Thesis (M.S.)--Brigham Young University. Dept. of Electrical and Computer Engineering, 2006. / Includes bibliographical references (p. 139-140).

Thesis (Ph.D.)--North Carolina State University, 2005. / Includes vita. Includes bibliographical references (p. 109-113). Also available online via the North Carolina State University Libraries website (http://www.lib.ncsu.edu/).

An adaptive decentralized nonlinear controller for a robot manipulator is presented in this thesis. Based on the adaptive control schemes designed by Seraji [18], Dai [30], and Jimenez [31], we redesigned and further simplified the control algorithm and, as a consequence, we achieved better path tracking performance. The proposed adaptive controller is made of a PD feedback controller which has time varying gains, a feedforward compensator based on the idea of inverse dynamics, and an auxiliary signal. Due to its adaptive structure, the controller shows robustness against disturbances and unmodeled dynamics. In order to ensure asymptotic tracking we select a Lyapunov function such that the controller forces the negative definiteness of the time derivative of such a Lyapunov function. To do this, the tracking position and velocity error are penalized and used as a part of the adaptive control gain. The main advantages of this scheme are the comparably faster convergence of tracking error, relatively simpler structure, and smoother control activity. This controller only requires the position and angular speed measurement, it does not require any knowledge about the mathematical model of the robot manipulator. Simulation shows the capacity of this controller and its robustness against disturbances. / Graduation date: 1993

The promise and potential of controllers that can reconfigure themselves in the case of control effector failures and uncertainties, and yet guarantee stability and provide satisfactory performance, has led to fault tolerant control being an active area of research. This thesis addresses this issue with the design of two fault tolerant nonlinear Structured Adaptive Model Inversion control schemes for systems with fixed magnitude discrete controls. Both methods can be used for proportional as well as discrete controls. However, discrete controls constitute a different class of problems than proportional controls as they can take only binary values, unlike proportional controls which can take many values. Two nonlinear control laws based on Structured Adaptive Model Inversion are developed to tackle the problem of control failure in the presence of plant and operating environment uncertainties. For the case of redundant actuators, these control laws can provide a unique solution. Stability proofs for both methods are derived and are presented in this thesis. Fault Tolerant Structured Adaptive Model Inversion that has already been developed for proportional controls is extended here to discrete controls using pulse width modulation. A second approach developed in this thesis is Fault Tolerant Control Allocation. Discrete control allocation coupled with adaptive control has not been addressed in the literature to date, so Fault Tolerant Control Allocation for discrete controls is integrated with SAMI to produce a system which not only handles discrete control failures, but also accounts for uncertainties in the plant and in the operating environment. Fault tolerant performance of both controllers is evaluated with non real-time nonlinear simulation for a complete Mars entry trajectory tracking scenario, using various combinations of control effector failures. If a fault is detected in the control effectors, the fault tolerant control schemes reconfigure the controls and minimize the impact of control failures or damage on trajectory tracking. The controller tracks the desired trajectory from entry interface to parachute deployment, and has an adaptation mechanism that reduces tracking errors in the presence of uncertainties in environment properties such as atmospheric density, and in vehicle properties such as aerodynamic coefficients and inertia. Results presented in the thesis demonstrate that both control schemes are capable of tracking pre-defined trajectories in the presence of control failures, and uncertainties in system and operating environment parameters, but with different levels of control effort.

The new vision for space exploration will focus on sending humans to the moon and eventually to Mars. This endeavor presents new challenges that are critically different from the past experience with robotic missions to Mars. For example, the strict landing accuracy requirements for a manned space vehicle make it necessary to fly a controlled entry trajectory rather than a more robust ballistic entry trajectory used for some robotic missions. The large variations in Mars atmospheric properties make a controlled entry and a safe precision landing for manned missions a difficult engineering problem. Model reference adaptive control is a candidate solution for the Mars entry control problem. This type of controller has an adaptation mechanism that reduces tracking errors in the presence of uncertain parameters such as atmospheric density or vehicle properties. This thesis develops two different adaptive control systems for the Mars ellipsled, a vehicle which is much larger than those that carried robotic payloads to Mars in the past. A sample mission will have multiple ellipsleds arriving at Mars carrying an assortment of payloads. It is of critical importance that the vehicles land in close proximity to each other to best assure that the crew has manageable access to their payloads. The scope of this research encompasses the atmospheric flight of the ellipsled, starting at the entry interface point through the final parachute deployment. Tracking performance of an adaptive controller for prescribed entry trajectories in the pres¬ence of atmospheric and vehicle model uncertainties is shown here. Both adaptive controllers studied in this thesis demonstrate successful adaptation to uncertainties in the Martian atmosphere as well as errors in the vehicle properties. Based on these results, adaptive control is a potential option for controlling Mars entry vehicles.

Changes in the characteristics of the structure, such as damage, have not been considered in most of the active and semi-active control methods that have been used to control and optimize the response of civil engineering structures. In this dissertation, a direct adaptive control which can deal with the existence of measurement errors and changes in structural characteristics or load conditioning is used to control the performance of structures. A Simple Adaptive Control Method (SACM) is modified to control civil structures and improve their performance. The effectiveness of the SACM is verified by several numerical examples. The SACM is used to reduce the structural response such as drift and acceleration using active and semi-active devices, and its performance is compared with that of other control methods. Also, a probabilistic indirect adaptive control method is developed and its behavior is compared to the SACM using a simple numerical example. In addition to the simplicity of the SACM implementation, the results show that SACM is very effective to reduce the response of structures with linear and non-linear behavior in comparison with other control methods.

ABSTRACT In this dissertation, several adaptive control design schemes for a class of nonlinear systems are proposed. The first topic of the research is concerned with self-tuning PID controller design. The main problem of designing PID controller is how to determine the values of three control gains, i.e., proportional gain , integral gain , and derivative gain . We attempt to use the technique of adaptive control based on the Lyapunov approach to design the PID controller for some class of partially known nonlinear systems. Three PID control gains are adjusted on-line such that better output performance can be achieved. The stability of the closed-loop PID control systems is analyzed and guaranteed by introducing a supervisory control and a modified adaptation law with projection. Second, two kinds of adaptive neural control systems including the direct and indirect neural controls are considered by using simple single auto-tuning neuron. We will first propose a novel neuron called auto-tuning neuron and use it to take place of the roles of the traditional neural networks used in the direct and indirect adaptive neural control systems. This can greatly reduce the computational time and network complexities due to the simple configuration of the auto-tuning neuron. It is also easy for hardware implementation. Third, based on the idea borrowed from natural evolution, genetic algorithm can search for optimal or near-optimal solutions for an optimization problem over the search domain. An optimization technique of real-coded genetic algorithm is used to design the PID controller by minimizing the performance index of integrated absolute error. The improvements of our results over that using other methods are also illustrated. In the last part of each section, some computer simulation results will also be provided to illustrate our proposed methods.

Based on the Lyapunov stability theorem, an adaptive output feedback controller is proposed in this thesis for a class of multi-input multi-output (MIMO) dynamic systems with time-varying delay and disturbances. With an adaptive mechanism embeded in the proposed control scheme, the controller will automatically adapt the unknown upper bound of perturbation, so that the information of upper bounded of perturbations is not required. Once the controlled system reaches the switching hyperplane, not only the dynamics of system can be stabilized, but also the state trajectories can be driven into a small bounded region whose size can be adjusted through the design parameter. Two numerical examples are given for demonstrating the feasibility of the proposed control scheme.

Based on the Lyapunov stability theorem, two adaptive variable structure control (AVSC) schemes with perturbation estimation are proposed in this thesis for two different classes of nonlinear systems with model uncertainties and external disturbance, so that the robust tracking problems can be solved. The class of systems firstly considered is the one which has square input matrix gain, the other is the one which has non-square input matrix and an output function. All systems considered contain perturbation in the input matrix gain. By introducing a perturbation estimation process embedded in both proposed control schemes, not only the perturbation can be estimated, but also the control energy can be reduced. In addition, the proposed control schemes also contain an adaptive mechanism in order to automatically adapt the unknown upper bound of perturbation estimation error, so that the property of uniformly ultimate boundedness for the closed-loop system is guaranteed. Finally, four numerical examples are presented to demonstrate the feasibility of the proposed control schemes.

Thesis (M.S.)--Florida State University, 2004. / Advisor: Dr. Carl A. Moore, Florida State University, College of Engineering, Dept. of Mechanical Engineering. Title and description from dissertation home page (viewed 6/21/04). Includes bibliographical references.