NMPC-Based Robotic Arm Trajectory Tracking

Overview

This project addresses the challenge of achieving precise and adaptable control of robotic arms in dynamic and uncertain environments using Nonlinear Model Predictive Control (NMPC).

Problem Statement

Achieving precise and adaptable control of robotic arms in dynamic and uncertain environments requires advanced control strategies beyond traditional PID methods.

Approach

• Leveraged Nonlinear Model Predictive Control (NMPC) and robust stochastic methods
• Modeled system using state-space representations with nonlinear dynamics
• Incorporated Gaussian processes to manage external disturbances and uncertainties
• Simulations conducted in MATLAB/Simulink

Results

• Significant improvements in trajectory tracking, stability, and robustness
• Outperformed traditional PID and linear MPC methods
• Demonstrated potential for surgical robotics and industrial automation applications

Technical Skills

MATLAB, Simulink, Simscape, NMPC, PID Control, State-Space Modeling, Arduino