Optimal Sampled-data Control Systems
Among the many techniques for designing linear multivariable analogue controllers, the two most popular optimal ones are H2 and H ¿ optimization. The fact that most new industrial controllers are digital provides strong motivation for adapting or extending these techniques to digital control systems. This book, now available as a corrected reprint, attempts to do so. Part I presents two indirect methods of sampled-data controller design: These approaches include approximations to a real problem, which involves an analogue plant, continuous-time performance specifications, and a sampled-data controller. Part II proposes a direct attack in the continuous-time domain, where sampled-data systems are time-varying. The findings are presented in forms that can readily be programmed in, e.g., MATLAB.
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Overview of ContinuousTime H2 and WooOptimal Control
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analog controller analog system Assume assumptions Auto bilinear transformation block diagram Chapter closed-loop compute Consider continuous-time signal continuous-time system control problem control system dash Define denote digital control digital implementation discrete-time signal discretized system eigenvalues Elliptic Filter equals Example fast discretization FDLTI feedback system filter finite follows Hilbert space Hilbert-Schmidt operator i(R+ IEEE Trans impulse response induced norm input-output internal stability Kd H Lemma lifted system linear system LTI system Lyapunov equation MATLAB matrix exponential minimize nonsingular optimal control output parametrization period h plant Proof realization Riccati equation sampled-data control sampled-data systems sampling frequency sampling period SD control shown in Figure shows SISO stabilizable stabilizing controllers standard SD system state-space step-invariant transformation step-tracking symplectic pair system G system in Figure telerobot Theorem time-invariant time-varying transfer function transfer matrix unit circle unit impulse unit step vector Woo-norm Woo-optimal zero