Adaptive Stable Control of Manipulators with Improved Adaptation Transients by Using On-line Supervision of the Free-Parameters of the Adaptation Algorithm and Sampling Rate
Journal:Informatica
Volume 8, Issue 2 (1997), pp. 289–309
Abstract
This paper addresses the application of convergence rules of gradient-type discrete algorithms to discrete adaptive control algorithms for linear time-invariant systems, which are based on Lyapunov's – like functions, in order to improve the transient performances based on fast adaptation. In particular, the adaptation covergence is increased as a generalized or filtered error increases through the application of Armijo rule for regulating the decrease of each Lyapunov's-like function on which the particular adaptation algorithm is based. The proposed scheme can be implemented with minor modifications in systems subject to unmodelled dynamics if some weak knowledge on such a dynamics is available consisting of upper-bounds of the dimension and norm of the unmodelled parameter vector.
Journal:Informatica
Volume 7, Issue 4 (1996), pp. 431–454
Abstract
Adaptive Control Distributed Parameter Systems (ACDPS) with adaptive learning algorithms based on orthogonal neural network methodology are presented in this paper. We discuss a modification of orthogonal least squares learning to find appropriate efficient algorithms for solution of ACDPS problems. A two times problem linked with the real time of plant control dynamic processes and the learning time for adjustment of parameters in adaptive control of unknown distributed systems is discussed.
The simulation results demonstrate that the orthogonal learning algorithms on a neural network concept allow to find perfectly tracked output control distributed parameters in ACDPS and have rather a good perspective in the development of generalised ACDSP theory and practice in the future.
Journal:Informatica
Volume 7, Issue 1 (1996), pp. 39–82
Abstract
This paper presents a direct adaptive, control algorithm, based on a σ-modification rule, which is robust respect to additive and multiplicative plant unmodelled dynamics for plants involving both internal (i.e., in the state) and external (i.e., in the output or input) known point delays. Several adaptive controller structures are given and analyzed for the case of plants with unknown parameters while being assumed that the nominal plant is of known order and relative order. The parametrized parts of two of the controller structures involve delays while those of the two remaining controllers are delay-free. However, auxiliary compensating signals which weight the plant input and output integrals are incorporated in all the controller structures for stabilization purposes. It is proved that, if the unmodelled dynamics is sufficiently small at low frequencies, then the adaptive algorithm guarantees boundedness of all the signals in the closed-loop system.