Adaptive H-Infinity MPC-Fuzzy Control Integrated With Sliding-Mode Disturbance Observer for Trajectory Tracking of a 5-DOF Robotic Manipulator With Gripper

Existing control schemes for 5-DOF robotic manipulators suffer from fundamental limitations: conventional PID controllers cannot handle nonlinear dynamics and coupling effects, adaptive methods exhibit slow response to sudden parameter changes, robust H∞ controllers sacrifice performance for worst-case robustness, and intelligent methods lack formal stability guarantees. These limitations become critical in high-precision applications requiring simultaneous handling of model uncertainties, external disturbances, nonlinearities, and physical constraints. This paper addresses these challenges by proposing a novel integrated control architecture that systematically combines four complementary control methodologies within a unified framework: (1) adaptive H-infinity control providing formal robustness guarantees, (2) Model Predictive Control enabling constraint handling and optimization, (3) Fuzzy Logic control managing complex nonlinearities, and (4) Sliding Mode Control with enhanced disturbance observer for high-frequency noise rejection. The primary contribution is a novel adaptive weighting mechanism that dynamically adjusts each controller's contribution based on real-time performance metrics using exponential functions of tracking error magnitude, ensuring smooth transitions and optimal resource allocation without hard-switching limitations. The integrated framework employs a comprehensive mathematical analysis including composite Lyapunov stability proofs to guarantee asymptotic convergence of tracking error and boundedness of control signals. Rigorous theoretical analysis provides formal convergence guarantees and robustness bounds for parameter variations up to ±30%. The proposed approach is validated through extensive simulations conducted on MATLAB/Simulink using a detailed 5-DOF robotic manipulator model with clearly defined Denavit-Hartenberg parameters, mass distribution, and inertial properties. Simulation results demonstrate significant performance enhancement compared to conventional control methods. The proposed controller achieves superior tracking accuracy with RMS tracking error of 1.35 mm compared to 2.50 mm for H∞ control, 2.87 mm for adaptive control, and 5.58 mm for PD control, representing up to 46% improvement over the best existing robust control method. The system exhibits exceptional robustness with only 11.5% performance degradation under ±30% parameter variations compared to 31.2% degradation for H∞ control and 85.3% for PD control. Disturbance rejection capability is demonstrated with maximum tracking error of 3.21 mm and recovery time of 0.43 s under 5N external force disturbance, significantly outperforming conventional methods.