Control Scheme For Robot Manipulators Research Articles

Finite-time Tracking for Robot Manipulators with Continuous Control

Terminal attractors are introduced in dynamic sliding mode error coordinates in order to obtain two control schemes for robot manipulators that guarantee finite-time convergence of tracking errors. The simple passivity-based design yields continuous controllers with singularity-free closed-loop dynamics. Robustness to a class of bounded disturbance and complex friction models are discussed. Simulation data show the performance of the controllers.

Adaptive control of robot manipulator using fuzzy compensator

This paper presents two kinds of adaptive control schemes for robot manipulator which has the parametric uncertainties. In order to compensate these uncertainties, we use the FLS (fuzzy logic system) that has the capability to approximate any nonlinear function over the compact input space. In the proposed control schemes, we need not derive the linear formulation of robot dynamic equation and tune the parameters. We also suggest the robust adaptive control laws in all proposed schemes for decreasing the effect of approximation error. To reduce the number of fuzzy rules of the FLS, we consider the properties of robot dynamics and the decomposition of the uncertainty function. The proposed controllers are robust not only to the structured uncertainty such as payload parameter, but also to the unstructured one such as friction model and disturbance. The validity of the control scheme is shown by computer simulations of a two-link planar robot manipulator.

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.

Decentralized adaptive fuzzy control of robot manipulators

This paper develops a decentralized adaptive fuzzy control scheme for robot manipulators via a combination of genetic algorithm and gradient method. The controller for each link consists of a feedforward fuzzy torque-computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line by an improved genetic algorithm, that is to say, not only the parameters but also the structure of the fuzzy system are self-organized. Because genetic algorithm can operate successfully without the system model, no exact inverse dynamics of the robot system are required. The feedback fuzzy PD system, on the other hand, is tuned on-line using gradient method. In this way, the proportional and derivative gains are adjusted properly to keep the closed-loop system stable. The proposed controller has the following merits: (1) it needs no exact dynamics of the robot systems and the computation is time-saving because of the simple structure of the fuzzy systems; and (2) the controller is insensitive to various dynamics and payload uncertainties in robot systems. These are demonstrated by analyses of the computational complexity and various computer simulations.

A new stable tracking control scheme for robotic manipulators

The paper considers tracking control of robots in joint space. A new control algorithm is proposed based on the well known computed torque method and a compensating controller. The compensating controller is realized by using a switch type structure and an RBF neural network. It is shown that stability of the closed loop system and better tracking performance can be established based on Lyapunov theory. Simulation results are also provided to support our analysis.

A New Type of Robust Tracking SP-D Control for Manipulators

This paper is concerned with a new type of robust tracking control scheme for robot manipulators using SP-D (Saturated Proportional and Differential) feedback loops. In this paper, the control scheme based on quasi-natural potential is generalized from set-point regulation to trajectory tracking by exploiting an effective auxiliary variable which consists of a saturated positional error and a velocity error. Without any uncertainties in the manipulator model, the generalized tracking SP-D control scheme ensures globally asymptotic stability. In the presence of parametric model uncertainties, the tracking SP-D control scheme is improved in order to be robust against the uncertainties. The proposed robust tracking SP-D controller is able to guarantee globally uniformly ultimate boundedness theoretically. Experimental results on a two-link direct drive manipulator with the robust tracking controller are in agreement with the theoretical analysis and show the effectiveness of the control scheme.

A Tracking Controller for Manipulators with Flexible Joints Using Sliding Observer

We present a tracking control scheme for robot manipulators with flexible joints. Only the information from motors is used for feedback. The concept of "desired fictional motor position" is introduced. The stability of a dynamic control system with the sliding observer is guaranteed by using Lyapunov stability theory. To illustrate the effect of the algorithm including the controller and the sliding observer, computer simulations have been carried out using a SCARA 2-DOF manipulator.

Adaptive Control of Robot Manipulators Including Motor Dynamics

An adaptive control scheme for robot manipulators including motor dynamics is proposed in this paper. The proposed scheme avoids the assumption that the values of motor parameters are known which is required in reference (13). An exponential control law is first developed under the assumption of no uncertainty. This forms a controller structure for the adaptive control. Using this control structure, a full-order adaptive control law is proposed to overcome parameter uncertainty for both robot link and motor. The stability analysis is in the Lyapunov stability sense. The method is further extended to the task space. Extensive simulations are performed to compare the different control schemes.

Adaptive control for Robot Manipulators with Flexible Joints

This paper presents an adaptive control scheme for robot manipulators with flexible joints. Our control scheme theoretically guarantees that the tracking error converges to zero and that all the signals in the system remain bounded. We also analyze the robustness of the adaptive controller. To show the capability of the proposed controller, an example of a one-link robot manipulator with one revolute flexible joint is considered. The experimental results are given to demonstrate the effectiveness of this method.

Intelligent Control for Nonlinear Systems using Dynamic Neural Networks with Robotic Applications

ABSTRACTIn this article the problem of intelligent control for systems with complex, unknown, and high nonlinear dynamics is studied using neuro-control approaches. Some new schemes of dynamic neural networks (DNNs) are proposed to design robust learning control systems for a general class of multi-input and multi-output (MIMO) nonlinear systems with unknown dynamics. The detailed structure of the DNNs and their learning capability are first discussed. The synthesis and design methods for the purposes of regulation, tracking, and model reference control are then proposed. Based on the DNNs approaches presented in this article, a new torque control scheme for robot manipulators is developed. The potentials of this scheme are demonstrated extensively by simulation studies.

A Robust control of Robot Manipulators with Flexible Joints.

This paper presents a robust control scheme for robot manipulators with flexible joints. By the analysis of the properties of the model of the manipulator and the presented lemma, exponential convergence is ensured which dose not make any assumption on stiffnesses of joints. The uncertaity bounds of parameters are needed to derive the control law. The control input is always continuos and can guarantee global exponential stability of tracking errors. The control scheme is applied to an one link flexible-joint manipulator as an illustrative example.

A simple alternative to neural network control scheme for robot manipulators

Recent research results have shown that neural network techniques are effective in compensating highly nonlinear uncertainties in the robot model where computed torque method is used for robot motion control. One excellent work was reported by Ishiguro et. al. (1992). The purpose of this note is to present a simple alternate solution to the same control problem which does not require the use of a neural network. The solution is based on the disturbance rejection technique developed by Hsia (1989-92). Computer simulations show that the alternate control method works better. >

Unified formulation of variable structure control schemes for robot manipulators

A general target model is proposed in the task space to represent motion trajectory, interaction force trajectory, and second-order function relating the motion errors and the interaction force errors. Using variable structure model reaching control (VSMRC) strategy, the model is achieved in the sliding mode with robust performance. Reaching transient can be eliminated or guaranteed with prescribed quality. By choosing a suitable model for the application, robust motion control, impedance control, hybrid position/force control, or constrained motion control are achieved, respectively.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Design of a robust adaptive control law for robotic manipulators

AbstractIn this article, a robust adaptive control scheme for robotic manipulators is designed based on the concept of performance index and Lyapunov's second method. Compensators are selected for a given feedback system by using a quadratic performance index. Then the stability of the system is proven based on Lyapunov's method, where a Lyapunov function and its time‐derivative are derived from the selected compensators. In the process of stabilization, stability bounds are obtained for disturbances, control gains, adaptation gains, and desired trajectories, in the presence of feedback delay due to digital computation and first‐order hold in the control loop. © 1994 John Wiley &amp; Sons, Inc.

A novel direct adaptive control scheme for robotic manipulators

A novel direct adaptive controller is presented for robotic manipulators. The novelty of our results is due to the adaptive laws adjusting the control gains directly, rather than providing an explicit estimate of inertia parameters, which are then used in the control law. Furthermore, only four control gains should be adjusted adaptively, and that number is independent of the number of degrees-of-freedom of robotic manipulators. The regressor matrix used by the control law is calculated with respect to the desired trajectory. It is shown that the control strategies are globally stable in the presence of a class of state-dependent uncertainties, and the tracking error can be made arbitrarily small.

An iterative learning scheme for motion control of robots using neural networks: A case study

In this paper, an iterative learning controller using neural networks has been studied for the motion control of robotic manipulators. Simulations of a two-link robot have demonstrated that the proposed control scheme for robotic manipulators can greatly reduce tracking errors after a few trials. Our modification of the original back-propagation algorithm is employed in the neural network, resulting in a much faster learning rate. The results of simulation have also shown that the proposed iterative learning controller has a faster rate of convergence and better robustness.

Adaptive Control of Robotic Manipulators Using an Extended Kalman Filter

This paper presents a new adaptive motion control scheme for robotic manipulators. This is an adaptive computed torque method (CTM) that requires only position measurements. These measurements and the input torques are used in an extended Kalman filter (EKF) to estimate the inertial parameters of the full non-linear robot model as well as the joint positions and velocities. These estimates are used by the CTM to generate the input torques. The theory behind Kalman filtering provides clear guide-lines on the selection of the design parameters for the controller when noise is present. Simulation results illustrate the performance of this scheme and demonstrate its noise rejection properties.

On robust adaptive control of robot manipulators

In this paper we present a robust adaptive control scheme for robot manipulators with time-varying parameters and unmodeled dynamics. Our scheme ensures that all signals in the closed-loop robot system are bounded and the tracking error is of the order of the parameter variations and unmodeled dynamics in the mean.

A comparison of model-based path control algorithms for direct-drive SCARA robots

Various advanced control strategies are applied to a direct-drive SCARA robot and studied in computer simulations. Besides computed torque control and direct adaptive control, heuristic optimal control, a new path control scheme for robotic manipulators, is included in the comparison study. PD control, the traditional robot control method, is used for generating a comparing baseline. While all schemes are applied for the same tracking task, the effect of modelling errors and measurement noise is considered in robot performance evaluation. Simulation results show that (1) without model errors, all advanced control schemes can achieve higher tracking accuracy than PD control; (2) with a random measurement error of 1%, computed torque and direct adaptive control methods are inferior to PD control; (3) heuristic control proves to be the most robust control scheme in case of mixed model and measurement errors.

適応ロバスト制御のDDアームを用いた基礎実験

In the previous work, we proposed an adaptive robust control scheme for robot manipulators. In this paper, our scheme is applied to a two d. o. f. DD-arm, and the validity of our scheme is shown from the experimental viewpoint