Abstract
This paper presents the adaptive controller design for brushed permanent magnet DC motor used in velocity-tracking applications based on worst-case approach. We first formulate the robust adaptive control problem as a nonlinearH∞-control problem under imperfect state measurement, and then solve it using game-theoretic approach. The controller guarantees the boundedness of closed-loop signals with bounded exogenous disturbances, and achieves desired disturbance attenuation level with respect to the unmeasured exogenous disturbance inputs and the measured disturbance inputs. The strong robustness properties are illustrated by a simulation example.
Highlights
Permanent magnet brushed DC (PMBDC) motors are widely used in real world applications, and in the highvolume commercial products, which is due to the PMBDC motors’ better cost-to-performance ratio than most other motors
This paper presents the adaptive controller design for brushed permanent magnet DC motor used in velocity-tracking applications based on worst-case approach
The classic adaptive control design based on the certainty equivalence approach leads to structurally simple adaptive controllers [1, 2], and its effectiveness for linear systems with or without stochastic disturbance inputs has been demonstrated when long-term asymptotic performance is considered [3]
Summary
Permanent magnet brushed DC (PMBDC) motors are widely used in real world applications, and in the highvolume commercial products, which is due to the PMBDC motors’ better cost-to-performance ratio than most other motors. Worst-case analysis-based robust adaptive-control design was motivated by the success of the game-theoretic approach to H∞-optimal control problems [11] in late 1990s, which addresses the disturbance attenuation property directly. In this approach, the robust adaptive control problem is formulated as a nonlinear H∞-control problem under imperfect state measurements. We study the adaptive control design for permanent magnet DC motor based on worst-case analysis approach. We formulate the robust adaptive control problem as a nonlinear H∞-control problem under imperfect state measurements, and apply the cost-to-come function analysis to derive the worst-case identifier and state estimator.
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