Real-Time High-Precision Control of Robot Manipulators: An Adaptive Data-Driven Linear MPC Framework

High-precision control of constrained robot manipulators under uncertainties remains a fundamental challenge. While conventional nonlinear model predictive control (NMPC) often struggles with real-time requirements due to its heavy computational burden, linear MPC (LMPC) typically relies on terminal invariant sets that are often computationally intractable for complex nonlinear systems. To address these limitations, this paper proposes a computationally efficient data-driven linear model predictive control (DLMPC) framework that achieves control accuracy comparable to NMPC while ensuring real-time performance. A variable-length sliding-window dynamic mode decomposition with control (DMDc) method is developed to identify a time-varying local affine model from recent input–output data, enabling accurate linearization under uncertainties. Based on this model, a novel time-varying terminal constraint is designed to substitute the conventional terminal set, thereby obviating the need for uncertainty upper bounds that are difficult to obtain in practice. The recursive feasibility and stability of the proposed framework are established using Lyapunov stability theory. Finally, experimental results on a Franka Emika Panda robot demonstrate the effectiveness and superior performance of the proposed method.