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Computer Science
Robotics
Robotics and Autonomous Systems
1. Introduction to Robotics and Autonomous Systems
2. Robot Kinematics and Dynamics
3. Sensors and Perception
4. Localization and State Estimation
5. Planning and Navigation
6. Robot Control
7. Machine Learning for Robotics
8. System Integration and Implementation
9. Safety, Reliability, and Ethics
10. Applications and Case Studies
Robot Control
Control Theory Fundamentals
System Modeling
Transfer Function Representation
State-Space Models
Linearization Techniques
Stability Analysis
Lyapunov Stability
BIBO Stability
Routh-Hurwitz Criterion
Performance Specifications
Transient Response
Steady-State Error
Robustness Measures
Classical Control Methods
PID Control
Proportional Control
Integral Control
Derivative Control
PID Tuning Methods
Ziegler-Nichols Method
Cohen-Coon Method
Model-based Tuning
Root Locus Design
Frequency Domain Design
Bode Plots
Nyquist Criterion
Lead-Lag Compensation
Modern Control Theory
State-Space Control
Controllability and Observability
State Feedback Design
Observer Design
Luenberger Observer
Kalman Filter as Observer
Linear Quadratic Regulator
Cost Function Design
Riccati Equation
LQG Control
Robust Control
H-infinity Control
Mu-synthesis
Uncertainty Modeling
Nonlinear Control
Nonlinear System Analysis
Phase Plane Analysis
Describing Functions
Feedback Linearization
Input-Output Linearization
State Feedback Linearization
Sliding Mode Control
Sliding Surface Design
Reaching Condition
Chattering Reduction
Adaptive Control
Model Reference Adaptive Control
Self-Tuning Regulators
Robot-Specific Control
Joint Space Control
Independent Joint Control
Computed Torque Control
Robust Joint Control
Cartesian Space Control
Resolved Motion Rate Control
Impedance Control
Hybrid Position-Force Control
Redundancy Resolution
Pseudoinverse Methods
Null Space Projection
Task Priority Methods
Advanced Control Techniques
Model Predictive Control
Prediction Models
Optimization Problem Formulation
Constraint Handling
Receding Horizon Implementation
Optimal Control
Calculus of Variations
Pontryagin's Maximum Principle
Dynamic Programming
Learning-based Control
Adaptive Control with Learning
Neural Network Control
Reinforcement Learning Control
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7. Machine Learning for Robotics