An improved adaptive control for repetitive motion of robots

In this paper some adaptive nonlinear multivariable techniques used in the control of robotic manipulators are presented. The nonlinear control law and state feedback are used in achieving a linear inputoutput behavior for the controlled system. For the design of the adaptive nonlinear control, the exact feedback input-output linearization and the method of gradient are used. The nonlinear control law achieves also decoupling. Computer simulations are included to demonstrate some theoretical aspects and the performances of these controllers for a typical structure of robotic manipulator.

Cybernetics and Systems

[1993] Proceedings IEEE International Conference on Robotics and Automation, 1993

American Journal of Applied Sciences, 2007

This research investigates the performance of an adaptive controller for five-bar linkage robot. The proposed controller was entirely independent on the physical specifications of the robot. The controller was examined as the dynamics of the mechanical system varies for various environment conditions, which make it an ambiguous system. In this study, the introduced controller was designed based on the governing ideal Euler-Lagrange equations on the robot but assessed using the on-line dynamic simulation of the mechanism for different target configurations which guarantees the high performance and effectiveness of the designed controller.

Journal of Robotic Systems, 1994

In 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. 0 2994 john Wiley 6 Sons, Znc.

Control Engineering Practice, 2003

In this paper a new adaptive control law is designed for robotic manipulators, based on the use of reference velocities instead of the actual ones and feedback signals generated from position errors. The law in question is suitable for trajectory tracking and positioning tasks. Its peculiarities are: a) high signal to noise ratio in the control torques; b) absence of parameter drift in positioning tasks. Simulation and experimental tests are shown with the aim to both confirm the validity and illustrate the actual characteristics of the proposed control law.

Proceedings 1992 IEEE International Conference on Robotics and Automation

In the present paper we propose a globally convergent adaptive control scheme for robot motion control with the following features: First, the adaptation law possesses enhanced robustness with respect to noisy velocity measurements. Secondly, the controllex does not require the inclusion of high gain loops that may excite the unmodeled dynamics and amplify the noise level. Thirdly, we derive for the known parameter design a relationship between compensator gains and closed-loop convergence rates which is independent of the robot task. This helps the designer to carry out the gain tuning with an eye on the robustnessperformance tradeoff.

Robotica, 1993

SUMMARYThe need to meet demanding control requirements in increasingly complex dynamical control systems under significant uncertainties makes neural networks very attractive, because of their ability to learn, to approximate functions, to classify patterns and because of their potential for massively parallel hardware implementation. This paper proposes the use of artificial neural networks (ANN) as a novel approach to the control of robot manipulators. These are part of the general class of non-linear dynamic systems where non-linear compensators are required in the controller.

Automatica, 1992

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.

Journal of Intelligent and Robotic Systems, 1996

Neural network based adaptive controllers have been shown to achieve much improved accuracy compared with traditional adaptive controllers when applied to trajectory tracking in robot manipulators. This paper describes a new Recursive Prediction Error technique for estimating network parameters which is more computationally efficient. Results show that this neural controller suppresses disturbances accurately and achieves very small errors between commanded and actual trajectories.

Based on a combination of a PD controller and a switching type two-parameter compensation force, an iterative learning controller with a projection-free adaptive algorithm is presented in this paper for repetitive control of uncertain robot manipulators. The adaptive iterative learning controller is designed without any a priori knowledge of robot parameters under certain properties on the dynamics of robot manipulators with revolute joints only. This new adaptive algorithm uses a combined time-domain and iteration-domain adaptation law allowing to guarantee the boundedness of the tracking error and the control input, in the sense of the infinity norm, as well as the convergence of the tracking error to zero, without any a priori knowledge of robot parameters. Simulation results are provided to illustrate the effectiveness of the learning controller. ᭧

Frontiers in Robotics and AI, 2015

This paper deals with a new control scheme for parallel kinematic manipulators (PKMs) based on the L 1 adaptive control theory. The original L 1 adaptive controller is extended by including an adaptive loop based on the dynamics of the PKM. The additional modelbased term is in charge of the compensation of the modeled non-linear dynamics in the aim of improving the tracking performance. Moreover, the proposed controller is enhanced to reduce the internal forces, which may appear in the case of redundantly actuated PKMs (RA-PKMs). The generated control inputs are first regulated through a projection mechanism that reduces the antagonistic internal forces, before being applied to the manipulator. To validate the proposed controller and to show its effectiveness, realtime experiments are conducted on a new four degrees-of-freedom (4-DOFs) RA-PKM developed in our laboratory.

Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003.

In this paper, three nonlinear H, control techniques used to control a robot manipulator are compared. The first technique consists in an explicit solution of the robotic H, control problem. It is found considering that the dynamic parameter matrices are exactly known. In the second, a linear parameterization is used to generate an adaptive control law in the presence of uncertain parameters. Finally, a neural network is considered when there is unmodeled dynamics. Results obtained from the experimental robot manipulator UArm 11, using the three methodologies, are presented.

Springer International Publishing, 2014

In this paper we present a nonlinear adaptive output feedback control algorithm. The algorithm is for model reference adaptive control of robotic manipulators. This algorithm uses model signals in the regressor and the linearization law and hence, does not require an observer. We show via various simulations that this algorithm has a region of convergence. We also show that the region of convergence can be increased if a normalizing factor is used in the adaptation law.

Proceedings. The First IEEE Regional Conference on Aerospace Control Systems,, 1993

This paper presents a practical robust adaptive control scheme to deal with real systems in the presence of structured and unstructured uncertainties. An application of the proposed robust adaptive control scheme for a mechanical manipulator has been developed and numerically demonstrated.