This paper presents a gradient-based fault detection method for pitch control systems in avionics. On the basis of the dynamic model of the airplane, the proposed method detects both operator and sensor faults by monitoring the online data. By integrating fault-related behaviors over an extended time window, the method effectively amplifies small changes caused by incipient faults, improving detectability. Theoretical analysis reveals that under normal flight conditions, the gradient has a zero expected value and a finite, analytically tractable variance. These characteristics make the method compatible with traditional fault detection approaches. Sufficient tests on real flight data verify its ability to detect hard-to-identify faults.

The use of intelligent connected vehicles (ICVs) is an emerging concept in transportation systems, where the platoon leader processes various types of information from followers and roadside units to make accurate decisions. However, in complex network environments, the leader election process can become complicated and inefficient due to potential network attacks and abnormal node behaviours. To address this challenge, this paper presents a leader vehicle election mechanism based on dynamic trust assessment within a zero-trust framework. The proposed mechanism comprises two key components: dynamic trust assessment and adaptive leader election. The dynamic trust assessment method involves a trust evaluation mechanism composed of direct trust assessment and recommendation trust assessment based on the state information of vehicle nodes, where the dynamic trust assessment results are adjusted according to node behaviour information. The leader election mechanism involves periodic elections among the vehicle nodes on the basis of the results of the dynamic trust assessment, in which the vehicle node with the most votes is selected as the leader. Through comparative experiments against a full trust scenario, the proposed method demonstrates superior performance in resisting network attacks and identifying abnormal nodes, thereby significantly improving the safety, stability, and operational efficiency of the vehicle platoon.

This article investigates the distributed aggregation optimization problem in multiagent systems (MASs), with a particular focus on addressing the aggregation effect commonly encountered in modern engineering and technological applications. In such scenarios, the local objective function of an agent depends not only on its own decision variables but also interacts with the decision variables of other agents, resulting in complex coupling relationships. To solve these challenges while ensuring that the optimization variables satisfy the coupled inequality constraints, this article introduces a novel framework called distributed aggregative parameter projection (DAPP). Specifically, the proposed distributed protocol is based on an improved parameter projection, including two direction updates, which minimizes the cost function and keeps the search direction obeying the inequality at each iteration. In addition, the linear convergence performance of the proposed scheme over the undirected and connected graph is ensured by rigorous theoretical proof with mild assumptions. Finally, simulation results demonstrate the superior performance of DAPP with smaller global function and faster convergence speed in comparison to the existing method.

Most of the past research on platooning control has not considered the impact of changes in the level of trust between vehicles in the platoon on the platoon control, and the communication process between connected vehicles is often subjected to malicious attacks such as time delays or interruptions, as well as tampering with status information, and the level of trust between vehicles changes, which affects the cooperative behaviors such as driving styles and inter-vehicle interval strategies. Changes in the level of trust between vehicles can affect the control process strategy of the vehicle, such as where the spacing strategy changes, thereby affecting the inputs to the controller, which in turn affects the outputs of the controller, and ultimately affects changes in the driving style of the vehicle, so considering the level of trust between vehicles in vehicle platoon control is critical to the safe operation of the vehicle platoon. To address this challenge, this paper proposes a dynamic splitting and merging control strategy for vehicle platoons based on the trust evaluation of vehicle nodes. Firstly, trust is evaluated using the Certainty Factor (C-F) uncertain reasoning model, which is a process that starts from initial evidence of uncertainty and derives reasonable conclusions with a certain degree of uncertainty by utilizing the uncertainty of evidence. An autoregressive model predicts short-term vehicle trajectories, and multi-source information from communication and perception is compared to determine vehicle node trust. Utilizing Bayesian reasoning methods, the trust level of vehicle nodes is updated. Based on traditional platoon control and trust evaluation, a software-level dynamic splitting and merging strategy is proposed to enhance resilience against unknown disturbances in a zero-trust environment. Finally, the system’s internal and string stability are analyzed, and the scheme’s effectiveness is validated through simulations. Note to Practitioners—The motivation of this paper is to solve the problem of vehicle platoon security control for connected vehicles in default distrust scenarios. Previous approaches were designed under the premise of considering mutual trust between vehicle nodes, ignoring the problems of inaccurate information interaction between connected vehicles and attacked interaction processes in real scenarios. To resist the risks of various types of attacks faced by intelligent networked vehicles in real traffic scenarios, this paper designs a vehicle platoon control strategy under zero-trust scenarios. Firstly, using the C-F uncertainty reasoning method, we propose a data-based node trust evaluation algorithm, and utilize its node trust evaluation results to re-establish the vehicle state equation under the zero-trust scenario. To make the vehicle platoon control under the zero-trust scenario scalable and resilient, this paper further proposes a vehicle platoon dynamic splitting and merging strategy based on the vehicle node trust evaluation scheme without changing the original communication topology of the vehicle platoon. The preliminary experimental results show that compared with the previous vehicle platoon control method under default vehicle node trust, the security of vehicle platoon operation is effectively improved. In the future, to better match real traffic scenarios, we will study how to dynamically adjust the trust threshold of vehicle nodes based on the vehicle operation environment, spacing strategy, sensor parameters, and other factors.

This paper presents a robust$H_{\infty}$performance analysis method for an uncertain linear parameter-varying (LPV) system to achieve less conservative disturbance rejections. Specifically, by adopting Lyapunov function, a feedback controller is designed based on linear matrix inequalities (LMIs) framework to eliminate the influences of unknown uncertainties for continuous-time and discrete-time LPV systems. Meanwhile, a convex optimization technique is explored for solving optimization problems of bilinear matrix inequalities (BMIs), in which several sufficient conditions guaranteeing system stability are relaxed. Furthermore, compared with some existing approaches, the superior performances of the presented scheme are demonstrated by rigorous theoretical analysis and numerical simulations on a practical truck-trailer system.Note to Practitioners—This paper is motivated by the problem of minimal control domain range (also called conservativeness) in robust$H_{\infty}$control approaches for LPV systems. Generally, LMI conditions are accompanied by a certain level of conservativeness for the robust stability of LPV systems. However, excessive conservatism makes the proposed control strategy impossible to implement in practical engineering. Considering this gap, we formulate a lemma to reduce the conservativeness in robust stability conditions while dealing with the effects of system uncertainty. Strict theoretical proofs and comparative simulation experiments are also conducted to verify the effectiveness of the proposed robust control scheme in continuous and discrete LPV systems.

In this paper, a novel sliding mode control (SMC) method is designed to investigate the finite-time consensus tracking (FTCT) problem of the second-order leader-following multi-agent systems (MASs) with imprecise communication topology of each agent. First, a T-S fuzzy model is introduced to characterize the inexact communication topology of the leader-following MASs. Moreover, a fuzzy SMC law is designed to ensure the reachability of the constructed sliding mode surface (SMS). Meanwhile, in light of the partitioning strategy, sufficient conditions for FTCT of the leader-following MASs are established. It is worth mentioning that the distributed SMC method proposed in this paper is based on the unknown sliding gain, which can greatly reduce the conservatism. Finally, the simulation study on a group of single-link robots and a numerical simulation demonstrate the feasibility of proposed control method. Note to Practitioners—This paper aims to ensure the transient performance and good robustness of second-order multi-agent systems. Generally, the classic nonlinear control method, sliding mode control (SMC), is accompanied by a certain level of conservativeness due to its fixed sliding gain matrix. Moreover, excessive conservatism may prevent the proposed control strategy from being implemented in practical engineering. To address this issue, we formulate an improved finite-time SMC framework to reduce the conservatiness. We also designed an improved algorithm based on genetic algorithm (GA) and linear matrix inequalities (LMIs) to solve the difficulties brought by the new control algorithm, thereby facilitating its practical implementation and enhancing the overall performance of the second-order multi-agent systems.

Considering the complexity of centralized plant-wide optimization and the presence of communication delay in production units, a distributed asynchronous optimization framework is developed for energy consumption optimization problem during ethylene production. First, the energy consumption optimization problem of ethylene process is formulated into a distributed asynchronous optimization problem. Then, considering multiple production units and the material transfer time involved, each unit is treated as a node, and each node is decomposed into calculation nodes, constraint nodes, and delay nodes. Specifically, each production unit can be optimized asynchronously without the necessity for synchronization. To overcome the impact of communication delay on asynchronous process, state vectors are incorporated into the distributed parameter projection algorithm. Additionally, to ensure that the inseparable coupled constraints between nodes during asynchronous operations are met, the parameter projection algorithm is employed. Numerical experiments and industrial simulations demonstrate the proposed algorithm exhibits faster convergence speeds compared to distributed synchronous algorithms, and the energy consumption is lower than the results obtained by the centralized algorithm. Note to Practitioners—As the scale of ethylene production expands, traditional centralized optimization methods struggle to meet the demands of plant-wide optimization due to their high model complexity and slow convergence rates. In this study, we develop a distributed optimization framework, in which the large-scale complex model is decomposed into small parts, and the global optimization task is accomplished cooperatively through local information exchange. Furthermore, considering the time delay due to material residence time in processing units, a distributed asynchronous parameter projection algorithm is proposed to solve the energy consumption issue in ethylene plant. Experimental results demonstrate that the proposed method is capable of converging to the feasible solution in the presence of time delay. Compared with the existing centralized methods, the proposed asynchronous distributed algorithm exhibits better performance, i.e. lower energy consumption. Besides, the proposed asynchronous distributed optimization framework holds potential for application in other process industries, such as the metallurgical industry and steel production process.

The problem of prescribed performance control (PPC) for the multi-input multi-output block-triangular nonlinear systems under output constraints is investigated in this article. It is focused on the scenario where the references are not known in advance. This renders the related solutions infeasible and becomes more challenging under the totally unknown and inherently nonlinear dynamics of the system. To overcome this challenge, a novel robust decoupling PPC strategy is developed in this article, in which an online boundary generation scheme and a smoothly constraint switching rule are devised and introduced. The resulting controller ensures that the system outputs evolve within their respective constraint bands and track the references with the predetermined overshoot, settling time and accuracy. Moreover, it is independent of function approximation, parameter identification, or disturbance estimation, despite the unbounded nonlinearities, unmatched disturbances and unknown dynamics. A comparative experiment on a 2-DOF serial flexible link robot is conducted to show the efficacy and superiority of our low-complexity high-performance control approach.

Accurately modeling the electrochemical process of large-scale lithium-ion batteries (LLBs), which involves estimating the electrochemical state distributions within the process, is crucial for the design and management of LLBs. A two-dimensional (2-D) physics-based model can describe the electrochemical process of LLBs accurately. However, due to the presence of complex partial differential equations (PDEs), solving the model becomes a challenging task. This article develops a physics-informed composite network (PICN) as a surrogate model of the 2-D physics-based model. Specifically, PICN consists of four deep neural networks (DNNs) to estimate the distributions of four key electrochemical states, respectively. Since the architecture of PICN is inspired by PDE characteristics, it can achieve high accuracies with four lightweight DNNs. Additionally, by incorporating physics and data, PICN achieves accurate estimations using limited data. It can even estimate the electrochemical state distributions that may not be measured directly. Moreover, PICN presents a low-frequency information-based pretraining strategy and a two-stage loss balance strategy to address the convergence failure and loss imbalance that may arise in the training of PICN. PICN is a new attempt to model the electrochemical process of LLBs by integrating physics with data. Extensive experiments show that it is better than state-of-the-art models.

This article is focused on the problem of prescribed performance control (PPC) for the strict-feedback systems under actuator failures with dynamic redundancies and deferred actuator replacement. It is concentrated on the cases where both the multiplicative nonlinearities and the additive nonlinearities of the plant are unknown and the fault-tolerant control (FTC) algorithm is as simple as possible. They render the existing solutions infeasible. In this article, we develop a low-complexity fault-tolerant PPC (FTPPC) approach, which is made up of a nominal controller, a fault detection module, and a reconfigurable controller. It ensures reference tracking with the predetermined speed and accuracy during the fault-free case and recovers the predefined performance after the deferred actuator replacement. The controller does not rely on the specific knowledge about the system dynamics, the disturbances, or the time profile and bound of the actuator failures. Moreover, it obviates the needs for parameter identification, function approximation, command filtering, and disturbance estimation. A comparative simulation on a jet engine compressor is carried out to demonstrate the above theoretical findings.

Predicting epileptic seizures is crucial for reducing patient suffering and informing clinical treatments. However, training supervised models for seizure prediction requires extensive labeled data, which is labor-intensive and costly. We propose a novel Multi-Task Self-Supervised Learning framework with U-Net architecture (MTL-SSU) for EEG-based seizure prediction. Unlike conventional self-supervised methods, MTL-SSU incorporates domain-specific knowledge for epileptic seizure analysis. Leveraging U-Net, the approach performs two key tasks: mimic segmentation and channel discrimination. These tasks enable the model to capture effective feature representations from unlabeled EEG signals. After pretraining, the U-Net encoder and a linear classifier are fine-tuned for seizure prediction. Employing a rigorous k-fold cross-validation strategy on the CHB-MIT database, MTL-SSU achieves 93.55% accuracy and 0.9775 AUC in patient-specific epilepsy prediction. Notably, even in semi-supervised learning scenarios with limited labeled data, MTL-SSU exhibits exceptional performance, highlighting its significant potential for clinical application.

The increasing utilization of wastewater necessitates dedicated attentions to the potential security threats, and formulate strategies for defense, response, and future protection. The nonideal actuator subject to the faults and constraints may underload the driving force and reduce the sewage purification efficiency. This article proposes an adaptive performance self-recovery control strategy for the wastewater treatment process (WWTP) with nonideal actuator. Therein, a Gaussian error function is reconstructed to imitate the asymmetrical actuator constraints. A fault effect identifier is designed to indirectly acquire fault information. Two boundary estimators are co-designed to estimate the infimum of virtual controller gain and the supremum of lumped uncertainty, respectively. The proposed control strategy can largely enhance the faulty performance self-recovery capability of the WWTP, while ensuring robust output regulation and fast convergence. Extensive experiments on dissolved oxygen control are executed on a WWTP platform to show the efficacy of the suggested control scheme.

This article focuses on solving a distributed convex optimization problem of multiagent systems with multiple inequality constraints. Considering communications between agents are prone to failures and not synchronized among themselves in some cases, a novel asynchronous adaptive step sizes-DIGing algorithm is proposed through integrating the projection operators and logic-andframework. In specific, a bilaterally adjustable adaptive step size mechanism is introduced to automatically abandon the irrational evolutionary route which relieves the limitations of traditional DIGing algorithm. By adopting the operator theory, the almost sure convergence of the proposed algorithm under asynchronous communication is proved. Finally, the theoretical and simulation results for the plantwide optimization problem in the ethylene production process illustrate the effectiveness of the proposed algorithm.

This article presents a distributed optimization framework in order to solve the plant-wide energy-saving problem of an ethylene plant. First, the ethylene production process is abstracted into a distributed network, and then, a new distributed consensus algorithm is proposed, which is called adaptive step-size-based distributed proximal consensus algorithm (ASS-DPCA). This algorithm can dynamically adjust the step size and automatically abandon the irrational evolutionary route while eliminating the dependence of optimization algorithms on model gradient information. Moreover, the designed algorithm is able to converge to an optimal solution for any convex cost functions and approach to a convex constraint set of agents over an undirected connected graph. Finally, the results of numerical simulation and industrial experiments show that the algorithm can reduce the total energy consumption of an ethylene plant with less computing time and assured consensus.

This article proposes a distributed optimization approach upon an undirected topology, through only local computation and communication, with the goal of optimizing global function which consists of a host of local functions under complex constraints. In particular, the accelerated distributed Nesterov gradient descent subject to complex constraints (Acc-DNGD-CCs) algorithm is developed for smooth and strongly convex functions. By adopting an estimation mechanism of gradient and only using the history information, the fast optimization of the presented algorithm is ensured. Subsequently, the parameter projection scheme is employed for handling constraints of uncertain parameters introduced by the coupling relationship between the nodes. Meanwhile, the rigorous theoretical proofs along with stability analysis are given to prove the linear convergence of the Acc-DNGD-CC algorithm. Furthermore, compared with some existing algorithms, the superior performances of Acc-DNGD-CC are verified by numerical simulation on a plant-wide ethylene separation optimization process in terms of energy saving.

Plant-wide optimization plays a vital role in improving the overall performance of large-scale industrial processes. Considering the modeling complexity and convergence difficulty of centralized plant-wide optimization, in this article, we propose a distributed framework by decomposing the global optimization problem into a set of subproblems, where multiple local units interact with each other between nodes. According to the proposed framework, plant-wide optimization problem can be effectively solved by distributed optimization. To eliminate the limitations of existing distributed algorithms, we introduce constraint node to describe the inseparable coupled constraints between nodes. By combining Lagrange duality and parameter projection, the proposed algorithm can solve optimization problems with multiple constraints. Taking ethylene production process as an example, the global energy consumption optimization is guaranteed without the whole-process mechanism model. Numerical simulation and industrial experimental results demonstrate that the proposed algorithm can reduce the energy consumption of the entire ethylene process with fewer computation time.