Finite-Time Output Robust Control for Restricted Joint Flight Emulator Robotic Arm With Adaptive Tangent Barrier Gains - Isaac Chairez; Alejandra Hernandez-Sanchez; Caridad Mireles; Arthur Mukhamedov; Grigory Bugriy; Stepan Lemak; Viktor Chertopolokhov (IEEE Access)
This research presents a novel, robust controller for acceleration tracking in robotic manipulators with time-varying joint limitations, accounting for articulation constraints. The proposed robotic system comprises an arm with limited joint mobility, simulating the dynamic movements required for effective motion cueing in flight simulators. The proposed robust control approach utilizes adaptive state-dependent gains to achieve finite-time convergence of the acceleration tracking error. Controller gains are determined using a class of controlled tangent barrier Lyapunov functions, ensuring that state constraints are satisfied. A stability analysis of the tracking error provides an explicit design for the state-dependent gains. A convex optimization technique based on matrix inequalities enhances the controller’s convergence rate. Given the complete forward characteristic of the recommended manipulation device, a finite-time convergent super-twisting-based differentiator may be employed to conduct the output feedback of the proposed controller specifically. The robust controller is tested on a reliable platform, a digital replica of the robotic manipulator. Indirect validation of tracking error convergence is provided through numerical evaluations, which also comply with articulation constraints and demonstrate the impact of gain optimization design. A standard state feedback control architecture serves as a benchmark for comparison. The origin is shown to be a fixed-time stable equilibrium point for the tracking error space, provided state space constraints are met, as evidenced by the faster convergence of the mean square estimation of the tracking error. Experimental assessments demonstrate that the barrier controller effectively tracks reference trajectories despite modeling uncertainties and implementation challenges, thereby validating the proposed controller.