Robust scheme of global parallel force/position regulators for robot manipulators under environment uncertainty

A simple robust scheme of parallel force/position control is proposed in this paper to deal with two problems for non-planar constraint surface and nonlinear mechanical feature of environment: i) uncertainties in environment that are usually not available or difficult to be determined in most practical situations; ii) stability problem or/and integrator windup due to the integration of force error in the force dominance rule in parallel force/position control. It shows that this robust scheme is a good alternative for anti-windup. In the presence of environment uncertainties, global asymptotic stability of the resulting closed-loop system is guaranteed; it also shows robustness of the proposed controller to uncertain environment with complex characteristics. Finally, numerical simulation verifies results via contact task of a two rigid-links robot manipulator.     

Stability analysis of robust adaptive hybrid position/force controller for robot manipulators using neural network with uncertainties

The aim of this paper is to design a robust adaptive neural network-based hybrid position/force control scheme for robot manipulators in the presence of model uncertainties and external disturbance. The feedforward neural network employed to learn a highly nonlinear function requires no preliminary learning. The control purposes are to achieve the stability in the sense of Lyapunov for desired interaction force between the end-effector and the environment and to regulate robot tip position in cartesian space. An adaptive compensator is also developed to eliminate the effect of disturbance term of neural network approximation error and external disturbance or unmodeled dynamics etc. A key feature of this compensator is that the prior information of the disturbance bound is not required. Finally, a comparative simulation study with a model-based robust control scheme for a two-link robot manipulator is presented.     

Reinforcement Learning and Robust Control for Robot Compliance Tasks

The complexity in planning and control of robot compliance tasks mainly results from simultaneous control of both position and force and inevitable contact with environments. It is quite difficult to achieve accurate modeling of the interaction between the robot and the environment during contact. In addition, the interaction with the environment varies even for compliance tasks of the same kind. To deal with these phenomena, in this paper, we propose a reinforcement learning and robust control scheme for robot compliance tasks. A reinforcement learning mechanism is used to tackle variations among compliance tasks of the same kind. A robust compliance controller that guarantees system stability in the presence of modeling uncertainties and external disturbances is used to execute control commands sent from the reinforcement learning mechanism. Simulations based on deburring compliance tasks demonstrate the effectiveness of the proposed scheme.     

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.      

Nonlinear robust adaptive Cartesian impedance control of UAVs equipped with a robot manipulator

In this paper, a new nonlinear robust adaptive impedance controller is addressed for Unmanned Aerial Vehicles (UAVs) equipped with a robot manipulator that physically interacts with environment. A UAV equipped with a robot manipulator is a novel system that can perform different tasks instead of human being in dangerous and/or inaccessible environments. The objective of the proposed robust adaptive controller is control of the UAV and its robotic manipulator’s end-effector impedance in Cartesian space in order to have a stable physical interaction with environment. The proposed controller is robust against parametric uncertainties in the nonlinear dynamics model of the UAV and the robot manipulator. Moreover, the controller has robustness against the bounded force sensor inaccuracies and bounded unstructured modeling (nonparametric) uncertainties and/or disturbances in the system. Tracking performance and stability of the system are proved via Lyapunov stability theorem. Using simulations on a quadrotor UAV equipped with a three-DOF robot manipulator, the effectiveness of the proposed robust adaptive impedance controller is investigated in the presence of the force sensor error, and parametric and non-parametric uncertainties.     

Bilateral parallel force/position teleoperation control

The extension of parallel force/position control to teleoperation systems is considered in this article. In the proposed four‐channel bilateral controller, higher priority is granted to position control at the master side and to force control at the slave side. The primary goal of this control architecture is the enhancement of force and position tracking performance in the presence of uncertainties in the system and environment. The stability and performance of the proposed controller is investigated by analyzing the three decoupled single‐degree‐of‐freedom systems obtained from decoupling and projecting the closed‐loop system dynamics onto the slave task‐space orthogonal directions. Experimental results demonstrate significant improvement in transparency. © 2002 Wiley Periodicals, Inc.     

Implementation of robust impedance and force control

In this paper, we discuss the problem of implementing impedance control in the presence of model uncertainties and its application to robot force control. We first propose a sliding mode-based impedance controller. The implementation of the targeted impedance, and the preservation of stability in the presence of model uncertainties, are the key issues in the proposed approach. Using sliding mode control, a simple and robust algorithm is obtained so that the targeted impedance can be accurately implemented without the exact model of the robot. The controller is designed in terms of the task space coordinates. The chattering in the sliding mode control is eliminated by using a continuous function. The problem of force control is also addressed for the impedance controlled robot. An off-line estimation method of the environment model is suggested and used in the force control scheme. The proposed impedance and force control schemes have been experimentally verified on a two degree-of-freedom direct-drive robot arm. The experimental results are presented in this paper.     

Force Reflecting Bilateral Control of Master–Slave Systems in Teleoperation

In this paper, a simple structure design with arbitrary motion/force scaling to control teleoperation systems, with model mismatches is presented. The goal of this paper is to achieve transparency in presence of uncertainties. The master–slave systems are approximated by linear dynamic models with perturbed parameters, which is called the model mismatch. Moreover, the time delay in communication channel with uncertainties is considered. The stability analysis will be considered for two cases: (1) stability under time delay uncertainties and (2) stability under model mismatches. For the first case, two local controllers are designed. The first controller is responsible for tracking the master commands, while the second controller is in charge of force tracking as well as guaranteeing stability of the overall closed-loop system. In the second case, an additional term will be added to the control law to provide robustness to the closed-loop system. Moreover, in this case, the local slave controller guarantees the position tracking and the local master controller guarantees stability of the inner closed-loop system. The advantages of the proposed method are two folds: (1) robust stability of the system against model mismatches is guaranteed and (2) structured system uncertainties are well compensated by applying independent controllers to the master and the slave sites. Simulation results show good performance of the proposed method in motion tracking as well force tracking in presence of model mismatches and time delay uncertainties.     

Neural network-based position synchronised internal force control scheme for cooperative manipulator system

More complex problems of simultaneous position and internal force control occur with cooperative manipulator systems than that of a single one. In the presence of unwanted parametric and modelling uncertainties as well as external disturbances, a decentralised position synchronised force control scheme is proposed. With a feedforward neural network estimating engine, a precise model of the system dynamics is not required. Unlike conventional cooperative or synchronised controllers, virtual position and virtual synchronisation errors are introduced for internal force tracking control and task space position synchronisation. Meanwhile joint space synchronisation and force measurement are unnecessary. Together with simulation studies and analysis, the position and the internal force errors are shown to asymptotically converge to zero. Moreover, the controller exhibits different characteristics with selected synchronisation factors. Under certain settings, it can deal with temporary cooperation by an intelligent retreat mechanism, where less internal force would occur and rigid collision can be avoided. Using a Lyapunov stability approach, the controller is proven to be robust in face of the aforementioned uncertainties.     

Robust PD Control Using Adaptive Compensation for Completely Restrained Parallel-Wire Driven Robots: Translational Systems Using the Minimum Number of Wires Under Zero-Gravity Condition

A parallel-wire driven mechanism uses flexible wires instead of heavy rigid links. In this paper, we propose a robust point-to-point (PTP) position control method in the task-oriented coordinates for completely restrained parallel wire-driven robots, which are translational systems using the minimum number of wires under zero-gravity conditions. In the cases where parallel-wire driven robots are disassembled/assembled and used outdoors (also applied in space), actuator positions would be uncertain or contain some errors. The error of internal force among wires that results from such uncertainty of actuator positions deteriorates positioning performance. To overcome such a difficulty, adaptive compensation is employed for robust PD control against the error of internal force, in this paper. It is necessary for the adaptive compensation to separate the internal force term linearly into a regressor matrix and a parameter vector concerned with the errors of actuator positions. The internal force term, however, possesses the nonlinear characteristic concerned with the errors of actuator position. Noting the structure of the internal force term, this paper shows that measuring both the position of an end-effector and wire length in real time enables the linear separation. Not only does this robust PD control method ensure precise positioning using external sensors; it enhances the robustness for uncertainty of the Jacobian matrix, which results from the error of actuator installation. First, we explain the linearization of the internal force term. Next, the robust PD control for the parallel-wire driven system using the uncertain Jacobian matrix is proposed; then, we prove the motion convergence to desired points and discuss its robustness based on Lyapunov stability analysis. Finally, the usefulness of the proposed control method is demonstrated through experiments and simulations.     

基于RBF神经网络补偿的一种绳牵引并联机器人 支撑系统的力/位混合控制

为了保证用于风洞试验的绳牵引并联机器人支撑系统(wire-driven parallel robot support system, WDPRSS)的末端执行精度,设计一种采用Hamilton-Jacobi Inequality(HJI)定理并基于RBF神经网络补偿的力/位混合控制.通过对WDPRSS的动力学建模分析,选择以位姿作为变量建立WDPRSS的整体动力学方程,将所设计的力/位混合控制代入到整体动力学方程中得到误差闭环系统,对闭环系统进行稳定性分析,结果表明WDPRSS是趋于渐近稳定特性的.对八绳牵引的并联机器人支撑系统进行Matlab/Simulink仿真实验,仿真结果表明所设计的力/位混合控制是正确有效的,满足控制精度要求,并将所设计的力/位混合控制与PD控制进行对比分析.最后,通过样机实验验证所提出控制方案的有效性.     

A novel adaptive robust control architecture for bilateral teleoperation systems under time‐varying delays

Bilateral teleoperation technology has caused wide attentions due to its applications in various remote operation systems. The communication delay becomes one of the main challenging issues in the teleoperation control design. Meanwhile, various nonlinearities, parameter variations, and modeling uncertainties existing in manipulator and environment dynamics need to be considered carefully in order to achieve good control performance. In this paper, a globally stable nonlinear adaptive robust control algorithm is developed for bilateral teleoperation systems to deal with these control issues. Namely, the unknown dynamical parameters of the environmental force are estimated online by the improved least square adaptation law. A novel communication structure is proposed where only the master position signal is transmitted to the slave side for the tracking design, and the online estimators of the environmental parameters are transmitted from the slave to the master to replace the traditional environmental force measurement. Because the estimated environmental parameters are not power signals, the passivity problem of the communication channel and the trade‐off limitation between the transparency performance and robust stability in traditional teleoperation control are essentially avoided. The nonlinear adaptive robust control is subsequently developed to deal with nonlinearities, unknown parameters, and modeling uncertainties of the master, slave, and environmental dynamics, so that the guaranteed transient and steady‐state transparency performance can be achieved. The experiments on two voice‐coil motor‐driven manipulators are carried out, and the comparative results verify that the proposed control algorithm achieves the excellent control performance and the guaranteed robust stability simultaneously under time delays. Copyright © 2014 John Wiley & Sons, Ltd.     

Robust controller design for a class of nonlinear uncertain chemical processes

This article considers the issue of designing robust controllers for single-input/single-output nonlinear chemical processes whose uncertainties satisfy the so-called generalized matching condition. The nominal system (mathematical model) is assumed to be input–output linearizable and the only assumption on uncertainties is that they are bounded. A design methodology of combining the techniques of the differential geometric feedback linearization, the sliding mode control strategy and the adaptive state feedback is presented. Based on the nominal system and the related bounds of uncertainties, a hybrid nonlinear controller, which is more practicable and easily implemented than many other existing ones in the literature, is proposed. A Lyapunov-based approach is utilized to guarantee the robust stability and behavior of the closed-loop system. For demonstrating the effectiveness and applicability of the proposed scheme, we applied it to the control of a continuously stirred tank reactor (CSTR) in the presence of uncertainties including unmodeled side reaction, measuring error, and/or extra unmeasured disturbances. The potential use of a sliding observer along with the proposed scheme is also investigated in this work. Extensive simulation results reveal that the proposed scheme appears to be a practical and promising approach to the robust control of nonlinear uncertain chemical processes.     

Adaptive robust motion/force control of holonomic-constrained nonholonomic mobile manipulators.

In this paper, adaptive robust force/motion control strategies are presented for mobile manipulators under both holonomic and nonholonomic constraints in the presence of uncertainties and disturbances. The proposed control is robust not only to parameter uncertainties such as mass variations but also to external ones such as disturbances. The stability of the closed-loop system and the boundedness of tracking errors are proved using Lyapunov stability synthesis. The proposed control strategies guarantee that the system motion converges to the desired manifold with prescribed performance and the bounded constraint force. Simulation results validate that the motion of the system converges to the desired trajectory, and the constraint force converges to the desired force.     

电液伺服系统的多滑模鲁棒自适应控制

针对一类参数与外负载非匹配不确定的非线性高阶系统,提出了一种基于逐步递推方法的多滑模鲁棒自适应控制策略.应用逐步递推的多滑模控制方法简化了高阶系统的控制问题,同时在自适应控制中加入鲁棒控制的方法,以消除不确定性对控制性能的影响.首先利用逐步递推方法与状态反馈精确线性化理论,得出确定系统的多滑模控制器设计方法;然后基于Lyapunov稳定性分析方法,给出不确定系统的参数自适应律,及鲁棒自适应控制器的设计方法.本文把该控制策略应用到电液伺服系统的位置跟踪控制中,仿真结果显示,该控制方法具有较强的鲁棒性及良好的跟踪效果.     

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

New enhanced performance robust control design scheme for grid-connected VSI

In this paper, a new method for intelligent robust control design is presented that achieves the best possible convergence rate of the system, utilizing the knowledge on the range of uncertain parameter. Thus resulting in enhanced stability and performance. The proposed method is applied to the grid-connected voltage source inverter (VSI) system with uncertainties in grid-impedance. Simulation and experimental results illustrate the efficacy of the proposed scheme. Comparison with existing methods shows that the proposed scheme can provide better reference tracking, stability for a wider uncertainty range, and improved transient and steady-state performance with low implementation cost.     

An output feedback parallel force/position regulator for a robot manipulator in contact with a compliant environment

In this paper the problem of regulating force and position for a robot manipulator in contact with an elastically compliant environment is considered. In the framework of parallel force/position control, an output feedback regulator with gravity compensation and desired force feedforward is proposed which only requires measurements of force and position. Semiglobal stability of the closed-loop system around the equilibrium is shown via a Lyapunov argument.     

Adaptive sensorless robust control of AC drives based on sliding mode control theory

This paper focuses in the design of a new adaptive sensorless robust control to improve the trajectory tracking performance of induction motors. The proposed design employs the so‐called vector (or field oriented) control theory for the induction motor drives, being the designed control law based on an integral sliding‐mode algorithm that overcomes the system uncertainties. This sliding‐mode control law incorporates an adaptive switching gain in order to avoid the need of calculating an upper limit for the system uncertainties. The proposed design also includes a new method in order to estimate the rotor speed. In this method, the rotor speed estimation error is presented as a first‐order simple function based on the difference between the real stator currents and the estimated stator currents. The stability analysis of the proposed controller under parameter uncertainties and load disturbances is provided using the Lyapunov stability theory. The simulated results show, on the one hand that the proposed controller with the proposed rotor speed estimator provides high‐performance dynamic characteristics, and on the other hand that this scheme is robust with respect to plant parameter variations and external load disturbances. Finally, experimental results show the performance of the proposed control scheme. Copyright © 2006 John Wiley & Sons, Ltd.