulator and demonstrate that accurate and robust trajectory tracking can be achieved by using the proposed controllers.     

Simulation of a SCARA robot with PD and learning controllers

In this study, a SCARA robot manipulator is simulated under PD and learning based controllers. The trajectory following performance of the robot is studied against these controllers. The adaptive/learning hybrid controller scheme and learning controller method are utilized as learning based controllers. The results of simulations show that, learning algorithm based controllers reduce the position tracking error effectively. The hybrid adaptive/learning controller has similar performance as the learning controller although it uses partial state information and compensates both mechanical and electrical dynamics, whereas the learning controller needs both position and velocity measurements neglecting electrical dynamics.     

Compound control of a compound cosine function neural network and PD for manipulators

In this paper, a compound cosine function neural network controller for manipulators is presented based on the combination of a cosine function and a unipolar sigmoid function. The compound control scheme based on a proportional-differential (PD) feedback control plus the cosine function neural network feedforward control is used for the tracking control of manipulators. The advantages of the compound control are that the system model does not need to be identified beforehand in the manipulator control system and it can achieve better adaptive control in an on-line continuous learning manner. The simulation results for the two-link manipulator show that the proposed compound control has higher tracking accuracy and better robustness than the conventional PD controllers in the position trajectory tracking control for the manipulator. Therefore, the compound cosine function neural network controller provides a novel approach for the manipulator control with uncertain nonlinear problems.     

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.     

Robot learning control based on recurrent neural network inverse model

A non-linear model-based feedforward, feedback, and learning controller is presented. This controller can control a non-linear plant such as a robot whose dynamics are initially unknown. In the feedforward part, a recurrent neural network (RNN) is used to model the inverse dynamics of the plant. In the feedback part, a PD controller is added to handle unmodeled dynamics and disturbances. Furthermore, an add-on learning controller is established to reduce tracking errors for repetitive tasks. The controller is validated with the control of a simulated two-joint manipulator. Simulation results show that the controller can successfully learn the inverse dynamics of a robot, perform accurate tracking for a general trajectory, and improve its own performance over the repetitions of a trajectory, with and without a payload change. © 1997 John Wiley & Sons, Inc.     

Augmented Nonlinear PD Controller for a Redundantly Actuated Parallel Manipulator

In order to improve trajectory tracking accuracy for a redundantly actuated parallel manipulator, a so-called augmented nonlinear PD (ANPD) controller based on the conventional dynamic controller is proposed in this paper. The ANPD controller is designed by replacing the linear PD in the augmented PD controller with a nonlinear PD algorithm. The stability of the parallel manipulator system with the proposed ANPD controller is proven using the Lyapunov stability theorem, and the ANPD controller is further proven to guarantee asymptotic convergence to zero of both the tracking error and error rate. The superiority of the ANPD controller is verified through trajectory tracking experiments of an actual 2-d.o.f. redundantly actuated parallel manipulator.     

Decentralized adaptive control of manipulators

This article presents two new adaptive schemes for motion control of robot manipulators. The first controller possesses a partially decentralized structure in which the control input for each task variable is computed based on information concerning only that variable and on two “scaling factors” that depend on the other task variables. The need for these scaling factors is eliminated in the second controller by exploiting the underlying topology of the robot configuration space, and this refinement permits the development of a completely decentralized adaptive control strategy. The proposed controllers are computationally efficient, do not require knowledge of either the mathematical model or the parameter values of the robot dynamics, and are shown to be globally stable in the presence of bounded disturbances. Furthermore, the control strategies are general and can be implemented for either position regulation or trajectory tracking in joint-space or task-space. Computer simulation results are given for a PUMA 762 manipulator, and these demonstrate that accurate and robust trajectory tracking is achievable using the proposed controllers. Experimental results are presented for a PUMA 560 manipulator and confirm that the proposed schemes provide simple and effective real-time controllers for accomplishing high-performance trajectory tracking. © 1994 John Wiley & Sons, Inc.     

Sliding Mode Adaptive Neural-Network Control for Nonholonomic Mobile Modular Manipulators

A general mobile modular manipulator can be defined as a m-wheeled holonomic/nonholonomic mobile platform combining with a n-degree of freedom modular manipulator. This paper presents a sliding mode adaptive neural-network controller for trajectory following of nonholonomic mobile modular manipulators in task space. Dynamic model for the entire mobile modular manipulator is established in consideration of nonholonomic constraints and the interactive motions between the mobile platform and the onboard modular manipulator. Multilayered perceptrons (MLP) are used as estimators to approximate the dynamic model of the mobile modular manipulator. Sliding mode control and direct adaptive technique are combined together to suppress bounded disturbances and modeling errors caused by parameter uncertainties. Simulations are performed to demonstrate that the dynamic modeling method is valid and the controller design algorithm is effective.     

Trajectory Tracking for Underwater Swimming Manipulators using a Super Twisting Algorithm

The underwater swimming manipulator (USM) is a snake‐like, multi‐articulated, underwater robot that is equipped with thrusters. One of the main purposes of the USM is to act like an underwater floating base manipulator. As such, it is essential to achieve good station‐keeping and trajectory tracking performance for the USM by using the thrusters and by using the joints to attain the desired position and orientation of the head and tail of the USM. In this ‘paper, we propose a sliding mode control (SMC) law, specifically the super‐twisting algorithm with adaptive gains, for the trajectory tracking of the USM's centre of mass. A higher‐order sliding mode observer is proposed for state estimation. Furthermore, we show the ultimate boundedness of the tracking errors. We demonstrate the applicability of the proposed control law and show that it leads to better performance than a linear PD‐controller.     

Trajectory Tracking Control of Omnidirectional Wheeled Mobile Manipulators: Robust Neural Network-Based Sliding Mode Approach

This paper addresses the robust trajectory tracking problem for a redundantly actuated omnidirectional mobile manipulator in the presence of uncertainties and disturbances. The development of control algorithms is based on sliding mode control (SMC) technique. First, a dynamic model is derived based on the practical omnidirectional mobile manipulator system. Then, a SMC scheme, based on the fixed large upper boundedness of the system dynamics (FLUBSMC), is designed to ensure trajectory tracking of the closed-loop system. However, the FLUBSMC scheme has inherent deficiency, which needs computing the upper boundedness of the system dynamics, and may cause high noise amplification and high control cost, particularly for the complex dynamics of the omnidirectional mobile manipulator system. Therefore, a robust neural network (NN)-based sliding mode controller (NNSMC), which uses an NN to identify the unstructured system dynamics directly, is further proposed to overcome the disadvantages of FLUBSMC and reduce the online computing burden of conventional NN adaptive controllers. Using learning ability of NN, NNSMC can coordinately control the omnidirectional mobile platform and the mounted manipulator with different dynamics effectively. The stability of the closed-loop system, the convergence of the NN weight-updating process, and the boundedness of the NN weight estimation errors are all strictly guaranteed. Then, in order to accelerate the NN learning efficiency, a partitioned NN structure is applied. Finally, simulation examples are given to demonstrate the proposed NNSMC approach can guarantee the whole system's convergence to the desired manifold with prescribed performance.     

Adaptive stabilization and tracking control of electrically driven manipulators

This article presents two new adaptive strategies for motion control of uncertain rigid-link, electrically driven manipulators. The first controller is a position regulation scheme that ensures (semiglobal) asymptotic convergence of the position error if no external disturbances are present, and convergence to an arbitrarily small neighborhood of zero in the presence of bounded disturbances. It is shown that the regulation scheme can be modified to provide accurate trajectory tracking control through the introduction of adaptive feedforward elements in the control law; this second control strategy retains the simple structure of the first controller and ensures arbitrarily accurate tracking in the presence of bounded disturbances. Each of the adaptive schemes is computationally efficient and requires virtually no information concerning either the manipulator or actuator models. Computer simulation results are given for a PUMA 560 manipulator and demonstrate that accurate and robust motion control can be achieved by using the proposed controllers. Experimental results are presented for an IMI Zebra Zero manipulator and confirm that the proposed approach provides a simple and effective means of obtaining high performance motion control. © 1996 John Wiley & Sons, Inc.     

一类二阶非线性系统的准滑模无模型自适应控制

为实现一类二阶非线性系统的轨迹跟踪控制,设计一种准滑模无模型自适应控制算法(quasi-sliding mode-model free adaptive control,SM-MFAC).首先,将模型分解为串联的两个离散子系统,并给出整体系统伪偏导数(pseudo partial derivative,PPD)的表达式;然后,利用两个子系统的输出数据设计出子系统2输出的期望状态,进而通过MFAC(model free adaptive control)对在线更新的期望状态进行不断追踪来实现对整体目标的跟踪控制;接着,对SM-MFAC控制系统进行稳定性分析,证明系统输出误差渐进跟踪到零的某个邻域内和等效控制输入是有界的;最后,以自由漂浮空间机械臂的关节轨迹跟踪控制为例验证SM-MAFC控制理论,在多体运动学与动力学仿真软件MBdyn中搭建一个平面两连杆的自由漂浮空间机械臂,关节系统中存在死区、输入饱和以及摩擦特性,通过在Matlab-simulink中的联合仿真表明:MFAC控制方案无法准确地估计出该类二阶非线性系统整体PPD的数值,导致控制性能降低,而所设计的SM-MFAC控制器相对于PID(proportional-integral-differential)、基于比例-微 分(proportional-differential,PD)的MFAC可以更快更准确地追踪目标曲线.     

Constrained motion control of flexible robot manipulators based on recurrent neural networks

In this paper, a neural network approach is presented for the motion control of constrained flexible manipulators, where both the contact force everted by the flexible manipulator and the position of the end-effector contacting with a surface are controlled. The dynamic equations for vibration of flexible link and constrained force are derived. The developed control, scheme can adaptively estimate the underlying dynamics of the manipulator using recurrent neural networks (RNNs). Based on the error dynamics of a feedback controller, a learning rule for updating the connection weights of the adaptive RNN model is obtained. Local stability properties of the control system are discussed. Simulation results are elaborated on for both position and force trajectory tracking tasks in the presence of varying parameters and unknown dynamics, which show that the designed controller performs remarkably well.     

Perfomance-based adaptive tracking control of robot manipulators

This article presents two new adaptive schemes for the motion control of robot manipulators. The proposed controllers are very general and computationally efficient because they do not require knowledge of either the mathematical model or the parameter values of the manipulator dynamics, and are implemented without calculation of the robot inverse dynamics or inverse kinematic transformation. It is shown that the control strategies are globally stable in the presence of bounded disturbances, and that in the absence of disturbances the ultimate bound on the size of the tracking errors can be made arbitrarily small. Computer simulation results are given for a PUMA 560 manipulator, and demonstrate that accurate and robust trajectory tracking can be achieved by using the proposed controllers. Experimental results are presented for an IMI Zebra Zero manipulator and confirm that the control schemes provide a simple and effective means of obtaining high-performance trajectory tracking. © 1995 John Wiley & Sons, Inc.     

Observer-Based Adaptive Controller Design of Flexible Manipulators Using Time-Delay Neuro-Fuzzy Networks

In this paper, a dynamical time-delay neuro-fuzzy controller is proposed for the adaptive control of a flexible manipulator. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity. For a perfect tracking control of the robot, the output redefinition approach is used in the adaptive controller design using time-delay neuro-fuzzy networks. The time-delay neuro-fuzzy networks with the rule representation of the TSK type fuzzy system have better learning ability for complex dynamics as compared with existing neural networks. The novel control structure and learning algorithm are given, and a simulation for the trajectory tracking of a flexible manipulator illustrates the control performance of the proposed control approach.     

Sliding mode-based adaptive tube model predictive control for robotic manipulators with model uncertainty and state constraints

In this paper, the optimal tracking control for robotic manipulatorswith state constraints and uncertain dynamics is investigated, and a sliding mode-based adaptive tube model predictive control method is proposed. First, utilizing the high-order fully actuated system approach, the nominal model of the robotic manipulator is constructed as the predictive model. Based on the nominal model, a nominal model predictive controller with the sliding mode is designed, which relaxes the terminal constraints, and realizes the accurate and stable tracking of the desired trajectory by the nominal system. Then, an auxiliary controller based on the node-adaptive neural networks is constructed to dynamically compensate nonlinear uncertain dynamics of the robotic manipulator. Furthermore, the estimation deviation between the nominal and actual states is limited to the tube invariant sets. At the same time, the recursive feasibility of nominal model predictive control is verified, and the ultimately uniformly boundedness of all variables is proved according to the Lyapunov theorem. Finally, experiments show that the robotic manipulator can achieve fast and efficient trajectory tracking under the action of the proposed method.     

基于不确定逼近的机械手自适应鲁棒预测控制

针对具有参数不确定性和未知外部干扰的机械手轨迹跟踪问题提出了一种多输入多输出自适应鲁棒预测控制方法. 首先根据机械手模型设计非线性鲁棒预测控制律, 并在控制律中引入监督控制项; 然后利用函数逼近的方法逼近控制律中因模型不确定性以及外部干扰引起的未知项. 理论证明了所设计的控制律能够使机械手无静差跟踪期望的关节角轨迹. 仿真验证了本文设计方法的有效性.