Robust Adaptive Control and its Application to a Mechanical Manipulator (original) (raw)
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On robust adaptive control of robot manipulators
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.
A robust adaptive controller for robot manipulators
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.
Design of a robust adaptive control law for robotic manipulators
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.
Turkish Journal of Electrical Engineering and Computer Sciences, 2013
In this paper, a new adaptive-robust control approach for robot manipulators is developed. The adaptiverobust control law is not only robust to unknown structured parameters but also robust to unknown unstructured parameters such as unstructured joint friction and disturbances. The bounded disturbances and unstructured model are taken into account in a dynamic model and it is assumed that the structured and unstructured parameters are unknown. The structured and unstructured parameters are distinguished between parameters and these parameters are treated separately. Next, new parameter estimation functions are developed for each of the 2 uncertainty groups. After that, the developed dynamic adaptive compensators for the unknown structured and unknown unstructured parameters are combined and the control law is formulated by the combination of the compensators, including the proportional-derivative feedforward control. Based on the Lyapunov theory, the uniform ultimate boundedness of the tracking error is obtained.
2012
In this paper, a parameter and uncertainty bound estimation functions for adaptive-robust control of robot manipulators are developed. A Lyapunov function is defined and parameters and uncertainty bound estimation functions are developed based on the Lyapunov function. Thus, stability of an uncertain system is guaranteed and uniform boundedness of the tracking error is achieved. As distinct from previous parameter and bound estimation laws, the parameters and uncertainty bounds are updated as a function of a combination of trigonometric function depending on robot parameters and tracking error. Based on the same Lyapunov function, a robust control law is also defined and the stability of the uncertain system is proved under the same set of conditions. Simulation results are given to illustrate the tracking performance of the proposed adaptive-robust controller.
Unfalsified adaptive control for manipulators with parameter uncertainties
2015 IEEE 2nd Colombian Conference on Automatic Control (CCAC), 2015
This work evaluates by simulation the performance of the Unfalsified Adaptive Control (UAC) for Multiple Degree of Freedom (MDoF) serial manipulators. The UAC is a data-driven technique that addresses stability issues of model-based controllers for robot arms with inertial uncertainties. The unfalsified controller selects the most suitable controller from a set, based on performance, to decide whether the controller in the closed loop should be changed, using only system inputs and outputs, i.e., torques and joint variables of the robotic arm, respectively. In this work, performance and robustness is evaluated by simulation on a 5-DoF manipulator showing the ability of the UAC to accomplish tracking tasks in the presence of inertial parameters disturbances.
Journal of Dynamic Systems, Measurement, and Control, 1986
This paper presents a new adaptive control scheme for mechanical manipulators. Making use of the fundamental properties of the manipulator equations, an adaptive algorithm is developed for compensating a nonlinear term in the dynamic equations and for decoupling the dynamic interaction among the joints. A computer simulation study is conducted to evaluate the performance of a manipulator control system composed of the manipulator, adaptive nonlinear compensator/decoupling controller and state feedback controller with integral action. Simulation results show that the manipulator control system with adaptive controller is insensitive to variations of manipulator configurations and payload.
Adaptive Robust Controller for Robot Manipulators: Experiments on a Puma 560 Robot
IFAC Proceedings Volumes, 2006
This work presents a new robust control scheme for articulated robotic systems. The aim of the controller is to add an adaptive scheme to the standard robust controller in order to improve its performance, especially in the case of unknown parameters or unknown variable loading. This paper presents real results for a Puma-560 robot, comparing the new strategy with the classical robust controller. They show a case in which the original controller does not exhibit a good performance, due to the presence of model uncertainties. The new strategy tries to tune automatically the uncertainty bound parameter of the robust action and results show that it has a satisfactory behaviour compared with the previous strategies.