This work describes analysis, design, and implementation of a novel switching robust adaptive control (SRAC) method for nonlinear systems. The proposed method takes advantage of both adaptive control (AC) and robust control (RC) methods. SRAC employs one of the methods when this method is advantageous and switches to the other method when the other one becomes the preferred choice. To this end, RC is used to deal with transient effects caused by uncertainties and disturbances. The system switches over to AC for good steady state performance when certain switching criteria are satisfied. If external disturbances become dominant or new uncertainties are introduced while AC is active, the system will switch back to RC. In this manner, the switching process between AC and RC will continue to take place guaranteeing improved performance, robustness, and accuracy for the entire operation of the system. The novel idea behind the proposed method is a smart novel mechanism of bi-directional switching between RC and AC. In this mechanism, the involvement of estimators and switching rules play a decisive part in guaranteeing the smooth switching and the stability of the system. The implementation and design issues of the novel method were first evaluated by simulation on a mass spring system and then on a robot manipulator system. To control these systems with satisfactory performance, nonlinearities and uncertainties have been properly analysed and embedded into models and control algorithms. Simulation results showed the superior performance of the proposed method compared with other control methods. The experimental validation of the proposed method was conducted on a Puma 560 robot manipulator system which was established by joints 2 and 3 of the robot. Extensive comparative experimental results have validated the efficacy and superior performance of the proposed SRAC method over other control methods in the face of uncertainties and disturbances. As part of this work, a comprehensive dynamic model of robotic manipulator in the presence of joint motors, gravitational forces, friction forces and payload has been developed using MAPLE. A systematic design framework for the SRAC method has also been developed.

The thesis deals with the design and implementation of an Adaptive Flight Control technique for Unmanned Aerial Vehicles (UAVs). The application of UAVs has been increasing exponentially in the last decade both in Military and Civilian fronts. These UAVs fly at very low speeds and Reynolds numbers, have nonlinear coupling, and tend to exhibit time varying characteristics. In addition, due to the variety of missions, they fly in uncertain environments exposing themselves to unpredictable external disturbances. The successful completion of the UAV missions is largely dependent on the accuracy of the control provided by the flight controllers. Thus there is a necessity for accurate and robust flight controllers. These controllers should be able to adapt to the changes in the dynamics due to internal and external changes. From the available literature, it is known that, one of the better suited adaptive controllers is the model based controller. The design and implementation of model based adaptive controller is discussed in the thesis. A critical issue in the design and application of model based control is the online identification of the UAV dynamics from the available sensors using the onboard processing capability. For this, proper instrumentation in terms of sensors and avionics for two platforms developed at UNSW@ADFA is discussed. Using the flight data from the remotely flown platforms, state space identification and fuzzy identification are developed to mimic the UAV dynamics. Real time validations using Hardware in Loop (HIL) simulations show that both the methods are feasible for control. A finer comparison showed that the accuracy of identification using fuzzy systems is better than the state space technique. The flight tests with real time online identification confirmed the feasibility of fuzzy identification for intelligent control. Hence two adaptive controllers based on the fuzzy identification are developed. The first adaptive controller is a hybrid indirect adaptive controller that utilises the model sensitivity in addition to output error for adaptation. The feedback of the model sensitivity function to adapt the parameters of the controller is shown to have beneficial effects, both in terms of convergence and accuracy. HIL simulations applied to the control of roll stabilised pitch autopilot for a typical UAV demonstrate the improvements compared to the direct adaptive controller. Next a novel fuzzy model based inversion controller is presented. The analytical approximate inversion proposed in this thesis does not increase the computational effort. The comparisons of this controller with other controller for a benchmark problem are presented using numerical simulations. The results bring out the superiority of this technique over other techniques. The extension of the analytical inversion based controller for multiple input multiple output problem is presented for the design of roll stabilised pitch autopilot for a UAV. The results of the HIL simulations are discussed for a typical UAV. Finally, flight test results for angle of attack control of one of the UAV platforms at UNSW@ADFA are presented. The flight test results show that the adaptive controller is capable of controlling the UAV suitably in a real environment, demonstrating its robustness characteristics.

Vision is a flexible, non-contact sensor that can be used for position feedback in closed-loop control of dynamic systems. Current vision systems for industrial automation provide low sample rates and large sample delays relative to other types of position sensors. Poor sample rates and sample delays are a result of the vast volume of data that must be collected and processed by the vision system. A predictive visual tracker can help compensate for some of the deficiencies of current industrial vision systems. The objectives of the present research are to demonstrate that vision is a useful feedback sensor and prediction can be used to improve performance by compensating for the feedback delay of the vision system. An inverted pendulum was stabilized using a vision sensor as feedback to a state-feedback controller. The vision data was run through a d-step ahead predictor to compensate for the vision system delays. The system was simulated in Mat lab and an actual physical system was used to test the performance of the control system. The inverted pendulum provides a good test-bed for studying predictive control using vision feedback. The pendulum will fall without the constant adjustment of the cart position. The adjustment of the cart by the controller is delayed because of latency and quantization errors in vision feedback. The better the controller is able to compensate for delays and quantization errors, the greater its ability to stabilize the inverted pendulum. / Graduation date: 1996

This thesis presents an effective control design methodology using a one-step-ahead prediction adaptive control law and an adaptive control law based on a Lyapunov function. These control law were applied to a highly maneuverable high performance aircraft, in particular, a modified F/A-18. An adaptive controller is developed to maneuver an aircraft at a high angle of attack even if the aircraft is required to fly over a highly nonlinear flight regime. The adaptive controller presented in this thesis is based on linear, bilinear, and nonlinear prediction models with input constraints. It is shown that the linear, bilinear, and nonlinear adaptive controllers can be constructed to minimize the given cost function or Lyapunov function with respect to the control input at each step. The control is calculated such that the system follows the reference trajectory, and such that control signal remains within its constraints. From several simulation results, the nonlinear controller is controller is better than the linear controller. A nonlinear adaptive control law based on a Lyapunov function is designed such that control inputs are smoother than for the one-step-ahead prediction adaptive controller. / Graduation date: 1994

by Michael Athans, Sanjoy K. Mitter, Lena Valavani. / Final report. / Bibliography: p. 26-31. / "March 1982." / Air Force Office of Scientific Research Grant AFOSR-77-3281B

by Michael Athans and Sanjoy K. Mitter. / Includes bibliographical references. / Research supported by Air Force Office of Scientific Research (AFSC), Research Grant AFOSR 77-3281. Covers time period, March 1, 1977 to February 28, 1978.

by Michael Athans, Sanjoy K. Mitter, Lena Valavani. / Interim report. / Includes bibliographies. / "March 20, 1981." / Air Force Office of Scientific Research Grant AFOSR-77-3281C

Charles E. Rohrs ... [et al.]. / Bibliography: p. 63-64. / "August 1981." / Supported by the Air Force Office of Scientific Research under Grant AF-AFOSR-77-3281 NASA Ames Research Center Grant NGL-22-009-124

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 152-166).

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