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In this paper, an adaptive learning control law is proposed to address complex uncertain systems with nonlinear parameterization. Specifically, the controller consists of: (i) a feedback type term, (ii) an adaptive mechanism for the unknown system parameters, and (iii) a learning-based technique to estimate the unknown periodic functions. As proven by a Lyapunov-based stability analysis, the designed...
In this paper, we consider the regulation control problem for an underactuated overhead crane system. Motivated by recent passivity-based controllers for underactuated systems, we design several controllers that asymptotically regulate the planar gantry position and the payload angle. Specifically, utilizing LaSalle's invariant set theorem, we first illustrate how a simple proportional-derivative...
In this paper, a position regulation control strategy is developed for a magnetic levitation system operating in the presence of a bounded, nonlinear, periodic disturbance. The proposed controller utilizes a saturated control force input in conjunction with a learning based disturbance estimator to asymptotically regulate the target mass to a desired set point position despite. the actuator's unidirectional...
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