Tube-based model predictive control (TMPC), a well-performed control algorithm in disturbed control scenarios, is widely applied in networked control systems (NCSs). Due to the inherent nature of communication networks, NCSs always are at risk of being attacked. Consequently, the pristine TMPC-based systems may undergo severe destruction. However, the field about resilience of this control method against cyber-attacks in communication links is still in its infancy. In this article, we consider a resilience scheme based on an actuator buffer to eliminate the harmful effects caused by denial-of-service attacks. In addition, we assess the inherent attack tolerance of TMPC by calculating the maximal permissible open-loop steps, the maximal steps with no feedback control while provably maintaining recursive feasibility and input-to-state stability, for arbitrary states. The effectiveness and merits of the proposed resilience scheme and algorithm are demonstrated through numerical comparisons, furthermore, implemented on a three-wheeled omnidirectional robot platform.

This article addresses the challenge of amplitude-unbounded false data injection (FDI) attacks targeting the sensor-to-controller (S–C) channel in cyber-physical systems (CPSs). We introduce a resilient tube-based model predictive control (MPC) scheme. This scheme incorporates a threshold-based attack detector and a control sequence buffer to enhance system security. We mathematically model the common FDI attacks and derive the maximum duration of such attacks based on the hypothesis testing principle. Following this, the minimum feasible sequence length of the control sequence buffer is obtained. The system is proven to remain input-to-state stability (ISS) under bounded external disturbances and amplitude-unbounded FDI attacks. Moreover, the feasible region under this scenario is provided in this article. Finally, the proposed algorithm is validated by numerical simulations and shows superior control performance compared to the existing methods.

This article presents a novel online obstacle avoidance trajectory planning method for autonomous ground vehicles (AGVs) based on long short-term memory-attention (LSTM-Attention) networks. The proposed method can guide AGVs to perform emergency maneuvers when encountering sudden and moving obstacles, while also ensuring high levels of real-time performance and optimality. It consists of two parts: 1) offline training and 2) online planning. In the offline training phase, an AGV obstacle avoidance trajectory dataset is generated using numerical trajectory optimization methods to train the LSTM-Attention network. This training allows the network to capture the mapping between the relative information of the vehicle and the obstacles and the optimal control actions. The trained network is then used for online trajectory planning to achieve optimal feedback obstacle avoidance control for AGVs facing sudden obstacles. Furthermore, to address situations involving sudden obstacles in different directions and moving obstacles, a rotation coordinate system method is proposed, significantly expanding the application scenarios of the proposed approach. The effectiveness and real-time performance of the designed method are comprehensively validated through extensive simulation and physical experiments.

Human-machine mixed traffic (HMMT) where autonomous ground vehicles (AGVs) coexist with human-driven vehicles (HDVs), is expected to be the predominant transportation mode in the foreseeable future. To address the multi-objective real-time lane-change trajectory planning problem for AGVs in HMMT, this study proposes a self-attention guided convolutional neural network-long short-term memory network (CNN-LSTM) based framework. This framework introduces an interval type-2 fuzzy physical programming (IT2FPP) method to iteratively solve the trajectory planning problem under varying HDV driving intentions and constructs a dataset using the corresponding HMMT system states and AGV control actions. IT2FPP can handle uncertainties in all boundaries of the preference function and accommodate fuzzy preferences from multiple decision-makers, overcoming the limitations of type-1 fuzzy physical programming. Additionally, a fitting function-based obstacle avoidance method is proposed to model obstacles in the trajectory planning problem with fitting functions, ensuring collision-free trajectories while improving computational efficiency. Then, a self-attention guided CNN-LSTM network is designed to learn the mapping function between the HMMT system states and AGV control actions, enabling real-time trajectory planning with very low computational burden. The network effectively extracts intrinsic features from time series data, demonstrating high accuracy. As HDV driving intentions are incorporated in the dataset, there is no need for inference when generating AGV trajectories in real-time, further enhancing planning efficiency. The framework effectively addresses the multi-objective real-time lane-change trajectory planning problem for AGVs in HMMT, demonstrating high accuracy, real-time performance, and practical application potential, as validated by simulation and physical experiments.

With the continuous development of Internet of Things (IoT) technology and artificial intelligence (AI) technology, the demand for Artificial Intelligence of Things (AIoT) edge applications is increasing. However, there are challenges in AIoT edge applications, such as limited resources of edge devices, data privacy leakage, inconsistent model deployment, device authentication, and data sharing difficulties, which can affect the security and intelligence level of AIoT edge applications. Therefore, we propose a trusted cloud–edge decision architecture that ensures trustworthy authentication of terminal devices. We use lightweight deep neural network training technology to run multilayer perceptron (MLP) models on resource-limited edge devices, reducing the difficulty of model design and development. We also introduce blockchain technology to enhance the security and privacy of model and data processing. We describe the four-layer architecture and corresponding workflow details, and we introduce the main data models and focus on the core technologies of the architecture. Finally, we completed the simulation verification of the model using carbon emissions data as a sample, demonstrating the feasibility and effectiveness of the model.

In large-scale networks, communication links between nodes are easily injected with false data by adversaries. This paper proposes a novel security defense strategy from the perspective of attack detection scheduling to ensure the security of the network. Based on the proposed strategy, each sensor can directly exclude suspicious sensors from its neighboring set. First, the problem of selecting suspicious sensors is formulated as a combinatorial optimization problem, which is non-deterministic polynomial-time hard (NP-hard). To solve this problem, the original function is transformed into a submodular function. Then, we propose an attack detection scheduling algorithm based on the sequential submodular optimization theory, which incorporates expert problem to better utilize historical information to guide the sensor selection task at the current moment. For different attack strategies, theoretical results show that the average optimization rate of the proposed algorithm has a lower bound, and the error expectation is bounded. In addition, under two kinds of insecurity conditions, the proposed algorithm can guarantee the security of the entire network from the perspective of the augmented estimation error. Finally, the effectiveness of the developed method is verified by the numerical simulation and practical experiment.

This paper focuses on the trajectory planning problem in highly complex and irregular parking lot scenarios. We formulate this problem as an optimal control problem (OCP) according to the requirements of the task and propose a planner to solve it numerically. To ensure optimal planning results, the proposed planner consists of three procedures, which are responsible for generating the guiding route, constructing the collision-free tunnel, and optimizing the trajectory, respectively. Firstly, we use the generalized Voronoi graph (GVG) to build an equidistant roadmap and propose an improved adaptive A* algorithm (IAA*) to improve the time efficiency of the guiding route generation process. Secondly, we employ a coarse trajectory to guide a homotopic route and replace the intractably scaled collision-avoidance constraints with within-tunnel constraints, which are small-scale and independent of the environment’s complexity. Our tunnel construction method ensures the integrity of the free spaces, so that repeated construction is not necessary. Finally, in the process of trajectory optimization, we propose a lightweight OCP iterative solution framework to search for the optimal solution with high computational efficiency, in which the customized OCP with only box constraints is quickly solved in each iteration. Besides, the theoretical analysis, numerical simulations, and real-world experiments had been carried out to demonstrate the effectiveness and efficiency of the method.

In this paper, we present the Multi-Center Privacy-Preserving Network (MP-Net), a novel framework designed for secure medical image segmentation in multi-center collaborations. Our methodology offers a new approach to multi-center collaborative learning, capable of reducing the volume of data transmission and enhancing data privacy protection. Unlike federated learning, which requires the transmission of model data between the central server and local servers in each round, our method only necessitates a single transfer of encrypted data. The proposed MP-Net comprises a three-layer model, consisting of encryption, segmentation, and decryption networks. We encrypt the image data into ciphertext using an encryption network and introduce an improved U-Net for image ciphertext segmentation. Finally, the segmentation mask is obtained through a decryption network. This architecture enables ciphertext-based image segmentation through computable image encryption. We evaluate the effectiveness of our approach on three datasets, including two cardiac MRI datasets and a CTPA dataset. Our results demonstrate that the MP-Net can securely utilize data from multiple centers to establish a more robust and information-rich segmentation model.

In this paper, a novel integrated real-time trajectory planning and tracking control framework capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is presented. In the motion planning component, a newly-proposed idea of utilizing deep neural networks (DNNs) for approximating optimal parking trajectories is further extended by taking advantages of a recurrent network structure. The main aim is to fully exploit the inherent relationships between different vehicle states in the training process. Furthermore, two transfer learning strategies are applied such that the developed motion planner can be adapted to suit various AGVs. In order to follow the planned maneuver trajectory, an adaptive learning tracking control algorithm is designed and served as the motion controller. By adapting the network parameters, the stability of the proposed control scheme, along with the convergence of tracking errors, can be theoretically guaranteed. In order to validate the effectiveness and emphasize key features of our proposal, a number of experimental studies and comparative analysis were executed. The obtained results reveal that the proposed strategy can enable the AGV to fulfill the parking mission with enhanced motion planning and control performance.Note to Practitioners—This article was motivated by the problem of optimal automatic parking planning and tracking control for autonomous ground vehicles (AGVs) maneuvering in a restricted environment (e.g., constrained parking regions). A number of challenges may arise when dealing with this problem (e.g., the model uncertainties involved in the vehicle dynamics, system variable limits, and the presence of external disturbances). Existing approaches to address such a problem usually exploit the merit of optimization-based planning/control techniques such as model predictive control and dynamic programming in order for an optimal solution. However, two practical issues may require further considerations: 1). The nonlinear (re)optimization process tends to consume a large amount of computing power and it might not be affordable in real-time; 2). Existing motion planning and control algorithms might not be easily adapted to suit various types of AGVs. To overcome the aforementioned issues, we present an idea of utilizing the recurrent deep neural network (RDNN) for planning optimal parking maneuver trajectories and an adaptive learning NN-based (ALNN) control scheme for robust trajectory tracking. In addition, by introducing two transfer learning strategies, the proposed RDNN motion planner can be adapted to suit different AGVs. In our follow-up research, we will explore the possibility of extending the developed methodology for large-scale AGV parking systems collaboratively operating in a more complex cluttered environment.

This article studies the problem of trajectory optimization for autonomous ground vehicles with the consideration of irregularly placed on-road obstacles and multiple maneuver phases. By introducing a series of event sequences, a new multiphase constrained optimal control formulation is constructed to describe the automatic overtaking process. Although existing trajectory optimization techniques can be applied to address the constructed problem, they may suffer from poor or premature convergence issues due to the complexity of the mission formulation. Thus, to offer an effective alternative, a novel desensitized trajectory optimization method is designed and implemented to explore the optimal overtaking maneuver for the AGVs. The proposed method applies a double layer structure, where an enhanced intelligent optimization method is used in the outer layer such that the main inner optimization routine can be boosted by starting at a better reference solution. The algorithm convergence as well as the solution optimality conditions are theoretically analyzed. Numerical results are provided to illustrate the validity of the established formulation. Comparative case studies were executed to demonstrate the quality of the obtained solution and the enhanced performance of the proposed trajectory optimization method.

In this article, the tracking problem of networked discrete-time second-order nonlinear multiagent systems (MASs) is studied. First, for the MASs without communication delay, a novel method, called distributed model-free sliding-mode control algorithm is proposed, which can make the system converge quickly without the accurate model. Furthermore, for the MASs with delay, in order to eliminate the influence of time delay on the system, a distributed model-free sliding-mode predictive control strategy based on time-delay compensation technology is proposed, which can actively compensate for time delay while ensuring system stability and consensus tracking performance requirements. Both the simulation and experiment results reveal the superiority of the proposed methods.

This article focuses on the design, test, and validation of a deep neural network (DNN)-based control scheme capable of predicting optimal motion commands for autonomous ground vehicles (AGVs) during the parking maneuver process. The proposed design utilizes a multilayer structure. In the first layer, a desensitized trajectory optimization method is iteratively performed to establish a set of time-optimal parking trajectories with the consideration of noise-perturbed initial configurations. Subsequently, by using the preplanned optimal parking trajectory data set, several DNNs are trained in order to learn the functional relationship between the system state-control actions in the second layer. To obtain further improvements regarding the DNN performances, a simple yet effective data aggregation approach is designed and applied. These trained DNNs are then utilized as the motion controllers to generate feedback actions in real time. Numerical results were executed to demonstrate the effectiveness and the real-time applicability of using the proposed control scheme to plan and steer the AGV parking maneuver. Experimental results were also provided to justify the algorithm performance in real-world implementations.

Constrained autonomous vehicle overtaking trajectories are usually difficult to generate due to certain practical requirements and complex environmental limitations. This problem becomes more challenging when multiple contradicting objectives are required to be optimized and the on-road objects to be overtaken are irregularly placed. In this article, a novel swarm intelligence-based algorithm is proposed for producing the multiobjective optimal overtaking trajectory of autonomous ground vehicles. The proposed method solves a multiobjective optimal control model in order to optimize the maneuver time duration, the trajectory smoothness, and the vehicle visibility, while taking into account different types of mission-dependent constraints. However, one problem that could have an impact on the optimization process is the selection of algorithm control parameters. To desensitize the negative influence, a novel fuzzy adaptive strategy is proposed and embedded in the algorithm framework. This allows the optimization process to dynamically balance the local exploitation and global exploration, thereby exploring the tradeoff between objectives more effectively. The performance of using the designed fuzzy adaptive multiobjective method is analyzed and validated by executing a number of simulation studies. The results confirm the effectiveness of applying the proposed algorithm to produce multiobjective optimal overtaking trajectories for autonomous ground vehicles. Moreover, the comparison to other state-of-the-art multiobjective optimization schemes shows that the designed strategy tends to be more capable in terms of producing a set of widespread and high-quality Pareto-optimal solutions.

Summary Class imbalance makes traditional intrusion detection system have low detection rate (DR) and high false positive rate (FR) for minority class, which is unsuitable for practical needs. In order to improve the DRs and reduce FRs of minority classes, we propose a novel intrusion detection method, which combines convolutional neural networks (CNNs) algorithm with threshold modification method based on receiver operating characteristic (ROC) curve. In this method, we use CNNs as a classifier and modify threshold of the classifier through ROC curve. In addition, NSLKDD dataset and UNSW‐NB15 dataset have been carried out to evaluate the performance of this method. The experimental results illustrate that the proposed method has a better performance no matter in improving DRs or reducing FRs of minority classes.

The objective of this paper is to present an approximation-based strategy for solving the problem of nonlinear trajectory optimization with the consideration of probabilistic constraints. The proposed method defines a smooth and differentiable function to replace probabilistic constraints by the deterministic ones, thereby converting the chance-constrained trajectory optimization model into a parametric nonlinear programming model. In addition, it is proved that the approximation function and the corresponding approximation set will converge to that of the original problem. Furthermore, the optimal solution of the approximated model is ensured to converge to the optimal solution of the original problem. Numerical results, obtained from a new chance-constrained space vehicle trajectory optimization model and a 3-D unmanned vehicle trajectory smoothing problem, verify the feasibility and effectiveness of the proposed approach. Comparative studies were also carried out to show the proposed design can yield good performance and outperform other typical chance-constrained optimization techniques investigated in this paper.

Highly constrained trajectory optimization problems are usually difficult to solve. Due to some real-world requirements, a typical trajectory optimization model may need to be formulated containing several objectives. Because of the discontinuity or nonlinearity in the vehicle dynamics and mission objectives, it is challenging to generate a compromised trajectory that can satisfy constraints and optimize objectives. To address the multiobjective trajectory planning problem, this paper applies a specific multiple-shooting discretization technique with the newest NSGA-III optimization algorithm and constructs a new evolutionary optimal control solver. In addition, three constraint handling algorithms are incorporated in this evolutionary optimal control framework. The performance of using different constraint handling strategies is detailed and analyzed. The proposed approach is compared with other well-developed multiobjective techniques. Experimental studies demonstrate that the present method can outperform other evolutionary-based solvers investigated in this paper with respect to convergence ability and distribution of the Pareto-optimal solutions. Therefore, the present evolutionary optimal control solver is more attractive and can offer an alternative for optimizing multiobjective continuous-time trajectory optimization problems.

This brief presents an integrated trajectory planning and attitude control framework for six-degree-of-freedom (6-DOF) hypersonic vehicle (HV) reentry flight. The proposed framework utilizes a bilevel structure incorporating desensitized trajectory optimization and deep neural network (DNN)-based control. In the upper level, a trajectory data set containing optimal system control and state trajectories is generated, while in the lower level control system, DNNs are constructed and trained using the pregenerated trajectory ensemble in order to represent the functional relationship between the optimized system states and controls. These well-trained networks are then used to produce optimal feedback actions online. A detailed simulation analysis was performed to validate the real-time applicability and the optimality of the designed bilevel framework. Moreover, a comparative analysis was also carried out between the proposed DNN-driven controller and other optimization-based techniques existing in related works. Our results verify the reliability of using the proposed bilevel design for the control of HV reentry flight in real time.

In this paper, a constrained space maneuver vehicles trajectory optimization problem is formulated and solved using a new three-layer-hybrid optimal control solver. To decrease the sensitivity of the initial guess and enhance the stability of the algorithm, an initial guess generator based on a specific stochastic algorithm is applied. In addition, an improved gradient-based algorithm is used as the inner solver, which can offer the user more flexibility to control the optimization process. Furthermore, in order to analyze the quality of the solution, the optimality verification conditions are derived. Numerical simulations were carried out by using the proposed hybrid solver and the results indicate that the proposed strategy can have better performance in terms of convergence speed and convergence ability when compared with other typical optimal control solvers. A Monte-Carlo simulation was performed and the results show a robust performance of the proposed algorithm in dispersed conditions.

This paper proposes a two-stage optimization framework for generating the optimal parking motion trajectory of autonomous ground vehicles. The motivation for the use of this multilayer optimization strategy relies on its enhanced convergence ability and computational efficiency in terms of finding optimal solutions under the constrained environment. In the first optimization stage, the designed optimizer applies an improved particle swarm optimization technique to produce a near-optimal parking movement. Subsequently, the motion trajectory obtained from the first stage is used to start the second optimization stage, where gradient-based techniques are applied. The established methodology is tested to explore the optimal parking maneuver for a car-like autonomous vehicle with the consideration of irregularly parked obstacles. Simulation results were produced and comparative studies were conducted for different mission cases. The obtained results not only confirm the effectiveness but also reveal the enhanced performance of the proposed optimization framework.

In this paper, we predeploy a large number of smart agents to monitor an area of interest. This area could be divided into many Voronoi cells by using the knowledge of Voronoi diagram and every Voronoi site agent is responsible for monitoring and tracking the target in its cell. Then, a cooperative relay tracking strategy is proposed such that during the tracking process, when a target enters a new Voronoi cell, this event triggers the switching of both tracking agents and communication topology. This is significantly different from the traditional switching topologies. In addition, during the tracking process, the topology and tracking agents switch, which may lead the tracking system to be stable or unstable. The system switches either among consecutive stable subsystems and consecutive unstable subsystems or between stable and unstable subsystems. The objective of this paper is to design a tracking strategy guaranteeing overall successful tracking despite the existence of unstable subsystems. We also address extended discussions on the case where the dynamics of agents are subject to disturbances and the disturbance attenuation level is achieved. Finally, the proposed tracking strategy is verified by a set of simulations.