This book shows how a conventional multi-layered approach can be used to control a snake robot on a desired path while moving on a flat surface. To achieve robustness to unknown variations in surface conditions, it explores various adaptive robust control methods. The authors propose a sliding-mode control approach designed to achieve robust maneuvering for bounded uncertainty with a known upper bound. The control is modified by addition of an adaptation law to alleviate the overestimation problem of the switching gain as well as to circumvent the requirement for knowledge regarding the bounds of uncertainty. The book works toward non-conservativeness, achieving efficient tracking in the presence of slowly varying uncertainties with a specially designed framework for time-delayed control. It shows readers how to extract superior performance from their snake robots with an approach that allows robustness toward bounded time-delayed estimation errors. The book also demonstrates how the multi-layered control framework can be simplified by employing differential flatness for such a system. Finally, the mathematical model of a snake robot moving inside a uniform channel using only side-wall contact is discussed. The model has further been employed to demonstrate adaptive robust control design for such a motion. Using numerous illustrations and tables, Adaptive Robust Control for Planar Snake Robots will interest researchers, practicing engineers and postgraduate students working in the field of robotics and control systems.

The book investigates the role of artificial input delay in approximating unknown system dynamics, referred to as time-delayed control (TDC), and provides novel solutions to current design issues in TDC. Its central focus is on designing adaptive-switching gain-based robust control (ARC) for a class of Euler-Lagrange (EL) systems with minimal or no knowledge of the system dynamics parameters. The newly proposed TDC-based ARC tackles the commonly observed over- and under-estimation issues in switching gain. The consideration of EL systems lends a practical perspective on the proposed methods, and each chapter is supplemented by relevant experimental data. The book offers a unique resource for researchers in the areas of ARC and TDC alike, and covers the state of the art, new algorithms, and future directions.

Controlling uncertain networked control system (NCS) with limited communication among subcomponents is a challenging task and event-based sampling helps resolve the issue. This book considers event-triggered scheme as a transmission protocol to negotiate information exchange in resilient control for NCS via a robust control algorithm to regulate the closed loop behavior of NCS in the presence of mismatched uncertainty with limited feedback information. It includes robust control algorithm for linear and nonlinear systems with verification. Features: Describes optimal control based robust control law for event-triggered systems. States results in terms of Theorems and Lemmas supported with detailed proofs. Presents the combination of network interconnected systems and robust control strategy. Includes algorithmic steps for precise understanding of the control technique. Covers detailed problem statement and proposed solutions along with numerical examples. This book aims at Senior undergraduate, Graduate students, and Researchers in Control Engineering, Robotics and Signal Processing.

This book shows how a conventional multi-layered approach can be used to control a snake robot on a desired path while moving on a flat surface. To achieve robustness to unknown variations in surface conditions, it explores various adaptive robust control methods. The authors propose a sliding-mode control approach designed to achieve robust maneuvering for bounded uncertainty with a known upper bound. The control is modified by addition of an adaptation law to alleviate the overestimation problem of the switching gain as well as to circumvent the requirement for knowledge regarding the bounds of uncertainty. The book works toward non-conservativeness, achieving efficient tracking in the presence of slowly varying uncertainties with a specially designed framework for time-delayed control. It shows readers how to extract superior performance from their snake robots with an approach that allows robustness toward bounded time-delayed estimation errors. The book also demonstrates how the multi-layered control framework can be simplified by employing differential flatness for such a system. Finally, the mathematical model of a snake robot moving inside a uniform channel using only side-wall contact is discussed. The model has further been employed to demonstrate adaptive robust control design for such a motion. Using numerous illustrations and tables, Adaptive Robust Control for Planar Snake Robots will interest researchers, practicing engineers and postgraduate students working in the field of robotics and control systems.

The book investigates the role of artificial input delay in approximating unknown system dynamics, referred to as time-delayed control (TDC), and provides novel solutions to current design issues in TDC. Its central focus is on designing adaptive-switching gain-based robust control (ARC) for a class of Euler-Lagrange (EL) systems with minimal or no knowledge of the system dynamics parameters. The newly proposed TDC-based ARC tackles the commonly observed over- and under-estimation issues in switching gain. The consideration of EL systems lends a practical perspective on the proposed methods, and each chapter is supplemented by relevant experimental data. The book offers a unique resource for researchers in the areas of ARC and TDC alike, and covers the state of the art, new algorithms, and future directions.