Abstract
In direct adaptive inverse control (DAIC), parameters of the controller are estimated directly in the feed-forward loop. In this paper, we propose a closed loop direct adaptive inverse control (CDAIC) scheme which improves tracking, error convergence, and disturbance rejection properties of DAIC. CDAIC is applicable to stable or stabilized, minimum or nonminimum phase linear plants. CDAIC and DAIC are compared using computer simulations for disturbance free and disturbed discrete type nonminimum phase linear plants. CDAIC shows better results compared to DAIC in terms of mean square tracking error and disturbance rejection.
Highlights
Adaptive control over the last five decades has emerged as one of the well-established discipline; see Astrom and Wittenmark [1], Gang and Rogelio [2], and Sastry and Bodson [3]
We propose a closed loop direct adaptive inverse control technique based on normalized least mean square (NLMS) for controlling linear plants
A closed loop direct controller based on NLMS for adaptive tracking of stable plants is proposed
Summary
Adaptive control over the last five decades has emerged as one of the well-established discipline; see Astrom and Wittenmark [1], Gang and Rogelio [2], and Sastry and Bodson [3]. Numerous techniques have been developed for control of minimum and nonminimum phase plants; refer to Widrow and Walach [4], Widrow and Bilello [5], Widrow and Plett [6], Plett [7], Shafiq [8], M. In DAIC schemes inverse is designed based on identified plant; see Plett [7] and Shafiq et al [9]. Adaptive inverse control of linear and nonlinear systems using dynamic neural networks is presented in Plett [7]. Direct and indirect model based control for nonlinear single input single output (SISO) plant using artificial neural networks are discussed in Wang and Chen [16].
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