Yuanchun Li

This paper proposes an enhanced active disturbance rejection sliding mode tracking differentiator, which ensures the rapid state convergence of optoelectronic mechanical platforms under frictional disturbances by reconstructing the optimal synthesis function into the optimal sliding mode control synthesis function. To cope with the low stability of the active disturbance rejection controller caused by deviations in the prescribed parameters, a globally oriented parameter identification scheme is developed by the Newton–Raphson optimized sparrow search algorithm, so that the globally optimal solution of the controller parameters can be obtained iteratively. Furthermore, disturbance observer is employed to perform real-time estimation of the constructed friction disturbance model and to implement feedback compensation within active disturbance rejection control, thereby enhancing the disturbance rejection capability of the system. The effectiveness of the proposed method on the optoelectronic mechanical platform is validated experimentally, and the system exhibits strong adaptability, particularly in the presence of frictional disturbances.

This article presents an online neuro-dynamic programming (NDP)-based optimal coordinated control method of biped reconfigurable robots (BRRs) under multilayer nested learning structure. Based on the requirements of walking task, the dynamic model of BRRs is established, decomposed into two operating modes: the swing phase and the support phase. To ensure stability during the support phase, a polyhedral friction cone approximation policy is employed to describe the distribution of plantar force, with corresponding reference values obtained through a quadratic programming (QP) algorithm. A novel cost function is constructed, incorporating the interaction forces between BRRs, torso, and ground, effectively reflecting the BRRs’ stability and coordinated control performance. Then, a functional link neural network (FLNN) identifier is designed to compensate for inherent model uncertainties. Leveraging the NDP framework, a near-optimal coordinated controller is obtained through the actor–critic dual-layer neural network (NN), enabling high-performance and stable planar walking of BRRs. Furthermore, the Lyapunov stability method is utilized to demonstrate that the tracking error of closed-loop BRRs is uniformly ultimately bounded (UUB). Finally, the effectiveness of the multilayer nested learning-based optimal coordinated control method is validated through experiments conducted on the self-designed claw-type BRR platform.

To address the force/position control challenges under varying external constraints, this letter proposes an actor-critic-identify (A-C-I) fuzzy logic system (FLS) structure-based reconfigurable robot manipulators (RRM) finite-time optimal backstepping force/position control. Unlike traditional force/position control methods, the proposed controller adopts a backstepping framework to construct performance indices and optimal controllers associated with the exponential form of tracking error dynamics. The FLS is employed in place of conventional neural networks due to its ability to avoid predefined initial weights and support fast convergence. This replacement enhances the identification of system uncertainties, the approximation of performance indices, and the derivation of the finite-time optimal controller. As a result, the strategy ensures rapid state error convergence and control optimality in the presence of external constraints. Lyapunov-based analysis confirms that the closed-loop system achieves semi-global practical finite-time stability (SGPFS), and experimental results demonstrate the effectiveness of the proposed approach.

To address the force/position control challenges in transitioning from free-space motion to tasks involving environmental contact, this paper proposes an Adaptive Dynamic Programming (ADP)-based finite-time optimal backstepping force/position control method for Reconfigurable Robot Manipulators (RRMs), which ensures rapid convergence of state errors under external constraints while maintaining system stability. By integrating robust control, the proposed method enhances both convergence speed and robustness against uncertainties. Furthermore, the parameters related to robustness are optimized using a cooperative game-theoretic approach based on Pareto optimality. A Lyapunov-based analysis demonstrates the closed-loop system’s Semi-Global Practical Finite-time Stability (SGPFS). Experimental validation confirms the effectiveness of the proposed control method.