Design and simulation of intelligent nonlinear controllers for nonlinear dynamical engineering systems using MATLAB/SIMULINK
Application to Selected Engineering systems
This book focuses on designing efficient adaptive nonlinear controllers for strict-feedback nonlinear systems, addressing multiple challenges to enhance safety and reliability. The challenges include external disturbances, uncertain dynamics, actuation faults, unmeasured states, constrained input, unknown control direction, and singularities in the control law. It introduces adaptive nonlinear control schemes that are practical for practitioners, enabling them to effectively tackle these issues simultaneously. The design incorporates reaching law-based Sliding Mode Control strategies combined with Input-Output Feedback Linearization techniques. Radial Basis Function Neural Networks (RBFNN) and Fuzzy Neural Networks (FNNs) are utilized to approximate unknown system dynamics. A model-free high-gain state observer is employed to estimate unavailable state variables. To address unknown control direction, a Nussbaum type function is introduced. The proposed schemes have broad applicability, overcoming significant restrictions found in existing literature while ensuring excellent transient and steady-state performance, including no peak overshoot, shorter settling times, smaller error bounds, and low continuous control efforts. These advancements eliminate the need for prior knowledge of bounds related to system dynamics uncertainties, approximation errors, actuator faults, external disturbances, control direction, and full-state me
