Browsing by Subject "Adaptive control systems"
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Item Adaptive fuzzy nonlinear internal model control strategy(Texas Tech University, 1996-12) Kreesuradej, WorapojProportional-Integratal Derivative like Fuzzy Logic Controllers (PID-FLCs), have been used for a variety of nonlinear control problems. Basically, a PID-FLC contains a control algorithm in the form of linguistic fuzzy rules. The problem with PID-FLCs is that there is no systematic design for developing fuzzy rules. It is also difficult to develop the controllers to meet specific requirements on control performances. In this dissertation, a nonlinear internal model control (NIMC) structure and an adaptive fuzzy NIMC strategy have been proposed to overcome the problems of PIDFLCs. One of the attractive features of the NIMC structure is that the relations between some designed parameters and the performance of the control system can be found explicitly. Thus, this control structure allows designers to systematically construct the fuzzy control. An adaptive fuzzy NIMC strategy has been proposed. The proposed strategy has two attractive features. First, the strategy provides an on-line adaptation to improve control performance and to keep the closed-loop system stable. Second, a fuzzy basis function (FBF) expansion is used to implement the controller. The use of the FBF expansion enhances the ability of the strategy to control practical nonUnear systems whose exact mathematical models are difficult to obtain. Finally, Simulation studies of controlling four nonlinear systems (e.g., a pendulum, an inverted pendulum, a forced Van der Pol equation, and a two-link cylindrical robot manipulator) have been conducted. The simulation results show that the proposed strategy has successfully controlled the four nonlinear systems.Item Adaptive training: a methodology for studying the attentional deficit of learning disabled children.(Texas Tech University, 1975-08) Hopson, Julie ANot availableItem Communication in distributed control telerobotics environments(Texas Tech University, 1998-08) Fu, GengNot availableItem Control of a nonlinear multivariable system with adaptive critic designs(Texas Tech University, 1997-05) Visnevski, Nikita A.A family of Adaptive Critic Designs (ACD) was proposed by Werbos (1992) as a new optimization technique combining together concepts of reinforcement learning and backpropagation. The goal of each design is to find an approximation of the cost-to-go function from the Bellman equation of dynamic programming or some function related to it, and then find the optimal solution of the problem by applying a reinforcement learning technique. In ACD we have two networks called critic and action (a substitute name for "controller" in the ACD literature), the action network trying to minimize an approximation of the cost-to-go. There are three basic implementations of ACD called Heuristic Dynamic Programming (HDP), Dual Heuristic Programming (DHP), and Globalized Dual Heuristic Programming (GDHP) (Prokhorov & Wunsch, to appear).Item Hardware requirements for fuzzy logic control systems(Texas Tech University, 1996-12) Workman, Mark E.The FUzzy Design GEnerator (FUDGE) is an inexpensive, but surprisingly powerful, fuzzy logic design tool. It can be used to develop, test and implement fuzzy logic controllers in a wide variety of applications. So, it is the purpose of this thesis to evaluate and improve this fuzzy logic design tool. This thesis also discusses several topics related to FUDGE that are either hard to find or have not been thoroughly documented by Motorola. Chapter I gives a overall introduction to goals and ambitions of this thesis, which include the development of some hardware requirement models for fuzzy logic control systems developed with the FUDGE environment. Plus, the development of a C++ translation program. This translation program provides object-oriented, C++, support for the FUDGE tool. Chapter II provides a basic overview of fuzzy logic. It begins by discussing the past historical developments of fuzzy logic systems. Then it covers some of the current attitudes and misconceptions about using fuzzy logic in control system applications. This is followed by a primer on the goals and benefits of implementing control systems with fuzzy logic. Included in this discussion is the implementation of fuzzy logic systems with Binary Input-Output Fuzzy Associative Memories (BIOFAMs) and rule inference with the Max-Min composition relation. For a more in depth study of theoretical fuzzy logic design, the reader is referred to such excellent text books as Bart Kosko's, Neural Networks and Fuzzy Systems. Chapters III and IV describe the goals, expectations and results of this research and can be best described in two major topics. The first topic is the development of hardware models for fuzzy logic control systems implemented with the FUDGE software. These models can be used to predict the memory and processing power requirements needed to implement a proposed fuzzy logic design. The second portion relates to increasing the number of high level languages that are supported by the FUDGE tool. Since FUDGE is both a design and implementation tool, it can create the output code necessary to implement a fuzzy logic design in several forms of microprocessor. The current version of FUDGE (Version 1.02) supports several of Motorola's assembly languages, as well as the ANSI C language. In this second topic, a fuzzy logic translation program is also described. This program translates the source code for a C based fuzzy engine (produced by FUDGE) into a functionally equivalent C++ based fuzzy engine object. This allows a designer to implement a fuzzy logic design in the high level languages of C or C++. Chapter V contains a summary of the work done in this thesis. It reviews the hardware models for memory allocation and processor execution delays, followed by an overview of the XFUDGE translation software and its contribution to the Fuzzy Design Generator.Item Noncertainty equivalent nonlinear adaptive control and its applications to mechanical and aerospace systems(2007) Seo, Dong Eun, 1973-; Akella, Maruthi Ram, 1972-Adaptive control has long focused on establishing stable adaptive control methods for various nonlinear systems. Existing methods are mostly based on the certainty equivalence principle which states that the controller structure developed in the deterministic case (without uncertain system parameters) can be used for controlling the uncertain system along by adopting a carefully determined parameter estimator. Thus, the overall performance of the regulating/tracking control depends on the performance of the parameter estimator, which often results in the poor closed-loop performance compared with the deterministic control because the parameter estimate can exhibit wide variations compared to their true values in general. In this dissertation we introduce a new adaptive control method for nonlinear systems where unknown parameters are estimated to within an attracting manifold and the proposed control method always asymptotically recovers the closed-loop error dynamics of the deterministic case control system. Thus, the overall performance of this new adaptive control method is comparable to that of the deterministic control method, something that is usually impossible to obtain with the certainty equivalent control method. We apply the noncertainty equivalent adaptive control to study application arising in the n degree of freedom (DOF) robot control problem and spacecraft attitude control. Especially, in the context of the spacecraft attitude control problem, we developed a new attitude observer that also utilizes an attracting manifold, while ensuring that the estimated attitude matrix confirms at all instants to the special group of rotation matrices SO(3). As a result, we demonstrate for the first time a separation property of the nonlinear attitude control problem in terms of the observer/controller based closed-loop system. For both the robotic and spacecraft attitude control problems, detailed derivations for the controller design and accompanying stability proofs are shown. The attitude estimator construction and its stability proof are presented separately. Numerical simulations are extensively performed to highlight closed-loop performance improvement vis-a-vis adaptive control design obtained through classical certainty equivalence based approaches.Item Recurrent neural networks for time series prediction(Texas Tech University, 1996-08) Tanyous, Ebtesam ShenoudaNot availableItem Task adaptation: an attempt to modify a time dependent attention deficit(Texas Tech University, 1977-05) Bohn, Carole A.NOT AVAILABLE