Optimal Adaptive Control for Multimotor Driving Servo Systems Under Spatiotemporal Performance Constraints

Multimotor driving servo systems (MMDSS) are widely employed in heavy-duty equipment and vehicles due to their superior driving capabilities and flexible coordination. However, the large inertia of these systems leads to significant energy consumption, which is overlooked in existing control strategies. Therefore, this article investigates energy-optimized load tracking control for MMDSS under spatiotemporal performance constraints, which require satisfying the prescribed transient and steady-state performances within a predefined time. First, given that unavoidable parameter uncertainties in MMDSS are a key factor contributing to excessive energy consumption, an optimal predefined-time adaptive law is developed to estimate the unknown parameters. This adaptive law ensures both the convergence and optimality of the parameter estimation within a predefined time and reduces computational complexity by avoiding the solution of the algebraic Riccati equation. Then, by employing the backstepping technique and a nonlinear command filter, an optimal adaptive tracking controller is designed to guarantee that the load output satisfies the spatiotemporal performance constraints, while simultaneously minimizing a cost function incorporating tracking error and control input. Notably, the proposed controller does not require a system relative degree of one and avoids singularity issues. To address the limitations imposed by initial conditions in prescribed performance control, this article proposes a unified error transformation function. This design ensures that the initial tracking error inherently satisfies performance constraints and eliminates the need for complex region partitioning. Finally, experiments conducted on a four-motor platform demonstrate the effectiveness of the proposed method.