Abstracz- An indirect robust adaptive controller designed for linear time-invariant plants may also meet its control objective approximately for slowlq time-varqing plants. The plant parameter variations act as a disturbance in the estimation and control parts of the indirect scheme, and therefore, for stability. they are required to be slow. In this paper, we develop new structures for the control and estimation parts of the indirect adaptive scheme which are more appropriate for time-varying plants. In contrast to the usual pointwise schemes, the new controller structure can meet the control objective exactly for a wide class of plants with smooth, but otherwise unrestricted, parameter variations. On the other hand, the possiblq available knowledge of the form or frequency of variation of the “fsst” parameters is included in the new estimator structure so that for successful estimation, the overall plant is not restricted to varq slowl) with time. The new estimator and control law are combined using the certainty equivalence principle to develop an indirect adaptive control scheme meeting the control objective for plants with time-varying parameters whose fast varying parts are of known form and the unknown parts are slow in the mean. 1. IN I RODVCTION N most of the existing general studies of the adaptive control I problem of time-varying (TV) plants, it is customary to consider the parameter variations as either slow’ or small, but completely unknown. This direction was based on the intuitive idea that a robust adaptive controller for linear time-invariant (LTI) plants should also perform well in the face of slow time variations of the plant parameters. In other words, the adaptive control of linear time-varying (LTV) plants was first conceived as a robustness problem with respect to the assumption of the plant time invariance. Using the recently developed robust adaptive laws, it was shown [ 11-[4] that adaptive controllers can successfully operate in slowly TV environments without requiring any persistence of excitation (PE) conditions. Moreover, in a few other studies [5]-[SI, specific models of parameter variations were considered, mainly for analytic simplicity. The main reasons for considering slowly TV plants in adaptive control can be traced back to the limitations of the adaptive and the control laws. The standard adaptive laws (parameter estimators) are essentially integrators with a finite gain in all frequencies except zero. That is, only constant parameters can be identified with zero identification error, while in the LTV case, small errors can be obtained, provided that the parameters are slowly TV. On the other hand, the control laws used in the adaptive literature are derived based on the assumption that the plant is LTI. In general, when these control
[1]
Fujio Ohkawa,et al.
A model reference adaptive control system for a class of discrete linear time-varying systems with time delay
,
1985
.
[2]
Robin J. Evans,et al.
Discrete-time adaptive control for deterministic time-varying systems
,
1984,
Autom..
[3]
P. Hartman.
Ordinary Differential Equations
,
1965
.
[4]
Petros A. Ioannou,et al.
Adaptive control of linear time-varying plants: a new model reference controller structure
,
1989
.
[5]
F. Giri,et al.
Pole placement direct adaptive control for time-varying ill-modeled plants
,
1990
.
[6]
Iven M. Y. Mareels,et al.
Persistency of excitation criteria for linear, multivariable, time-varying systems
,
1988,
Math. Control. Signals Syst..
[7]
B. Anderson.
Exponential stability of linear equations arising in adaptive identification
,
1977
.
[8]
C. Desoer,et al.
Feedback Systems: Input-Output Properties
,
1975
.
[9]
G. Kreisselmeier.
Adaptive control of a class of slowly time-varying plants
,
1986
.
[10]
J. Martín-Sánchez.
Adaptive Control for Time-Variant Processes
,
1985
.
[11]
G. Goodwin,et al.
Stochastic adaptive control for exponentially convergent time-varying systems
,
1984
.
[12]
Petros A. Ioannou,et al.
Adaptive control of linear time-varying plants
,
1987,
Autom..
[13]
A. Ilchmann,et al.
Time-varying polynomial matrix systems
,
1984
.