Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms (FNNA).With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom (DoF). The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.     

基于模糊神经网络运算法则的并联机器人自适应控制研究

针对并联机器人数学模型不完全确知并包含外部扰动的非线性多变量系统,提出一种基于模糊神经网络运算法则（FNNA）的自适应控制策略。将各个支链的模糊规则通过神经网络进行在线训练并得出模糊规则的权重并将此运用于在线辨识非线性自适应控制系统的未知动态,有效抑制了系统的数学模型不精确所产生的误差及外部扰动。仿真结果表明该控制方法明显提高了控制系统的轨迹跟踪性能,并对外部干扰及系统的非线性具有很强的鲁棒性。     

Observer-based adaptive control of robot manipulators: Fuzzy systems approach

This paper presents a fuzzy adaptive control suitable for motion control of multi-link robot manipulators with structured and unstructured uncertainties. When joint velocities are available, full state fuzzy adaptive feedback control is designed to ensure the stability of the closed loop dynamic. If the joint velocities are not measurable, an observer is introduced and an adaptive output feedback control is designed based on the estimated velocities. In the proposed control scheme, we need not derive the linear formulation of robot dynamic equation and tune the parameters. To reduce the number of fuzzy rules of the fuzzy controller, we consider the properties of robot dynamics and the decomposition of the uncertainties terms. The proposed controller is robust against uncertainties and external disturbance. Further, it is shown that required stability conditions, in both cases, can be formulated as LMI problems and solved using dedicated software. The validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator.     

Adaptive Chattering Free Neural Network Based Sliding Mode Control for Trajectory Tracking of Redundant Parallel Manipulators

In this paper, an adaptive chattering free neural network‐based sliding mode control (ACFN‐SMC) method is proposed for tracking trajectories of redundant parallel manipulators. ACFN‐SMC combines adaptive chattering free radial basis function neural networks (RBFN), sliding mode control with online updating the robust term parameters, and a nonlinear compensation item for reducing tracking errors. The stability of the closed‐loop system with modeling uncertainties, frictional uncertainties, and external disturbances is ensured by using the Lyapunov method. The proposed controller has a simple structure and little computation time while securing dynamic performance with expected quality in tracking trajectories of redundant parallel manipulators. In addition, the ACFN‐SMC strategy does not need to know the upper bound of any uncertainties. From the simulation results, it is evident that the proposed control strategy not only has significantly higher robustness capability for uncertainties but also can achieve better chattering elimination when compared with those using existing intelligent control schemes.     

Adaptive manipulator trajectory control using neural networks

A unified study of adaptive control and neural network based control schemes for the trajectory tracking problem of robot manipulators is presented. Efficacy of parametrized adaptive algorithms in compensating the structured uncertainties in robot dynamics is verified through extensive simulation. The ability of neural networks to provide a robust adaptive framework in the presence of both structured and unstructured uncertainties is investigated. A case study is carried out in support of a parametrized adaptive scheme using neural networks. Simulation results clearly indicate that the neural network based adaptive controller achieves better tracking in the presence of parametric uncertainties as well as unmodelled effects compared to the simple direct adaptive scheme.     

Adaptive Control of a Two-Link Robot Using Batch Least-Square Identifier

We design a regulation-triggered adaptive controller for robot manipulators to efficiently estimate unknown parameters and to achieve asymptotic stability in the presence of coupled uncertainties. Robot manipulators are widely used in telemanipulation systems where they are subject to model and environmental uncertainties. Using conventional control algorithms on such systems can cause not only poor control performance, but also expensive computational costs and catastrophic instabilities. Therefore, system uncertainties need to be estimated through designing a computationally efficient adaptive control law. We focus on robot manipulators as an example of a highly nonlinear system. As a case study, a 2-DOF manipulator subject to four parametric uncertainties is investigated. First, the dynamic equations of the manipulator are derived, and the corresponding regressor matrix is constructed for the unknown parameters. For a general nonlinear system, a theorem is presented to guarantee the asymptotic stability of the system and the convergence of parameters’ estimations. Finally, simulation results are discussed for a two-link manipulator, and the performance of the proposed scheme is thoroughly evaluated.      

Adaptive learning tracking control of robotic manipulators with uncertainties

An adaptive learning tracking control scheme is developed for robotic manipulators by a synthesis of adaptive control and learning control approaches. The proposed controller possesses both adaptive and learning properties and thereby is able to handle robotic systems with both time-varying periodic uncertainties and time invariant parameters. Theoretical proofs are established to show that proposed controllers ensure asymptotical tracking performance. The effectiveness of the proposed approaches is validated through extensive numerical simulation results.     

Decentralized adaptive control scheme for control of a multi-arm-type robot

This paper deals with the problem of decentralized stabilization for a class of unknown large-scale dynamic systems formed of interconnected subsystems under constant disturbances. A decentralized adaptive control scheme is proposed based on the Lyapunov direct method and a condition of asymptotic stability is derived. Then the decentralized adaptive control scheme is applied for a class of robot manipulators to track the desired trajectory as closely as possible despite a wide range of manipulator motions and parameter uncertainties of links and payload.     

Distributed Force/Position Consensus Tracking of Networked Robotic Manipulators

In this paper, we address the tracking problem of distributed force/position for networked robotic manipulators in the presence of dynamic uncertainties. The end-effectors of the manipulators are in contact with flat compliant environment with uncertain stiffness and distance. The control objective is that the robotic followers track the convex hull spanned by the leaders under directed graphs. We propose a distributed adaptive force control scheme with an adaptive force observer to achieve the asymptotic force synchronization in constrained space, which also maintains a cascaded closed-loop structure separating the system into kinematic module and dynamic module. A decentralized stiffness updating law is also proposed to deal with the environment uncertainties. The convergence of tracking errors of force and position is proved using Lyapunov stability theory and input-output stability analysis tool. Finally, simulations are performed to show effectiveness of the theoretical approach.      

Dynamics and control of a novel 3-DOF parallel manipulator with actuation redundancy

This paper deals with the dynamics and control of a novel 3-degrees-of-freedom （DOF） parallel manipulator with actuation redundancy. According to the kinematics of the redundant manipulator, the inverse dynamic equation is formulated in the task space by using the Lagrangian formalism, and the driving force is optimized by utilizing the minimal 2-norm method. Based on the dynamic model, a synchronized sliding mode control scheme based on contour error is proposed to implement accurate motion tracking control. Additionally, an adaptive method is introduced to approximate the lumped uncertainty of the system and provide a chattering-free control. The simulation results indicate the effectiveness of the proposed approaches and demonstrate the satisfactory tracking performance compared to the conventional controller in the presence of the parameter uncertainties and un-modelled dynamics for the motion control of manipulators.     

Trajectory Tracking with Parallel Robots Using Low Chattering,Fuzzy Sliding Mode Controller

This paper proposes a trajectory tracking scheme which belongs to the sliding mode control (SMC) for the 4-degree-of-freedom (DOF) parallel robots. Two fuzzy logic systems (FLS) are first put forward to replace the constant switching control gain and the width of the boundary layer. The fuzzy adaptive supervisory controller (FASC) is combined with the fuzzy sliding mode control (FSMC) to further reduce the chattering. The design is simple and less fuzzy rules are required. The simulation results demonstrate that the chattering of the SMC is reduced greatly and the parallel robot realizes the trajectory tracking with very good robustness to the parameter uncertainties and external disturbances.     

A novel adaptive bilateral control scheme using similar closed‐loop dynamic characteristics of master/slave manipulators

This article presents a novel adaptive bilateral control scheme for obtaining ideal responses for teleoperation systems with uncertainties. A condition that is equivalent to getting an ideal response in teleoperation has been found to be making the closed‐loop dynamics of master and slave manipulators a similar form. An adaptive approach is applied to achieve similarity for the uncertain master and slave manipulators. Using the similar closed‐loop dynamic characteristics of master/slave teleoperation systems, excellent position and force tracking performance has been obtained without estimating the impedance of human and environment. The validity of the theoretical results is verified by experiments. © 2001 John Wiley & Sons, Inc.     

Impedance Control of Flexible Robot Arms with Parametric Uncertainties

This paper presents an adaptive impedance control strategy for flexible manipulators by using an end-effector trajectory control approach. The impedance control objective is converted into tracking a trajectory generated by a designed ideal impedance model. A manifold is designed to prescribe desirable performance of the system. An adaptive control scheme is derived in such that the motion of the system will converge and remain to the ideal manifold for the case of parametric uncertainties. Stability of the control system is analyzed. Simulations are carried out to demonstrate the effectiveness of the proposed control method.     

基于神经网络的不确定机器人自适应滑模控制

提出一种机器人轨迹跟踪的自适应神经滑模控制。该控制方案将神经网络的非线性映射能力与变结构控制理论相结合,利用RBF网络自适应学习系统不确定性的未知上界,神经网络的输出用于自适应修正控制律的切换增益。这种新型控制器能保证机械手位置和速度跟踪误差渐近收敛于零。仿真结果表明了该方案的有效性。     

Distributed adaptive containment control of networked flexible-joint robots using neural networks

This study presents a distributed adaptive containment control approach for a group of uncertain flexible-joint (FJ) robots with multiple dynamic leaders under a directed communication graph. The leaders are neighbors of only a subset of the followers. The derivatives of the leaders are unknown, namely, the position information of the leaders is only available for implementing the proposed control approach. The local adaptive dynamic surface containment controller for each follower is designed using only neighbors’ information to guarantee that all followers converge to the dynamic convex hull spanned by the dynamic leaders. The function approximation technique using neural networks is employed to estimate the model uncertainties of each follower. It is proved that the containment control errors converge to an adjustable neighborhood of the origin regardless of model uncertainties and the lack of shared communication information. Simulation results for FJ manipulators are provided to illustrate the effectiveness of the proposed adaptive containment control scheme.     

Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.     

时滞柔性关节机械臂自适应位置/力控制

针对具有时滞的柔性关节机械臂自适应位置和力控制问题进行了研究.首先,通过坐标变换得出降维的位置/力控制模型.随后,将时间滞后近似表示成一阶滞后,进行时滞补偿.利用自适应算法修正机械臂系统参数,克服模型参数不确定性对系统的影响.同时,采用反步控制技术设计机械臂位置/力控制器,运用Lyapunov稳定性定理证明控制器能使机械臂位置和力跟踪误差收敛.最后的仿真研究验证了控制方案的有效性.     

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.     

机器人自适应PID饱和输出反馈控制

针对机器人系统在仅有位置传感、驱动器饱和、存在建模不确定性及干扰等条件下的轨迹跟踪控制问题,提出了一种新的自适应PID控制方案。采用高精度滤波器估计机器人关节速度,采用带饱和函数的控制器限制输出力矩,采用自适应PID控制器补偿建模不确定性和干扰。通过Lyapunov直接法,证明系统的稳定性。最后以两关节机器人为例,给出仿真实验结果,验证了算法的有效性。     

机器人神经模糊控制

本文首先讨论了机器人动力学的特殊性，提出了一种基于神经网络的模糊控制方法。该方法借助于一类新型的神经网络结构，实现了模糊规则的自动更新和隶属函数的自调整。该算法被用于机器人动态控制，取得了满意的仿真结果。