Dynamics and Control of Robotic Systems syllabus

Lagrangian Dynamics and Inertia Tensors: Introduction to Lagrange's equation, Derivation from Kinetic and potential energy, Link inertia tensor, Jacobian inertia tensor and their role in manipulator dynamics. Dynamic Models, Trajectories, and Control: Newton-Euler (recursive) and Lagrange-Euler (closedform) dynamic models, Dynamic modeling of 2-link and 3-link manipulators, Operational space dynamic model, Trajectory planning considerations, Joint interpolated trajectories and interpolation methods, Set point tracking, Introduction to actuator dynamics.

State Space Representation and Properties: Introduction to state variables and state space representation, State-space representations of transfer-function systems, Controllability and observability (definitions, Kalman rank condition tests, importance in control design). Controller Design in State Space: Design of controllers using root-locus in state space, Pole placement with state feedback (full-state feedback) and with output feedback, Introduction to robust control systems.

MIMO Modeling, Analysis, and Properties: Modeling, analysis, and design of linear multi-input, Multi-output (MIMO) control systems, Both state space and transfer matrix (frequency domain) approaches, Stability analysis of MIMO LTI systems, Controllability, Sterilizability (stabilizability), observability in MIMO context. Realization, Reduction, and Design: Realization theory and model order reduction for MIMO systems, Multivariable control system design methodologies.

Joint Space Control Methods: Manipulator control problem (challenges: nonlinearity, coupling, uncertainties), Joint space control, Computed torque techniques, Near minimum time control, Feedforward control. Existing Control Algorithms for Robots: Survey of existing control algorithms used in controlling robots including PD control with gravity compensation, Inverse dynamics control, Nonlinear decoupled feedback control, Resolved motion control, Adaptive control.

Programming Nonlinear and Adaptive Control Strategies: PD control with gravity compensation, Full inverse dynamics (feedback linearization) control, Nonlinear decoupled feedback control, Resolved motion control in operational space, Adaptive control for robots with unknown or varying parameters. Trajectory Programming and Integration: Robot control of trajectory using programming languages (e.g., VAL, RAPID, KRL), Comparison and integration of all motion control strategies.