Data-Driven Disturbance-Observer-Based Actuator-Space Control of a Dual-Axis Thrust-Vectoring Platform

Electromechanical thrust-vector control systems are subject to friction, backlash, and configuration-dependent dynamics that are difficult to model explicitly, motivating controllers that adapt from operational data without requiring an identified plant. This paper proposes a data-driven, disturbance-observer-based controller in actuator space for a dual-axis thrust-vectoring platform. The controller operates on a sliding surface defined over the actuator tracking error and uses an adaptive input matrix gain together with a lumped disturbance observer, both of which are updated from encoder and control data through gradient descent applied to a joint identification loss. The Newton–Euler equations of the mechanical system are projected onto the actuator coordinates through the angular-velocity map, yielding a structurally well-posed actuator-space model, which is used to design the adaptive gain; nonetheless, the dynamic model is never evaluated online. The resulting controller requires only encoder measurements of actuator displacements, a reference trajectory computed from the platform geometry, and bounded normalized commands; the mechanism Jacobian and the inertia and Coriolis matrices are not evaluated online. Boundedness of the adaptive gain and disturbance estimate is established, and uniform ultimate boundedness of the tracking error follows under a mild alignment condition. Simulation results on a coupled nonlinear plant and hardware-in-the-loop experiments on a dual-channel actuator testbed are presented, comparing the proposed controller against PID, super-twisting, unit-vector sliding mode, and MFAC baselines on a circular thrust-vector reference.