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Adaptive actuator compensation control for multivariable nonlinear systems

Gang Tao, Xiaoli Ma

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Abstract

Adaptive control schemes are developed for multivariable nonlinear systems with input actuator nonlinearities. Such a controller contains an adaptive inverse to compensate the actuator nonlinearity, a nonlinear feedback to linearize the nonlinear dynamics, and a linear feedback to stabilize the linearized system. For nonlinear systems which have no relative degree, dynamic extension is employed to realize adaptive inverse compensation designs for actuator nonlinearities. These adaptive designs ensure closed-loop stability in the presence of uncertain actuator nonlinearities, and, as shown by simulation results, improve system tracking performance.

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Adaptive control schemes are developed for multivariable nonlinear systems with input actuator nonlinearities. Such a controller contains an adaptive inverse to compensate the actuator nonlinearity, a nonlinear feedback to linearize the nonlinear dynamics, and a linear feedback to stabilize the linearized system. For nonlinear systems which have no relative degree, dynamic extension is employed to realize adaptive inverse compensation designs for actuator nonlinearities. These adaptive designs ensure closed-loop stability in the presence of uncertain actuator nonlinearities, and, as shown by simulation results, improve system tracking performance.

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Available abstract

Adaptive control schemes are developed for multivariable nonlinear systems with input actuator nonlinearities. Such a controller contains an adaptive inverse to compensate the actuator nonlinearity, a nonlinear feedback to linearize the nonlinear dynamics, and a linear feedback to stabilize the linearized system. For nonlinear systems which have no relative degree, dynamic extension is employed to realize adaptive inverse compensation designs for actuator nonlinearities. These adaptive designs ensure closed-loop stability in the presence of uncertain actuator nonlinearities, and, as shown by simulation results, improve system tracking performance.

Key concepts: Control theory (sociology), Actuator, Nonlinear system, Multivariable calculus, Compensation (psychology), Adaptive control, Plant, Controller (irrigation)

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