An on-line equivalent system identification scheme for adaptive control

A prime obstacle to the widespread use of adaptive control is the degradation of performance and possible instability resulting from the presence of unmodeled dynamics. The approach taken is to explicitly include the unstructured model uncertainty in the output error identification algorithm. The order of the compensator is successively increased by including identified modes. During this model building stage, heuristic rules are used to test for convergence prior to designing compensators. Additionally, the recursive identification algorithm as extended to multi-input, multi-output systems. Enhancements were also made to reduce the computational burden of an algorithm for obtaining minimal state space realizations from the inexact, multivariate transfer functions which result from the identification process. A number of potential adaptive control applications for this approach are illustrated using computer simulations. Results indicated that when speed of adaptation and plant stability are not critical, the proposed schemes converge to enhance system performance

The application of genetic algorithms to the adaptation of IIR filters

The adaptation of an IIR filter is a very difficult problem due to its non-quadratic performance surface and potential instability. Conventional adaptive IIR algorithms suffer from potential instability problems and a high cost for stability monitoring. Therefore, there is much interest in adaptive IIR filters based on alternative algorithms. Genetic algorithms are a family of search algorithms based on natural selection and genetics. They have been successfully used in many different areas. Genetic algorithms applied to the adaptation of IIR filtering problems are studied in this thesis, and show that the genetic algorithm approach has a number of advantages over conventional gradient algorithms, particularly, for the adaptation of high order adaptive IIR filters, IIR filters with poles close to the unit circle and IIR filters with multi-modal error surfaces. The conventional gradient algorithms have difficulty solving these problems. Coefficient results are presented for various orders of IIR filters in this thesis. In the computer simulations presented in this thesis, the direct, cascade, parallel and lattice form IIR filter structures have been used and compared. The lattice form IIR filter structure shows its superiority over the cascade and parallel form IIR filter structures in terms of its mean square error convergence performance

Model reference adaptive control of a two axes hydraulic manipulator

Dissertation submitted for obtain the degree of Doctor, at the University of Bat

Learning algorithms for adaptive digital filtering

In this thesis, we consider the problem of parameter optimisation in adaptive digital filtering. Adaptive digital filtering can be accomplished using both Finite Impulse Response (FIR) filters and Infinite Impulse Response Filters (IIR) filters. Adaptive FIR filtering algorithms are well established. However, the potential computational advantages of IIR filters has led to an increase in research on adaptive IIR filtering algorithms. These algorithms are studied in detail in this thesis and the limitations of current adaptive IIR filtering algorithms are identified. New approaches to adaptive IIR filtering using intelligent learning algorithms are proposed. These include Stochastic Learning Automata, Evolutionary Algorithms and Annealing Algorithms. Each of these techniques are used for the filtering problem and simulation results are presented showing the performance of the algorithms for adaptive IIR filtering. The relative merits and demerits of the different schemes are discussed. Two practical applications of adaptive IIR filtering are simulated and results of using the new adaptive strategies are presented. Other than the new approaches used, two new hybrid schemes are proposed based on concepts from genetic algorithms and annealing. It is shown with the help of simulation studies, that these hybrid schemes provide a superior performance to the exclusive use of any one scheme

Statistical Physics of Evolutionary Trajectories on Fitness Landscapes

Random walks on multidimensional nonlinear landscapes are of interest in many areas of science and engineering. In particular, properties of adaptive trajectories on fitness landscapes determine population fates and thus play a central role in evolutionary theory. The topography of fitness landscapes and its effect on evolutionary dynamics have been extensively studied in the literature. We will survey the current research knowledge in this field, focusing on a recently developed systematic approach to characterizing path lengths, mean first-passage times, and other statistics of the path ensemble. This approach, based on general techniques from statistical physics, is applicable to landscapes of arbitrary complexity and structure. It is especially well-suited to quantifying the diversity of stochastic trajectories and repeatability of evolutionary events. We demonstrate this methodology using a biophysical model of protein evolution that describes how proteins maintain stability while evolving new functions

Dynamic modeling and adaptive control for space stations

Of all large space structural systems, space stations present a unique challenge and requirement to advanced control technology. Their operations require control system stability over an extremely broad range of parameter changes and high level of disturbances. During shuttle docking the system mass may suddenly increase by more than 100% and during station assembly the mass may vary even more drastically. These coupled with the inherent dynamic model uncertainties associated with large space structural systems require highly sophisticated control systems that can grow as the stations evolve and cope with the uncertainties and time-varying elements to maintain the stability and pointing of the space stations. The aspects of space station operational properties are first examined, including configurations, dynamic models, shuttle docking contact dynamics, solar panel interaction, and load reduction to yield a set of system models and conditions. A model reference adaptive control algorithm along with the inner-loop plant augmentation design for controlling the space stations under severe operational conditions of shuttle docking, excessive model parameter errors, and model truncation are then investigated. The instability problem caused by the zero-frequency rigid body modes and a proposed solution using plant augmentation are addressed. Two sets of sufficient conditions which guarantee the globablly asymptotic stability for the space station systems are obtained

Development and applications of adaptive IIR and subband filters

Adaptive infinite impulse response (IIR) filter is a challenging research area. Identifiers and Equalizers are among the most essential digital signal processing devices for digital communication systems. In this study, we consider IIR channel both for system identification and channel equalization purposes. We focus on four different approaches: Least Mean Square (LMS), Recursive Least Square (RLS), Genetic Algorithm (GA) and Subband Adaptive Filter (SAF). ). The performance of conventional LMS and RLS based IIR system identification and channel equalization are found with the help of computer simulations. And also the convergence speed and the ability to locate the global optimum solution using a population based algorithm named Genetic Algorithm is given

Time Varying Compensator Design for Reconfigurable Structures Using Non-Collocated Feedback

Analysis and synthesis tools are developed to improved the dynamic performance of reconfigurable nonminimum phase, nonstrictly positive real-time variant systems. A novel Spline Varying Optimal (SVO) controller is developed for the kinematic nonlinear system. There are several advantages to using the SVO controller, in which the spline function approximates the system model, observer, and controller gain. They are: The spline function approximation is simply connected, thus the SVO controller is more continuous than traditional gain scheduled controllers when implemented on a time varying plant; ft is easier for real-time implementations in storage and computational effort; where system identification is required, the spline function requires fewer experiments, namely four experiments; and initial startup estimator transients are eliminated. The SVO compensator was evaluated on a high fidelity simulation of the Shuttle Remote Manipulator System. The SVO controller demonstrated significant improvement over the present arm performance: (1) Damping level was improved by a factor of 3; and (2) Peak joint torque was reduced by a factor of 2 following Shuttle thruster firings