Control Systems For Robotics

This section discusses various advanced control systems in robotics, emphasizing strategies like PID control, adaptive control, robust techniques, optimal control, nonlinear methods, and approaches for underactuated and nonholonomic systems.

Advanced Pid And Adaptive Control

This section covers advanced techniques for PID control and adaptive control in robotics, essential for dealing with changing system dynamics and improving performance.

Pid Control Review

PID control is a foundational technique in robotics that minimizes output errors by using Proportional, Integral, and Derivative components.

Advanced Pid Enhancements

This section covers advanced methods to enhance PID control in robotics, addressing real-world challenges such as non-ideal conditions and dynamic changes.

Adaptive Control

Adaptive control allows a controller to adjust its parameters in real-time to deal with changes in system dynamics, making it particularly useful in uncertain environments.

Model Reference Adaptive Control (Mrac)

Model Reference Adaptive Control (MRAC) is a dynamic control strategy that adapts controller parameters to achieve desired system performance based on a predefined model.

Self-Tuning Regulators (Str)

Self-Tuning Regulators adapt control laws online by estimating system parameters, advancing the capabilities of adaptive control.

Robust And Optimal Control Strategies

This section introduces robust and optimal control strategies crucial for maintaining performance in robotics despite uncertainties and disturbances.

Robust Control

Robust control strategies are designed to maintain stability and performance in control systems despite uncertainties and external disturbances.

H-Infinity Control

H-infinity control is a robust control strategy that aims to minimize the worst-case amplification of disturbances in robotic control systems.

Optimal Control

Optimal control focuses on minimizing a cost function while satisfying system dynamics, utilizing strategies like Linear Quadratic Regulators (LQR).

Linear Quadratic Regulator (Lqr)

The Linear Quadratic Regulator (LQR) is a method for optimal control that minimizes a quadratic cost function while managing the dynamics of a system.

Extensions

This section discusses various advanced control strategies in robotics, emphasizing the importance of extensions like LQG and MPC for improving control system performance under real-world constraints.

Nonlinear Control And Feedback Linearization

This section focuses on nonlinear control strategies, particularly feedback linearization, which is essential for managing the complexities of robotic systems that exhibit nonlinear behavior.

Feedback Linearization

Feedback linearization is a method that transforms a nonlinear system into an equivalent linear system for easier control design and analysis.

Sliding Mode Control (Smc)

Sliding Mode Control (SMC) is a robust control strategy that drives system behavior along a predetermined sliding surface to maintain performance despite disturbances.

Force And Impedance Control

This section focuses on the importance of force and impedance control in robotics, highlighting how these techniques enhance human-robot interaction and performance in various tasks.

Force Control

Force control in robotics looks beyond traditional position or velocity control to focus on regulating interaction forces between robots and their environments.

Hybrid Position/force Control

Hybrid position/force control integrates position and force control for effective robotic interaction with environments.

Impedance And Admittance Control

This section discusses Impedance and Admittance Control as essential methods in robotics for managing interaction forces and motion.

Control In Underactuated And Nonholonomic Systems

This section discusses control strategies for underactuated and nonholonomic systems in robotics, highlighting methods that exploit natural dynamics for control.

Underactuated Systems

Underactuated systems in robotics have fewer control inputs than degrees of freedom, requiring innovative control strategies to exploit their natural dynamics.

Nonholonomic Systems

Nonholonomic systems have non-integrable constraints that affect their movement, particularly in wheeled robots.

Advanced Topics And Research Areas

This section explores cutting-edge research areas in robotics control, emphasizing learning-based control, passivity-based control, whole-body control, and human-in-the-loop systems.

Chapter Summary

This section provides a comprehensive overview of key control strategies and methodologies in robotics, focusing on the application and significance of various control techniques.