Direct-drive Robot Manipulator Research Articles

Position Tracking Constrained Adaptive Output Feedback Control of Robotic Manipulators

Abstract This work presents the design and the corresponding stability analysis of a model-based, joint position tracking error constrained, adaptive output feedback (OFB) controller for robot manipulators. Specifically, provided that the initial joint position tracking error starts within a predefined region, the proposed controller algorithm ensures that the joint tracking error remains inside this region and asymptotically approaches zero, despite the lack of joint velocity measurements and uncertainties associated with the system dynamics. The constraint imposed on the position tracking error term ensures predictable overshoot for the overall system and enables a predetermined transient performance. The need for the joint velocity measurements is removed via the use of a surrogate filter formulation in conjunction with the use of desired model compensation. The stability and the convergence of the closed-loop system are proved via a barrier Lyapunov function (BLF)-based argument. Extensive numerical simulations and experimental studies performed on a two-link, direct-drive robotic manipulator are provided to illustrate the feasibility and effectiveness of the proposed method.

PD with neuro-adaptive compensation control using the signed power function

This paper presents a neuro-adaptive control scheme dedicated to solve the motion trajectory tracking problem of robot manipulators under uncertain parameters and external disturbances. A two degrees of freedom direct-drive robot manipulator was taken as a case of study. The proposed controller is able to guarantee asymptotic convergence of the position and velocity tracking errors, and the weights of the artificial neural network are bounded. Artificial neural network weights are updated online using filtered error approach, adaptive laws and signed power function. This scheme does not require any offline training. The neuro-adaptive controller is experimentally validated and compared with a classical one-layer neuro-adaptive controller; the proposed scheme obtains better quantitative metrics related with the RMS values of the position and velocity errors, and a good robust behaviour against external disturbances.

Robust state/output feedback linearization of direct drive robot manipulators: A controllability and observability analysis

In this research, a robust feedback linearization technique is analysed for robot manipulators control. A complete first-order Taylor series expansion is used to linearize the robot dynamics which takes into account initial conditions and the Taylor-series remainder. A modified PD control law with Taylor-series compensation is used to guarantee robust reference tracking. Whilst classic feedback linearization controllers guarantee asymptotic convergence to zero, the proposed approach shows that, for real applications, if the linearized robot dynamics is stable then the nonlinear robot states are also stable and remain bounded. This premise is assessed via Lyapunov stability theory under a controllability and observability analysis; and hence, exponential convergence to a bounded set is concluded. Experiments are carried out using a 1-degree of freedom robot and a 4-degree of freedom exoskeleton robot to validate the proposed approach.

UKF-Based Neural Training for Nonlinear Systems Identification and Control Improvement

Numerous academic works have addressed the identification and control problem for complex dynamic systems. In recent decades, the use of control algorithms based on neural networks (NNs) has been highlighted, which have shown satisfactory results in the trajectory tracking control for a class of discrete-time nonlinear systems. The present work proposes an efficient learning law for discrete-time recurrent high order neural networks (RHONNs), using a training algorithm based on an unscented Kalman filter (UKF). This learning law is applied through a decentralized neural block control using UKF (DNBC-UKF), for trajectory tracking control of a class of discrete-time nonlinear systems. The proposed controller is experimentally evaluated by means of real-time tests in a two degrees of freedom (DOF) vertical direct-drive robotic manipulator against a decentralized neural block control using EKF (DNBC-EKF). The Lyapunov stability theory is used to show that the identification errors of RHONNs are semi-globally uniformly ultimately bounded (SGUUB), the RHONN weights remains bounded, and the closed-loop tracking errors go to zero.

Global Saturated Regulator with Variable Gains for Robot Manipulators

In this paper, we propose a set of saturated controllers with variable gains to solve the regulation problem for robot manipulators in joint space. These control schemes deliver torques inside the prescribed limits of servomotors. The gamma of variable gains is formed by continuous, smooth, and differentiable functions of the joint position error and velocity of the manipulator. A strict Lyapunov function is proposed to demonstrate globally asymptotic stability of the closed-loop equilibrium point. Finally, the functionality and performance of the proposal are illustrated via simulation results and comparative analysis against Proportional-Derivative (PD) control scheme on a two-degrees-freedom direct-drive robot manipulator.

Global Saturated Regulator with Variable Gains for Robot Manipulators

In this paper, we propose a set of saturated controllers with variable gains to solve the regulation problem for robot manipulators in joint space. These control schemes deliver torques inside the prescribed limits of servomotors. The gamma of variable gains is formed by continuous, smooth, and differentiable functions of the joint position error and velocity of the manipulator. A strict Lyapunov function is proposed to demonstrate globally asymptotic stability of the closed-loop equilibrium point. Finally, the functionality and performance of the proposal are illustrated via simulation results and comparative analysis against Proportional-Derivative (PD) control scheme on a two-degrees-freedom direct-drive robot manipulator.

An asinh-type regulator for robot manipulators with global asymptotic stability

In this paper, a new asinh-type control scheme with gravity compensation for the position control problem of robot manipulators in joint space is presented. The properties and characteristics of the asinh control structure make the position error and the motion velocity asymptotically converge to the equilibrium point. A strict Lyapunov function to formally prove global asymptotic stability is developed. The tuning of the control gains is obtained by PSO (Particle Swarm Optimization) technique without saturating the servomotors. To illustrate the effectiveness and performance of the proposed scheme, an experimental comparative analysis between the proportional-derivative (PD) and atanh controls against the proposed algorithm on a three degrees of freedom direct-drive robot manipulator is carried out.

A Family of Hyperbolic-Type Explicit Force Regulators with Active Velocity Damping for Robot Manipulators

This paper addresses the explicit force regulation problem for robot manipulators in interaction tasks. A new family of explicit force-control schemes is presented, which includes a term driven by a large class of saturated-type hyperbolic functions to handle the force error. Also, an active velocity damping term with the purpose of obtaining energy dissipation on the contact surface is incorporated plus compensation for gravity. In order to ensure asymptotic stability of the closed-loop system equilibrium point in Cartesian space, we propose a strict Lyapunov function. A force sensor placed at the end-effector of the robot manipulator is used in order to feed back the measure of the force error in the closed-loop, and an experimental comparison of the performanceL2-norm between 5 explicit force control schemes, which are the classical proportional-derivative (PD), arctangent, and square-root controls and two members of the proposed control family, on a two-degree-of-freedom, direct-drive robot manipulator, is presented.

Regulación Saturada con Ganancia Variable Derivativa de Robots Manipuladores

In this paper a family with a large number of hyperbolictype saturated regulators for robot manipulators, was presented. The proposed regulators consider a constant proportional gain while the derivative variable gain is self-tuned according to a function that depends on the position error, speed of motion and a damping factor, in order to modify the velocity of the transient response of the robot. The derivative control with variable gain enables reduced overshots, oscillations and ripple, enabling a smooth arrival to the steady state. The paper also proposes a strict Lyapunov function which enables the demonstration of asymptotic global stability of the closed-loop equation. In order to illustrate the performance and functionality of the proposed family of control schemes, an experimental comparison between seven control schemes was implemented. Five of these control schemes belong to the proposed family while two additional control schemes are well-known strategies such as the proportional-derivative (PD) and hyperbolic tangent (Tanh) control schemes. The experiments were performed by using a three degree-of-freedom, direct-drive robot manipulator.

Adaptive Voltage-based Control of Direct-drive Robots Driven by Permanent Magnet Synchronous Motors

Tracking control of the direct-drive robot manipulators in high-speed is a challenging problem. The Coriolis and centrifugal torques become dominant in the high-speed motion control. The dynamical model of the robotic system including the robot manipulator and actuators is highly nonlinear, heavily coupled, uncertain and computationally extensive in non-companion form. In order to overcome these problems, this paper presents a novel adaptive control for direct-drive robot manipulators driven by Permanent Magnet Synchronous Motors (PMSM) in tracking applications. The novelty of this paper is that the proposed adaptive law is free from manipulator dynamics by using the Voltage Control Strategy (VCS). Additionally, a state space model of the robotic system driven by PMSM is presented. The VCS differs from the commonly used control strategy for robot manipulators the so called torque control strategy. The position control of the PMSM is effectively used for the tracking control of the robot manipulator. This idea takes the control problem from the manipulator control to the motor control resulting in a simple yet efficient control design. Compared with the torque control, the control design is simpler, easier to implement with better tracking performance. The control method is verified by stability analysis. Simulation results show superiority of the proposed control to the torque control applied by field oriented control on the direct-drive robot driven by PMSM.

Unbounded regulators with variable gains for a direct-drive robot manipulator

This paper addresses the position-control problem with variable gains for robot manipulators. We present a new regulator based on a hyperbolic-sine structure with tuning rules for control gains. It is demonstrated that the equilibrium point of the closed-loop system is globally, asymptotically stable according to Lyapunov theory. By using a similar methodology, this concept can be extended to other unbounded controllers such as PD and PID. In order to show the usefulness of the proposed scheme and with the purpose of validating its asymptotical stability property, an experimental comparison involving constant gains controllers, for example: simple PD, PID and hyperbolic-tangent schemes vs variable-gains hyperbolic-sine and PD control schemes, was performed by using a three degree-of-freedom, direct-drive robot manipulator.

Simultaneous fault diagnosis for robot manipulators with actuator and sensor faults

This paper studies a model-based fault detection and isolation (FDI) scheme for robot manipulators with actuator and sensor faults. Without adding redundant joint sensor measurements, the multiple faults simultaneously occurring in the actuators and sensors of the manipulator are detected, estimated and isolated. Considering Lipschitz-like nonlinearities and modeling uncertainties, a nonlinear adaptive observer is presented to estimate the faulty parameters with a pre-specified estimation error bound. The performances of the proposed FD scheme, including the robustness of observers to the uncertainties, the accuracy of fault estimation and the rapidity of fault diagnosis, are rigorously analyzed. The proposed approach is verified by a simulation of Integrated Motion Inc. (IMI) two-link, revolute, direct-drive robot manipulator.

Unbounded regulators with variable gains for a direct-drive robot manipulator

This paper addresses the position-control problem with variable gains for robot manipulators. We present a new regulator based on a hyperbolic-sine structure with tuning rules for control gains. It is demonstrated that the equilibrium point of the closed-loop system is globally, asymptotically stable according to Lyapunov theory. By using a similar methodology, this concept can be extended to other unbounded controllers such as PD and PID. In order to show the usefulness of the proposed scheme and with the purpose of validating its asymptotical stability property, an experimental comparison involving constant gains controllers, for example: simple PD, PID and hyperbolic-tangent schemes vs variable-gains hyperbolic-sine and PD control schemes, was performed by using a three degree-of-freedom, direct-drive robot manipulator.

Voltage Control Strategy for Direct-drive Robots Driven by Permanent Magnet Synchronous Motors

Torque control strategy is a common strategy to control robotic manipulators. However, it becomes complex duo to manipulator dynamics. In addition, position control of Permanent Magnet Synchronous Motors (PMSMs) is a complicated control. Therefore, tracking control of robots driven by PMSMs is a challenging problem. This article presents a novel tracking control of electrically driven robots which is free from manipulator model. The proposed control law is simple but robust against uncertainties associated with manipulator dynamics. The novelty is the developing of voltage control strategy for the direct-drive robots driven by PMSMs. In addition, a state-space model is obtained for the robotic system including the direct-drive robot manipulator and the PMSMs. Then, the proposed approach is verified by stability analysis. A comparative study through simulations shows the superiority of the voltage control strategy to the torque control strategy.

A new continuous high‐gain controller scheme for a class of uncertain nonlinear systems

SUMMARYIn this study, we present a new robust continuous controller mechanism for the tracking problem of uncertain nonlinear systems. The proposed strategy is based on a Lyapunov‐type stability argument and only requires the uncertainties of the dynamical system to be the first‐order differentiable to achieve asymptotic practical tracking. For the ease of presentation, the controller formulation is presented on a general, second‐order dynamical system, extension to higher order versions are also possible with a considerably small effort. Simulation studies comparing the performance of the proposed method with the classical Sliding mode and robust integral of the sign of the error controller are presented to illustrate the performance and the feasibility of the proposed strategy. Experimental validation on a two link direct drive robot manipulator are also included to illustrate the implementability of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

Robust-tracking control for robot manipulator with deadzone and friction using backstepping and RFNN controller

This study deals with a robust non-smooth non-linearity compensation scheme for the direct-drive robot manipulator with an asymmetric deadzone, dynamic friction in joints and between the environmental contact space and end-effector and uncertainty. A model-free recurrent fuzzy neural network (RFNN) control system to approximate the ideal backstepping control law is designed to replace the traditional model-based adaptive controller, which requires information on the robots dynamics in advance. The simple dead-zone estimator and friction compensator based on the elasto-plastic friction model are developed in order to estimate unknown dead-zone width and friction parameters. The Lyapunov stability analysis yields the adaptive laws of the RFNN controller as well as the estimators of a dead-zone width and an elasto-plastic friction parameter. The validity of the proposed control scheme is confirmed from simulated results for free and constrained direct-drive robots with a deadzone in joint actuator, dynamic friction in joints and contact surfaces of the end-effector and uncertainty.

Robust adaptive deadzone and friction compensation of robot manipulator using RWCMAC network

A robust adaptive compensation scheme is presented for compensation of asymmetric deadzone, dynamic friction and uncertainty in the direct-drive robot manipulator. Simple estimation laws are derived to build observers for estimation of deadzone and friction based on the LuGre friction model. A model-free RWCMAC controller to mimic the ideal control law is employed to overcome some shortcomings of the traditional model-based adaptive controller, which requires information on the robots dynamics in advance. The Lyapunov stability analysis yields the adaptive laws of the RWCMAC network as well as observers of deadzone and friction. Furthermore, the stability and optimal convergence speed of the learning rates of the RWCMAC is also guaranteed by employing the fully informed particle swarm (FIPS) algorithm. Robust tracking performance of the proposed control schemes is verified by simulations of direct-drive robots with deadzone in joint input torque, joint dynamic friction and uncertainty.

Triangular form for Euler–Lagrange systems with application to the global output tracking control

This paper presents a practical method to solve the problem of global output feedback tracking trajectories for a class of Euler–Lagrange systems when the variables of velocity are the unmeasured part of the state. We exhibited a new output feedback control scheme, which globally exponentially stabilize trajectories. It relies on the determination of a change of coordinates which gives to the systems a triangular form. Results are illustrated on the academic example of the two-link direct drive robot manipulator.

Position Control of Direct-Drive Robot Manipulators with PMAC Motors Using Enhanced Fuzzy PD Control

A position control approach for direct-drive robot manipulators with permanent magnet AC (PMAC) motors is proposed. The conventional vector control architecture has been simplified by specifying the motor stator phase so that the rotating d-axis current is zero. The position control is designed to be an enhanced fuzzy PD controller, by incorporating two nonlinear tracking differentiators into a conventional fuzzy PD controller. The proposed control methodology is easy to implement, and exhibits better control performance than conventional control methods. Experiments conducted on a single-link manipulator directly driven by a PMAC motor demonstrate the validity of the proposed approach.

Perspectives of Computational Intelligence in Robotics and Automation

Perspectives of Computational Intelligence in Robotics and Automation