Recurrent neural network (RNN) has recently been viewed as a significant alternative to online mathematical problem solving. This paper offers important improvements by proposing the first RNN model to solve the time-dependent underdetermined linear system with bound constraint. In particular, by introducing a time-dependent non-negative vector, the bound-constrained underdetermined linear system is initially transformed into a time-dependent system that comprises linear and nonlinear equations. The newly constructed RNN model can thus zero in on the time-dependent equations. Then, the model is theoretically proven to have convergence properties, and the simulation results further substantiate the efficacy of the proposed RNN model to solve the time-dependent underdetermined linear system with bound constraint. Finally, the proposed RNN model is applied to physically-constrained redundant robot manipulators, thereby indicating the applicability of the proposed model.

Based on the P-type composition of Petri nets (PN) defined in this paper, a framework for a structuralcontrol of discrete event systems (DES) is constructed such that a closed-loop PN is obtained by composing a plant PN and a controller. As for a disjunction or conjunctive normal form (CNF) of linear constraints, a new approach is proposed to design a structural controller in this framework. First, a switching-net is defined for a disjunction of constraints, and an extended plant is obtained through the P-type composition of a plant PN and switching-net. Second, the disjunction of bounded constraints are transformed into a conjunction of switching-constraints on the extended plant. Third, a controller is synthesized by designing monitors for conjunctive switchingconstraints according to a supervision-based-on-place-invariant method. Fourth, in a similar manner, a controller is also designed for a CNF of bounded constraints. The resulting controller is maximally permissive if each disjunction of constraints meets the jump-free condition, and its size grows polynomially with the number of constraints. Another advantage is that the closed-loop system is still a PN for many real DES since a CNF can describe not only convex but also nonconvex state-regions.

In robotic force–motion interaction tasks, ensuring stable and accurate force tracking in environments with unknown impedance and time-varying contact dynamics remains a key challenge. Addressing this, the study presents an intelligent impedance control (IIC) strategy that integrates model-based insights with deep reinforcement learning (DRL) to improve adaptability and robustness in complex manipulation scenarios. The control problem is formulated as a Markov Decision Process (MDP), and the Deep Deterministic Policy Gradient (DDPG) algorithm is employed to learn continuous impedance policies. To accelerate training and improve convergence stability, an expert-guided initialization strategy is introduced based on iterative error feedback, providing a weak-model-based demonstration to guide early exploration. To rigorously assess the impact of contact uncertainties on system behavior, a comprehensive performance analysis is conducted by utilizing a time- and frequency-domain approach, offering deep insights into how impedance modulation shapes both transient dynamics and steady-state accuracy across varying environmental conditions. A high-fidelity simulation platform based on MATLAB (version 2021b) multi-toolbox co-simulation is developed to emulate realistic robotic contact conditions. Quantitative results show that the IIC framework significantly reduces settling time, overshoot, and undershoot under dynamic contact conditions, while maintaining stability and generalization across a broad range of environments.

Purpose – According to UN estimates more than 2,000 people are killed or maimed every month by land-mines. Although some mechanical solutions to their removal have been proposed, this is still heavily dependent on human manipulation. This study seeks to posit a robotic solution to this extremely hazardous operation. Design/methodology/approach – Examines an active tele-operated master-slave robot hand system in which the master and slave hands have completely different structures. Findings – A secure grasping strategy with a neuro-fuzzy position control is optional, involving robust position control and accurate force control. Originality/value – To the best of the authors' knowledge, the configuration and control system of the tele-operation master-slave robotic hand is novel in the applied robotics research field.