Robotics Advance | Chapter 6: Control Systems for Robotics by Prakhar Chauhan | Learn Smarter
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Chapter 6: Control Systems for Robotics

Control systems in robotics serve as the essential link between intended motions and physical actions, addressing complexities such as uncertainties and variable dynamics. This chapter covers a variety of advanced control strategies including PID enhancement, adaptive control, robust control, optimal control, nonlinear control, and specialized methods for underactuated and nonholonomic systems. The application of these techniques is crucial in achieving desired robotic behaviors across various advanced robotic systems.

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Sections

  • 6

    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.

    Learning Practice

  • 6.1

    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.

    Learning Practice

  • 6.1.1

    Pid Control Review

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

    Learning Practice

  • 6.1.2

    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.

    Learning Practice

  • 6.1.3

    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.

    Learning Practice

  • 6.1.3.1

    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.

    Learning Practice

  • 6.1.3.2

    Self-Tuning Regulators (Str)

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

    Learning Practice

  • 6.2

    Robust And Optimal Control Strategies

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

    Learning Practice

  • 6.2.1

    Robust Control

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

    Learning Practice

  • 6.2.1.1

    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.

    Learning Practice

  • 6.2.2

    Optimal Control

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

    Learning Practice

  • 6.2.2.1

    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.

    Learning Practice

  • 6.2.2.2

    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.

    Learning Practice

  • 6.3

    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.

    Learning Practice

  • 6.3.1

    Feedback Linearization

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

    Learning Practice

  • 6.3.2

    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.

    Learning Practice

  • 6.4

    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.

    Learning Practice

  • 6.4.1

    Force Control

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

    Learning Practice

  • 6.4.2

    Hybrid Position/force Control

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

    Learning Practice

  • 6.4.3

    Impedance And Admittance Control

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

    Learning Practice

  • 6.5

    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.

    Learning Practice

  • 6.5.1

    Underactuated Systems

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

    Learning Practice

  • 6.5.2

    Nonholonomic Systems

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

    Learning Practice

  • 6.6

    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.

    Learning Practice

  • 6.7

    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.

    Learning Practice

References

Untitled document (22).pdf

Class Notes

Memorization

What we have learnt

  • Classical PID control can b...
  • Robust control ensures perf...
  • Optimal controllers like LQ...

Final Test

Revision Tests