Trajectory Tracking Control of a Planar Robotic Arm Using Inverse Dynamics and Fuzzy Gain Scheduling: Simulation and Experimental Validation

This paper presents a complete modeling-to-validation workflow for an inverse dynamics controller applied to a two-degree-of-freedom planar robotic arm, incorporating high-fidelity simulation and physical prototyping. The inverse dynamics model is analytically formulated inMATLAB using the Newton–Euler method and extended to include Jacobian-based velocity and acceleration estimation under a trapezoidal time-scaling law. A closed-form computed-torque control strategy is derived, with feedforward torque profiles generated from time-parameterized trajectories in Cartesian space and mapped to joint space via inverse kinematics. The system is modeled in SolidWorks to validate the mathematical formulation developed in the MATLAB environment. For trajectory generation, two representative end-effector paths are considered: a semi-elliptical curve, commonly used to emulate foot swing motions, and a cubic Bézier curve, introduced to test controller generality across diverse geometries. The complete approach is experimentally validated using a custom test jig of the robotic arm. Hardware trials and fuzzy gain scheduling focus on the semi-elliptical case, whereas the cubic Bézier trajectory is examined in simulation to demonstrate extension of the framework. Both simulation and experimental results demonstrate the efficacy of the proposed method in ensuring smooth end-effector motion, even in the presence of noise.