Control
javad keighobadi; Mohammad mehdi Fateh
Abstract
Recent research on the backstepping control of robotic systems has motivated us to design a robust backstepping voltage-based controller with computational simplicity and ease of implementation. In this paper, an adaptive robust tracking controller based on backstepping method (ARTB) is presented for ...
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Recent research on the backstepping control of robotic systems has motivated us to design a robust backstepping voltage-based controller with computational simplicity and ease of implementation. In this paper, an adaptive robust tracking controller based on backstepping method (ARTB) is presented for uncertain electrically-driven robotic manipulators in the framework of voltage control strategy. It is intended to convert robot control problem to motor control problem. In the design procedure, the manipulator dynamics are incorporated into a lumped uncertainty, such that the proposed adaptation law promptly compensates for it. Hence, high tracking accuracy, robust behavior and less complexity are the prominent features of the proposed control system in the presence of external disturbances, parametric uncertainties and un-modeled dynamics. Moreover, the control approach is useful for high-speed tracking purposes. The stability of the closed-loop system is guaranteed based on the Lyapunov theory and the tracking error converges to zero asymptotically. As a case study, the proposed ARTB is simulated on a two-link robot manipulator driven by permanent magnet DC motors. Numerical simulations are included to show the superiority of the proposed controller to a state augmented adaptive backstepping method, a sliding backstepping controller and an adaptive backstepping sliding mode control in tracking the desired trajectory.
Control
Mohammad Reza Shokoohinia; Mohammad Mehdi Fateh
Abstract
In this paper, a novel model-free control scheme is developed to enhance the tracking performance of robotic systems based on an adaptive dynamic sliding mode control and voltage control strategy. In the voltage control strategy, actuator dynamics have not been excluded. In other words, instead of the ...
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In this paper, a novel model-free control scheme is developed to enhance the tracking performance of robotic systems based on an adaptive dynamic sliding mode control and voltage control strategy. In the voltage control strategy, actuator dynamics have not been excluded. In other words, instead of the applied torques to the robot joints, motor voltages are computed by the control law. First, a dynamic sliding mode control is designed for the robotic system. Then, to enhance the tracking performance of the system, an adaptive mechanism is developed and integrated with the dynamic sliding mode control. Since the lumped uncertainty is unknown in practical applications, the uncertainty upper bound is necessary in the design of the dynamic sliding mode controller. Hence, the lumped uncertainty is estimated by an adaptive law. The stability of the closed-loop system is proved based on the Lyapunov stability theorem. The simulation results demonstrate the superior performance of the proposed adaptive dynamic sliding mode control strategy.
Control
Mohsen Jalaeian-F; Mohammad Mehdi Fateh; Morteza Rahimiyan
Abstract
This paper presents a novel optimal impedance voltage-controller for Electrically Driven Lower Limb Rehabilitation Robots (EDLR). To overcome the dynamical complexities, and handle the uncertainties, the proposed method employs an expected forward model of the actuator. The difference between this model’s ...
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This paper presents a novel optimal impedance voltage-controller for Electrically Driven Lower Limb Rehabilitation Robots (EDLR). To overcome the dynamical complexities, and handle the uncertainties, the proposed method employs an expected forward model of the actuator. The difference between this model’s output and the actual output represents the existing value of lamped uncertainty. A voltage-controller is designed based on this uncertainty estimator, which compensate for the uncertainties. Parameters of the controller have been optimized using genetic algorithms. Key contributions of this paper are I) estimation of the uncertainty by the expected model’s output, II) overcoming the changes in motor parameters, III) introducing a class of closed-loop system termed as “Repeatable”, and IV) designing an optimal impedance voltage-controller that is non-sensitive to the parameter variations. Significant merits of the approach are swift calculations, efficiency, robustness, and guaranteed stability. Furthermore, the simplicity of design, ease of implementation and model-free independent joint structure of the approach are noticeable. The method is compared with an adaptive robust sub-controller and a Taylor-series-based adaptive robust controller, through simulations in passive range of motion and active assistive rehabilitation exercises. The results show the superiority of the proposed method in tracking performance and the time of calculations.
