Feedback Linearization with Fuzzy Compensation for Uncertain Nonlinear Systems

This paper presents a nonlinear controller for uncertain single-input-single-output (SISO) nonlinear systems. The adopted approach is based on the feedback linearization strategy and enhanced by a fuzzy inference algorithm to cope with modeling inaccuracies and external disturbances that can arise. The boundedness and convergence properties of the tracking error vector are analytically proven. An application of the proposed control scheme to a second-order nonlinear system is also presented. The obtained numerical results demonstrate the improved control system performance

FUZZY FEEDBACK LINEARIZING CONTROLLER AND ITS EQUIVALENCE WITH THE FUZZY NONLINEAR INTERNAL MODEL CONTROL STRUCTURE

This paper examines the inverse control problem of nonlinear systems with stable dynamics using a fuzzy modeling approach. Indeed, based on the ability of fuzzy systems to approximate any nonlinear mapping, the nonlinear system is represented by a Takagi-Sugeno (TS) fuzzy system, which is then inverted for designing a fuzzy controller. As an application of the proposed inverse control methodology, two popular control structures, namely, feedback linearization and Nonlinear Internal Model Control (NIMC) are investigated. Moreover, the paper points out that, under some conditions, both of the control structures are equivalent and naturally implement a Smith predictor in the presence of time delays

Nonlinear and Fault-tolerant Control Techniques for a Quadrotor Unmanned Aerial Vehicle

Unmanned Aerial Vehicles (UAVs) have become more and more popular, and how to control them has become crucial. Although there are many different control methods that can be applied to the control of UAVs, nonlinear control techniques are more practical since the nonlinear features of most UAVs. In this thesis, as the first main contribution, three widely used nonlinear control techniques including Feedback Linearization Control (FLC), Sliding Mode Control (SMC), and Backstepping Control (BSC) are discussed, investigated, and designed in details and flight-tested on a unique quadrotor UAV (Qball-X4) test-bed available at the Networked Autonomous Vehicles (NAV) Lab in Concordia University. Each of these three control algorithms has its own features. The advantages and disadvantages are revealed through both simulation and experimental tests. Sliding mode control is well known for its capability of handling uncertainty, and is expected to be a robust controller on Qball-X4 UAV. Feedback linearization control and backstepping control are considered a bit weaker than sliding mode control. A comparison of these three controllers is carried out in both theoretical analysis and experimental results under same fault-free flight conditions. Testing results and comparison show the different features of different control methods, and provide a view on how to choose controller under a specific condition. Besides, safety and reliability of UAVs have been and will always be a critical issue in the aviation industry. Fault-Tolerant Control (FTC) has played an extremely important role towards UAVs’ safety and reliability and the safety of group people if an unexpected crash occurred due to faults/damages of UAVs. Therefore, FTC has been a very active and quickly growing research and development field for UAVs and other safety-critical systems. Based on the use of sliding mode control technique, referred to as Fault-Tolerant SMC (FT-SMC) have been investigated, implemented, flight-tested and compared in the Qball-X4 test-bed and also simulation environment in both passive and active framework of FTC in the presence of different actuator faults/damages, as the second main contribution of this thesis work