ISA transactions最新文献

The distributed optimal design of high-speed train movement is systematically investigated in this article. A distributed optimal control law is proposed, addressing the train consist of cars coupled by spring buffers, and is affected by aerodynamic drag and rolling resistance. A new distributed controller is proposed to decouple the train model by fully removing the in-train force, which greatly simplifies the complexity of calculation. Then the pending problem is redescribed to the control of cars with different mass. Grounded on the Lyapunov stability theory and optimal control theory, distributed optimal control law is proposed in line with guaranteed cost function, which enables faster updates of the real-time status of each car and adaptive vehicle mass. It ensures consistency in the tracking process of each car of the train, and further reduces the in-train force among cars. To eliminate the speed overshoot which results from the influence of acceleration change during train operation, we weigh in with the feed-forward compensator to assure the train's good acceleration performance. Ultimately, numerical simulations results are obtained to demonstrate convincingly the significance of our proposed control law.

The article aims at the decentralized optimal H_∞ fusion estimation issue for multi-sensor networked systems with insecure network communications, where hybrid attacks consisting of stochastic deception and denial-of-service attacks happen on both the sensor-to-local filter channel and the local filter-to-fusion center channel simultaneously. Some random variables obeying Bernoulli distributions are utilized to depict the hybrid attacks existing in two classes of communication channels in a unified framework. Relying on a novel augmentation method, the fusion estimation error system with globally internal dynamics is obtained. Two sufficient conditions to assure the corresponding H_∞ performance and exponentially mean-square stability of the local and fusion estimation error systems are derived. To reduce the adverse effect of hybrid attacks, the decentralized optimal H_∞ fusion filter with better H_∞ performance index than each local H_∞ filter is presented by linear matrix inequality technique. An actual civil aircraft system demonstrates the algorithms to be valid.

The article proposes a novel state-feedback control method for a multiple-input multiple-output (MIMO) nonlinear system with actuator faults and input quantization. The innovation of the design approach lies in the utilization of fuzzy logic systems (FLSs) to approximate the uncertain intermediate virtual control laws, thereby achieving a simplified virtual control design form. Additionally, finite-time control is employed to enhance the system's response speed. Different from the existing literatures, the adaptive control scheme of partial loss fault gain is integrated with input quantization, which completes the unknown gain estimation and avoids the assumption condition of unknown control gain. The theoretical analysis combined with Lyapunov stability analysis shows that the tracking error can converge regardless of whether the system experiences a fault, while the closed-loop signal remains stably bounded for a finite time. Finally, the simulation results of the quadrotor unmanned aerial vehicle (UAV) attitude system indicate that this control scheme is effective.

This paper presents a map-free navigation approach for industrial automatic mobile robots (AMRs), designed to ensure computational efficiency, cost-effectiveness, and adaptability. Utilizing deep reinforcement learning (DRL), the system enables real-time decision-making without fixed markers or frequent map updates. The central contribution is the Heuristic Dense Reward Shaping (HDRS), inspired by potential field methods, which integrates domain knowledge to improve learning efficiency and minimize suboptimal actions. To address the simulation-to-reality gap, data augmentation with controlled sensor noise is applied during training, ensuring robustness and generalization for real-world deployment without fine-tuning. Training results underscore HDRS's superior convergence speed, training stability, and policy learning efficiency compared to baselines. Simulation and real-world evaluations establish HDRS-DRL as a competitive alternative, outperforming traditional approaches, and offering practical applicability in industrial settings.

The stability of the suspension is a key challenge for the application and promotion of electromagnetic suspension technology, especially when it operates in conjunction with a flexible structure, which significantly increases the system's complexity. This paper abstracts the characteristics of the coupling conditions between an electromagnetic suspension system and a flexible structure and designs and constructs an experimental apparatus that includes an electromagnet and a simulated flexible structure with adjustable stiffness and inertia. Based on the Lyapunov method, the central manifold theorem, and the Poincaré method, the stability of the electromagnetic suspension system and the conditions for Hopf bifurcations are derived. Finally, through reasonable experimental design and data analysis, the correctness of the theoretical analysis conclusions is validated, providing references for the engineering applications of electromagnetic suspension systems.

To increase the adaptability of bridge construction equipment in high-altitude settings, this study examines a magnetorheological (MR) damper designed for cable-stayed climbing robots. Initially, a novel damper incorporating a spring-MR fluid combination and three magnetic circuit units is developed. A robot-cable-wind coupling dynamic model is subsequently formulated via Hamilton's principle, based on force analysis. The simulation results indicate that the damper's maximum output force is 204.60 N, with optimal working currents of 0.2 A (Force 4) and 0.4 A (Force 7). To verify the analysis, testing is conducted using an MR damper. The results reveal an average relative error of 4.60% for the actual output damping force. When mounted on the robot, the climbing speed range, average relative error, and maximum relative error are controlled within 0.66 mm/s, 0.78% and 2.5%, respectively. This approach allows for the rapid selection of suitable working currents and markedly enhances the climbing stability of the robot.

This paper investigates an output feedback adaptive fault-tolerant tracking control for a class of nonlinear systems with system nonlinearities, sensor failures and external disturbances, in which sensor redundancy is employed to enhance measurement reliability. A sensor fusion mechanism, together with a novel history-based weighted average algorithm is first designed to fuse all sensor outputs. Then, an adaptive controller based on the sensor fusion output, a dynamic gain and a state observer is constructed to handle all the uncertainties caused by system nonlinearities, external disturbances, sensor failures and fusion mechanism. It is shown that by using the proposed scheme, the closed-loop system is stable, the sensor fusion mechanism can eliminate the effects of faulty sensors, and the real tracking error can be driven into a small compact set mainly affected by the fusion error. Experimental results are accomplished to validate the proposed scheme.

This work investigates the time-varying formation tracking (TVFT) problem for linear multi-agent systems with a leader of unknown input in the presence of unknown external disturbances on directed graphs. Each agent is subjected to different external disturbances generated by unknown exosystems. To eliminate the unknown external disturbances of each follower, an adaptive disturbance observer and a state observer are constructed first. Then, utilizing the estimated information, a fully distributed TVFT protocol is designed with adaptive coupling parameters such that the global information of the communication topology is not required. By the developed distributed controller, the multi-agent system on a communication topology containing a directed spanning tree can asymptotically track the leader with a desired time-varying formation and simultaneously reject external disturbances in spite of their unknown exosystems. Furthermore, the proposed controller is modified to a continuous one to eliminate the chattering problem. Finally, a simulation example is provided to illustrate the effectiveness of theoretical results.

Recent decades, extensive applications exemplified in intelligent connected vehicles (ICVs) and unmanned aerial vehicles (UAVs) have emerged with the rapidly development of multi-agent systems (MASs). Inspired by these applications, the optimal tracking control problem for uncertain MASs under uncertain topological networks is addressed based on the theory of observer design and reinforcement learning (RL). Thus, an adaptive extended observer based on concurrent learning (CL) technique is designed to simultaneously estimate system states and unknown parameters, where unknown parameters estimated convergence is guaranteed in a relaxed persistence of excitation condition. Moreover, a Luenberger observer is designed to estimate the state of the leader under uncertain topological networks, which acts as the information compensation of the leader. Via the proposed observers, an optimal tracking control algorithm is devised leveraging actor-critic (AC)-neural network (NN), which does not require the state derivative information. Lastly, a numerical simulation is performed to demonstrate the validity of the scheme in question.

One of the most significant advantages of Model Predictive Control (MPC) is its ability to explicitly incorporate system constraints and actuator specifications. However, a major drawback is the computational cost associated with calculating the optimal control sequence at each sampling time, posing a substantial challenge for real-time implementation in high-order systems with fast dynamics. Additionally, uncertainties are inherently present in dynamic systems, requiring a robust formulation that accounts for these uncertainties. Additionally, uncertainties are inherently present in dynamic systems, requiring a robust formulation that accounts for these uncertainties. The tube-based MPC is one of the robustification formulations that can tackle these challenges. We propose a comprehensive methodology for designing a tube-based MPC framework specifically tailored for high-order Linear Parameter-Varying (LPV) systems with fast dynamics, along with its real-time implementation in embedded systems. Our innovations include the use of zonotopes for the offline computation of reachable sets, significantly reducing computational costs, and the development of new Linear Matrix Inequality (LMI) conditions that ensure the existence of nominal control and state sets. Additionally, we introduce a novel scaled-symmetric ADMM-based optimization algorithm, which diverges from conventional quadratic programming structures and integrates acceleration strategies and normalization techniques for enhanced numerical robustness and rapid convergence. The methodology is validated on a tiltrotor UAV with a suspended load, demonstrating its effectiveness in a trajectory tracking problem. Experimental results using a controller-in-the-loop (CIL) framework with a high-fidelity 3D simulator confirm its suitability for real-time control in practical scenarios.