True digital control: A unified design procedure for linear sampled data control systems

The paper describes the True Digital Control (TDC) design philosophy for linear, single input, single output (SISO) systems described by the backward shift (z−l) and delta (δ) operator transfer function model; and outlines a Computer Aided Control System Design (CACSD) procedure based upon this design philosophy. The control system design analysis used in the CACSD procedure is based on the definition of suitable Non-Minimum State Space (NMSS) forms for the z−l and δ models, which allow for state variable feedback (SVF) control involving only the measured input and output variables, together with their stored past values. The resulting “Proportional-Integral-Plus” (PIP) control systems then provide either SVF pole assignment control or optimal LQG control without resort to the complexity of state reconstructor (observer) design. The paper outlines the major stages in the TDC design: from model identification and parameter estimation; through PIP control system design and the evaluation of these designs in the presence of uncertainty, to their implementation in fixed gain, self-tuning of self-adaptive form.

[1]  Peter C. Young Self-adaptive Kalman filter , 1979 .

[2]  David M. Auslander,et al.  Control and dynamic systems , 1970 .

[3]  Peter C. Young,et al.  Direct digital and adaptive control by input-output state variable feedback pole assignment , 1987 .

[4]  Graham C. Goodwin,et al.  A unified approach to adaptive control , 1988 .

[5]  Anthony J. Jakeman,et al.  Joint parameter/state estimation , 1979 .

[6]  Peter C. Young,et al.  Identification and system parameter estimation , 1985 .

[7]  J. P. Norton,et al.  An Introduction to Identification , 1986 .

[8]  Peter C. Young,et al.  Self-tuning and self-adaptive PIP control systems , 1988 .

[9]  Peter C. Young Process Parameter Estimation and Self Adaptive Control , 1966 .

[10]  J. A. Clark,et al.  Computer Applications in Agricultural Environments , 1987 .

[11]  Peter C. Young,et al.  The determination of the parameters of a dynamic process , 1965 .

[12]  Peter C. Young,et al.  The Instrumental Variable Method: A Practical Approach to Identification and System Parameter Estimation , 1985 .

[13]  P. Young,et al.  Refined instrumental variable methods of recursive time-series analysis Part I. Single input, single output systems , 1979 .

[14]  Peter C. Young,et al.  Self-adaptive design of a nonlinear temperature control system , 1991 .

[15]  Peter Young A second generation adaptive autostabilization system for airborne vehicles , 1981, Autom..

[16]  Lennart Ljung,et al.  System identification toolbox for use with MATLAB , 1988 .

[17]  P. H. Hammond Theory of Self-Adaptive Control Systems , 1966 .

[18]  T. Söderström,et al.  Instrumental variable methods for system identification , 1983 .

[19]  P. Young Some observations on instrumental variable methods of time-series analysis , 1976 .

[20]  Peter C. Young,et al.  Recursive Estimation and Time Series Analysis , 1984 .

[21]  P. Young,et al.  Refined instrumental variable methods of recursive time-series analysis Part III. Extensions , 1980 .

[22]  P. Young,et al.  An approach to the linear multivariable servomechanism problem. , 1972 .

[23]  Per-Olof Gutman,et al.  A Comparison between Robust and Adaptive Control of Uncertain Systems , 1987 .

[24]  Peter C. Young,et al.  Recursive Estimation, Forecasting, and Adaptive Control , 1989 .

[25]  Peter Young,et al.  Parameter estimation for continuous-time models - A survey , 1979, Autom..

[26]  M. Kendall,et al.  The advanced theory of statistics , 1945 .

[27]  Peter C. Young,et al.  2 – THE MODELLING AND CONTROL OF NUTRIENT FILM SYSTEMS , 1987 .

[28]  Kevin Warwick,et al.  Implementation of self tuning controllers , 1988 .