Simple adaptive control techniques have recently been developed for continuous stationary and non-stationary linear systems, based on the fact that the output stabilizability property of systems can be used to realize ‘almost passive’ configurations that may facilitate the implementation of simple robust adaptive controllers. This paper extends these concepts to a class of non-linear systems that are linear in the control; this includes manipulators and similar systems. Since it has recently been shown that non-linear systems that can be stabilized via static or dynamic output feedback become ‘almost passive’ via some corresponding parallel feedforward, a rigorous proof of the robust stability of the simple adaptive control of non-stationary almost passive systems is presented

Simple adaptive control for a class of non-linear systems with application to robotics

Simplified Techniques for Adaptive Control of Robotic Systems

Recently it was shown for continuous stationary and nonstationary linear systems that the conditions usually required for stability of adaptive controllers may be overly restrictive, and that basic stabilizability properties of systems may be sufficient for implementation of simple and robust nonlinear adaptive controllers. Developed initially for linear time invariant systems these concepts can be extended to nonstationary systems and to the class of nonlinear systems linear in control, which includes manipulators and other similar systems.This paper presents a tutorial survey of techniques and methods that together lead to a simple, adaptive controller (SAC) which seems to be applicable with confidence to many real-world systems.

Simple adaptive control - a survey

This paper proposes the control scheme using neural network for micro grids with isolated operation mode. Normally, the micro grid operates in interconnected mode with the medium voltage network, however when the power go down, the micro grid must have the ability to operate stably autonomously. So this paper evaluates the stability of the micro grids with isolated operation mode using the feedback error learning scheme in neural networks.

Power consumption/supply control using neural network for micro grids

A short tutorial on simple adaptive controllers, based on the stabilizability properties of the plants, is presented. Modern control systems, such as nonlinear, adaptive, or intelligent systems, use nonstationary controllers. They try to adapt their controllers to the specific conditions imposed by plant uncertainties or changing external environments so that the performance and stability of the control system are maintained. Nonstationary controllers, however, raise new problems related to eventual instability, even under the same conditions that would guarantee stability with fixed controllers. It has been shown that stabilizability properties can be used to implement simple and robust nonlinear adaptive controllers, and that the conditions usually required for stability of adaptive controllers may be overly restrictive. >

David E. Goldberg

Papers

Simple adaptive control of a robot arm