This study proposes an adaptiveapproximation-basedcontrol approach for non-linear pure-feedback systems in the presence of full state constraints. Completely non-affine non-linear functions are considered and assume...
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This study proposes an adaptiveapproximation-basedcontrol approach for non-linear pure-feedback systems in the presence of full state constraints. Completely non-affine non-linear functions are considered and assumed to be unknown. The dynamic surface design based on integral barrier Lyapunov functionals is provided to achieve both the desired tracking performance and the constraints satisfaction, in consideration of the full-state-constrained non-affine non-linearities. In this design procedure, simple sufficient conditions for choosing control gains, which can be checked off-line, are established to guarantee the feasibility of the controller. The function approximation technique is employed to estimate unknown non-linearities induced from the controller design procedure where the adaptive laws using the projection operator are designed to ensure the boundedness of the function approximators in the feasibility conditions. It is shown that all the signals in the closed-loop system are uniformly ultimately bounded and the tracking error converges to an adjustable neighbourhood of the origin while all state variables always remain in the constrained state space.
This paper examines an adaptivecontrol scheme for tubular linear motors with micro-metric positioning tolerances. Uncertainties such as friction and other electro-magnetic phenomena are approximated with a radial bas...
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This paper examines an adaptivecontrol scheme for tubular linear motors with micro-metric positioning tolerances. Uncertainties such as friction and other electro-magnetic phenomena are approximated with a radial basis function neural network, which is trained online using a learning law based on Lyapunov design. Differently from related literature, the approximator is trained using a composite adaptation law combining the tracking error and the model prediction error. Stability analysis and bounds for both errors are established, and an extensive experimental investigation is performed to assess the practical advantages of the proposed scheme. (C) 2010 Elsevier Ltd. All rights reserved.
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