Within the framework of differential graphical games, this paper investigates the leader-following output consensus problem for fully heterogeneous multiagent systems, where each follower incorporates both the influence of its own policy on its utility and that of its neighbors’ policies. To achieve output consensus, we design a novel fully distributed estimator-based control protocol without requiring prior knowledge of global network topology. Specifically, the fully distributed estimator is formulated through a graphical game perspective, modeling agent-neighbor interactions as zero-sum conflicts, to overcome the fragility of cooperative estimation in adversarial or resource-constrained environments. By employing min-max strategies, each agent can robustly optimize its local estimation policy against neighbors’ worst-case policies. To demonstrate the applicability of this approach to intelligent transportation networks, we develop a corresponding fully distributed platooning control algorithm that ensures coordinated motion of heterogeneous connected vehicles. Finally, numerical simulations and real-world experiments validate the effectiveness of the proposed protocols.

The problem of reinforcement learning (RL)-based fuzzy control for nonlinear systems with unknown dynamics via parallel composite policy iteration (PCPI) scheme is studied in this article. The main objective of this article is to solve the fuzzy algebraic Riccati equation (FARE), which is inherently complex and cannot be easily solved by traditional mathematical formulas. Policy iteration (PI) and value iteration (VI) algorithms proposed have been widely used to address this problem. However, these algorithms have the disadvantages of an initial stabilizing control policy, the persistent excitation (PE) condition, and huge amounts of data. To effectively alleviate these drawbacks, a novel PCPI algorithm is proposed in this article. Specifically, for each fuzzy subsystem, an adaptive parameter is designed to eliminate the requirement of an initial stabilizing control policy. In addition, an online model-free PCPI algorithm is proposed for the situation where the dynamic information of the fuzzy system is difficult to obtain. By substituting the stored historical data with online data, the PE condition is relaxed to the initial excitation (IE) condition. Concurrently, the corresponding algorithm can be executed independently and concurrently under each fuzzy rule, thereby fully exploiting the available computational resources. Finally, the effectiveness of the algorithms set forth in this article is verified through a single-link robot arm and quarter-car active suspension (QCAS) experiment.

This article investigates a two-person zero-sum game for sequential decision making processes with dynamically evolving performance preferences. First, we incorporate this problem within a game theoretic framework, and establish the existence and uniqueness conditions for the temporal equilibrium strategy. Moreover, we present a compact formulation for one player's best response to the other's strategy. Furthermore, the specific convergence rate is achieved through the design of utility parameters. Finally, we validate our approach on the optimal liquidation and competitive supply chain models.

This article addresses the distributed formation control problem for safety-certified uncertain multirobot systems with minimum restrictive connectivity maintenance (MRCM). Compared to this problem with deterministic models, it presents inherently more challenging requirements for safety and stability analysis. An integrated quadratic programming controller incorporating input-to-state safety control barrier functions (ISSf-CBFs) is developed to simultaneously enforce safety certification and MRCM. This controller builds upon a nominal design leveraging input-to-state stability and ISSf lemmas. The derived ISSf-CBFs provide formal guarantees for both safety certification and MRCM under uncertainties. The key to obtaining this controller lies in constructing ISSf-CBFs that enable safety certification and MRCM, from which the corresponding constraints are formally derived. Unlike existing methods, the strategy maximizes formation performance by executing formation tasks at full capability whenever possible, and only modifying the nominal controller when safety or MRCM constraints are violated. Finally, simulation and experiment validate the effectiveness of the proposed controller.

This paper investigates path-guided cooperative circumnavigation control for autonomous vehicles with collision avoidance. Compared to the trajectory-guided control frameworks, which require vehicles to track time-dependent trajectories, the proposed method eliminates temporal constraints but introduces new design challenges. Coordination error variables based on separation angles and path information are defined, and a temporally unconstrained guidance law incorporating linear and angular velocities is proposed. The developed guidance law is inspired by chase-and-wait strategies, achieving structural simplicity. To address collision avoidance involving both inter-vehicle interactions and external obstacles for autonomous vehicles under model uncertainties and environmental disturbances, an input-to-state safe control barrier function (ISSf-CBF) is employed. The constructed ISSf-CBF systematically maps state-space safety constraints to control input constraints, thereby ensuring collision-free operations in dynamic environments. The key to achieving robust collision-free path-guided circumnavigation lies in synthesizing an input-to-state safety-certified controller by defining ISSf-CBF constraints. Simulation results verify the validity and effectiveness of the theoretical results.

This paper addresses data-driven injection attack against unknown intelligent automation systems (IASs) with slowly time-varying delays, which is a more general but also more challenging to deal with than the model-based real-time systems. Using the control input and system output measurements, several new data-driven injection attack strategies based on compact form dynamic linearization (CFDL) and incremental triangular dynamic linearization (ITDL) are proposed. The attack strategies are more general than the existing ones, taking into account the unknown model parameters and time-varying delays in control-to-actuator as well as sensor-to-controller data transmission channels. Consequently, the new design attack results are anticipated to have wider applicability. Based on the established attack models of CFDL and ITDL, the data-driven optimal parameter estimation algorithms are employed to overcome the difficulty of the unknown model. Furthermore, with the help of the principle of the linear regression equation, the problem of seeking partial derivatives for the attack inputs with time delays is avoided. Several examples are presented to illustrate the validity of the designed attack strategies.Note to Practitioners—The primary objective of this paper is to focus on the cyber security of discrete-time intelligent automation systems from the viewpoint of the attacker, which provides insight into the way of generating attack strategies under the data-driven framework. The majority of the available attack strategies against intelligent automation systems are based on a priori knowledge of the system, without delays or with single-sided fixed time delay, and thus in reality the attack strategies fail or are mostly ineffective due to inaccessibility of the attacked system parameters in conjunction with communication network time delays. This paper synthesizes parameter estimation, dynamic linearization, optimality principle and control theory to propose data-driven injection attack strategies based on CFDL and ITDL, respectively. Compared with the CFDL-based attack strategy, the latter is more applicable in the situation where the attacker suffers from restricted memory space occupation and hashrate. Specifically, the operation of attacker has two phases: eavesdropping estimation and strategy generation. In the eavesdropping estimation phase, the attacker continuously eavesdrops and stores the traffic data of the system for parameter estimation as well as optimizes the estimated parameters. In the strategy generation phase, the attacker generates strategies with the assistance of optimal parameters. Mathematically analytical forms of the proposed two data-driven injection attack strategies are presented in detail. Then, their effectiveness is verified through numerical simulation and experiments based on a leader-following form consisting of five quadcopters, but not tested in production. In the future, we are intending to design the data-driven attack strategies for heterogeneous intelligent automation systems with time-varying delays under the framework of game.

Coalition structure is an effective cooperation architecture for task implementation in the field of multi-robot systems. Nevertheless, the presence of uncertainty inherently complicates the decision-making process for robots and may potentially result in suboptimal coordination. To tackle this challenge, this paper considers an uncertain multi-robot coalition formation scenario in which task information (the types of all tasks) is incompletely known to robots. Given the local beliefs of robots, the problem of multi-robot coalition formation under uncertainty is formulated as a coalition formation game. In this game, each robot is a rational and self-interested player and tends to join a coalition according to its preference. A polynomial-time coalition formation algorithm is proposed to identify the social agreement, i.e., Nash stable partition, among the robots. The convergence of the proposed algorithm is strictly guaranteed as long as the communication topology of the considered system is strongly connected. The coalition formation game is then extended to a dynamic game, and we propose a belief updating algorithm that enables robots to update their beliefs as the game is played repeatedly. Simulation results demonstrate the effectiveness of our proposed algorithms, and the robots will eventually learn the true type of each task.Note to Practitioners—The work reported in this article will be beneficial for deploying multi-robot systems to support cooperative surveillance applications. In these scenarios, robots can form stable coalitions to perform surveillance tasks, even when the task information is incompletely known due to sensor noise or limited sensor range. The problem of multi-robot coalition formation is known to be NP-hard, which becomes even more challenging when uncertainty is taken into account. This paper proposes game-based algorithms to solve the problem of multi-robot coalition formation under uncertainty. Some practical schemes are introduced as benchmarks to further illustrate the performance improvement brought by our proposed algorithms. The proposed algorithms are further evaluated through a real-world experiment, demonstrating their effectiveness for practical application.

This paper investigates anticipatory decision for unknown distributed systems with robustness concerns. Anticipatory decision focuses on action selection before observations appear at temporal scales. Firstly, anticipatory decision forms sequential feedback with min-max performance guarantees, while causality comes from time series analysis. Next, distribution, robustness and time consistency partition the optimization into spatial and temporal sub-games. The spatial sub-games dispel conflicts on distribution and robustness, while the temporal ones ensure stability and performance through time consistency. Finally, we propose a multi-step reinforcement learning algorithm under causality analysis and game theoretical framework. Numerical results demonstrate the effectiveness of the approach, and practical experiments show potential real-world applications. Note to Practitioners—This framework focuses on anticipatory decision for distributed systems, which suffer from distributed communication, unknown dynamics, environmental disturbances and state observation loss. Our framework has various application scenarios, e.g., internal surgical robots, low-light autonomous driving and non-GPS navigation, and these scenarios mainly involve dynamic environments and weak signal feedback. For example, decision-making in autonomous driving requires not only reacting to current environmental conditions but also anticipating future scenarios and uncertainties due to poor visibility. Most results deal these issues with model-driven approaches, while unknown dynamics render these methods inapplicable. For implementation, we propose a multi-step reinforcement learning algorithm for anticipatory decision framework with stability and robustness guarantees, and details mainly contain three parts: 1) We collect data during offline phase, and form the data structure, namely, current-next observation pair with multi-step decision and accumulated reward; 2) Strategies and value functions are approximated with neural networks through Monte-Carlo methods; 3) The strategy is deployed as sequential feedback in practical systems, and predicts multi-step decisions with single-step state observation. Finally, we select robot consensus with optical sensors as the implementation demo.

This paper aims to predict future optimal control inputs for unknown continuous-time linear systems. In contrast to most existing approaches that generate control input without observation loss, the proposed control scheme analyzes causality between observation and sequence feedback from the time-delay series model, allowing for the existence of observation loss. These time-delay series can be viewed as multiplayer games over a temporal scale, then a temporal game-theoretic approach ensures system stability and performance. By integrating the Bellman principle, a data-driven adaptive dynamic programming algorithm is proposed to avoid system knowledge. Furthermore, the designed parallel data-driven predictive algorithm reduces the computational complexity. Finally, the applicability and effectiveness of the methodology are demonstrated through numerical simulations and practical experiments. Note to Practitioners—This paper mainly concerns the predictive control of unknown continuous systems, which suffer from unknown dynamics and state observation loss. The proposed methods are suitable for weak information feedback and dynamically changing scenarios, such as autonomous driving in low visibility scenarios and endoscopic surgical robots. Most of the current processing methods are model-driven, which makes them unsuitable for unknown system dynamics changes. To address this issue, we propose a time-delay switched strategy for control prediction with stability and optimality guarantees. The practical application can be divided into three parts: i) Data collection: Collect historical multi-intervals accumulated inputs and outputs data, the amount of data should reach the requirement of the full rank of the data matrix; ii) Iterative learning: the optimal control strategy is learned from the historical data through an adaptive dynamic programming method; iii) Deployment: Control intervals are extended through temporal time-delay feedback on historical state trajectory, and length trigger conditions will switch these feedbacks logically for actuators. Finally, the Quanser QBot 2e robot is used as a demonstration example.

Nonlinear affine systems with relative degree two widely exist in the control field, and the unified output tracking control approach of these systems is still an open problem. This paper presents a formal framework for output tracking control design methodology for nonlinear affine systems with relative degree two. Specifically, a control barrier function is constructed to enforce the forward invariance or the stability of a set contained within a 0-sublevel set of the first-order derivative of pre-stablished Lyapunov function, thereby stabilizing the system output to the desired value. Additionally, by taking the first-order derivative of the elaborated control barrier function, control input terms with a nonzero coefficient are introduced into the stability condition, addressing the weakness of controller design using conventional control Lyapunov function approaches, which fails in states where the coefficient of the control input term is zero. Furthermore, the stability conditions are proposed in the form of linear inequality constraints involving control inputs, with which a collection of admissible controllers is provided. Then, by resorting to the pointwise minimum norm (PMN) technique, a numerical solution of an admissible controller can be derived in the context of a quadratic program. We finally demonstrate the effectiveness of the controller design method via two simulations, adaptive cruise control and vehicle lateral control.

In complex and variable terrains, affine formation control, with its flexible formation adjustment ability, can achieve various formation shapes to adapt well to the environment. Notably, the existing affine formation research based on stress matrices require the variation parameters for translation, rotation, scaling, and shearing of formations to be predesigned offline. To address this, we propose a novel method for online affine parameter adjustment that enables self-reconfiguration of formations in multiobstacle environments. By adopting artificial potential field environment excitation, the proposed motion planning algorithm can dynamically adjust the affine transformation parameters online, and realize the self-reconfiguration of formation shape to avoid collision. Then, a distributed model predictive controller is proposed for multiagent systems, which actively utilizes historical control input information to flexibly adjust controller performance while avoiding algebraic loops between neighboring agent controllers. The algorithm separates stability and performance optimization within the nonlinear model predictive control framework, ensuring both the feasibility and stability of the underlying optimization. Finally, the simulation results confirm the effectiveness of the proposed controller.

This article investigates the pursuit-evasion games for target capture in multiagent systems. To address this challenge, a novel data-driven optimal control policy is proposed, leveraging off-policy reinforcement learning and Nash-minmax strategies. First, a comprehensive framework for multiagent pursuit-evasion games is developed, modeled as a two-layer game structure. In this framework, interactions among agents within the same team are characterized as nonzero-sum games, while interactions between opposing teams are adversarial and treated as zero-sum games. Second, Nash-minmax strategies are introduced to solve the formulated multiagent pursuit-evasion games. These strategies effectively derive distributed Nash solutions for agents within the same team and adversarial worst-case policies for agents in opposing teams. Furthermore, to eliminate the reliance on prior knowledge of agent dynamics and initial stabilizing control gains, a data-driven optimal control policy is designed, ensuring the achievement of target capture. Finally, a numerical example is provided to demonstrate the effectiveness and practical applicability of the proposed approach.

This article considers the formation control of multiple autonomous aerial vehicles (AAVs), where the AAVs operate in dynamic environments with multiple obstacles and narrow (constrained) areas. A new fuzzy model predictive interval output-constrained maneuver formation control method for AAVs is proposed. The interval output constraint algorithm utilizes the distance information to implement output constraints and formation changes, which are more practical but more challenging than the output constraint problem based on user-assigned settling time, especially in narrow areas. One of the distinctive advantages of the proposed output-constrained model predictive controller is that it can activate formation changes and output constraints for AAVs when passing through narrow space, and it can automatically restore the formation of AAVs and deactivate the output constraints when the AAVs are far away from the narrow space. Unlike most nonlinear model predictive control strategy, where predictive control relies on the accurate underlying dynamical system, the article introduces adaptive fuzzy updating law to receding horizon optimization algorithm to estimate and compensate unknown dynamics and external disturbances. Two potential field functions are designed to safely track in 3-D environments with obstacles. Finally, several examples are provided to illustrate the effectiveness of the proposed controller.

In this paper, we introduce a novel hierarchical decision-making framework that integrates control barrier functions (CBFs) with reinforcement learning (RL) to enhance the safety and efficiency of autonomous vehicle merging at on-ramps. The proposed technique is comprised of three layers: a behavioral planning layer that employs proximal policy optimization (PPO) algorithm aims to learn its driving behaviors, which are not necessarily compliant with vehicle dynamics; a motion control layer that adopts model predictive control (MPC) framework to ensure the kinematic feasibility to follow the trajectories with heading angles and velocities provided by the upper layer; and lastly, a control barrier function-guided layer, which is the core innovation of this paper, is implemented. It leverages the forward invariance of control barrier functions to design a safety controller that refines the output of MPC to maintain continuous driving safety. The refined results are then used as objective function, guiding subsequent updates in the learning process. Simulation results demonstrate that the proposed CBF-guided architecture significantly improves training efficiency and performance, achieving a 60% reduction in training time and 6% increase in success rate compared to standard RL methods.

Human-robot skill transfer is an important means for robots to learn skills and has received more and more attention and research in recent years. Typically, to ensure effective skill transfer, a skill is demonstrated several times by a human, from which a robot learns the features contained in the demonstrations and reproduces the skill in a new environment. However, it is necessary to consider the cases such as errors in human demonstrations and sensor issues, resulting in imperfect demonstrations, unrelated data, information loss, and variations in the lengths and amplitudes of the demonstrations. Therefore, this brief proposes a new trajectory alignment and filtering method for extracting relatively useful information from multiple demonstrations. This method can be used in conjunction with most probabilistic movement learning methods (this brief uses probabilistic movement primitives (ProMPs) as an example) for learning from demonstrations (LfDs), so that the robot can eventually learn and generate trajectories for completing skills from multiple demonstrations of varying quality. The effectiveness of the proposed method is verified by simulation results.

This article investigates robust predictive control problem for unknown dynamical systems. Since the dynamics unavailability restricts feasibility of model-driven methods, learning robust predictive control (LRPC) framework is developed from the aspect of time consistency. Under feedback-like control causality, the robust predictive control is then reconstructed as spatial-temporal games, and we guarantee stability through time-consistent Nash equilibrium. For gradation clarity, our framework is specified as four-follow contents. First, multistep feedback-like control causality is drawn from time series analysis, and Takens' theorem provides theoretical support from steady-state property. Second, control problem is reconstructed as games, while performance and robustness partition the game into temporal nonzero-sum subgames and spatial zero-sum ones, respectively. Next, multistep reinforcement learning (RL) is designed to solve robust predictive control without system model. Convergence is proven through bounds analysis of oscillatory value functions, and properties of receding horizon are derived from time consistency. Finally, data-driven implementation is given with function approximation, and neural networks are chosen to approximate value functions and feedback-like causality. Weights are estimated with least squares errors. Numerical results verify the effectiveness.

This article investigates the optimal group consensus problem (GCP) in multiagent systems (MASs). To address this problem, a novel distributed optimal control policy is designed in the framework of off-policy reinforcement learning (RL). First, a framework for multiagent differential graphical games is formulated. Second, a min-max strategy is then introduced to ensure the achievement of group consensus through a data-driven value iteration (VI) approach. Finally, the presented consensus control policy is extended to address the group formation tracking problem (GFTP) of nonholonomic mobile robots, with a numerical example to illustrate the efficacy of the proposed results. Compared with the existing literature, this article has the following contributions: 1) A group of agents are decomposed into multiple subgroups to accomplish different consensus objectives; 2) the prior knowledge of agents’ dynamics and initial stabilizing control gains can be eliminated; and 3) the performance index function (PIF) for each agent is designed to integrate not only its individual control policy but also that of its neighboring agents.

This article investigates the problem of finite-horizon optimal fault-tolerant control for heterogeneous nonlinear discrete-time multiagent systems. A distributed optimal fault estimation and optimal fault-tolerant control scheme is presented based on the Stackelberg game and graphical game frameworks, in which agent systems and corresponding fault estimators are treated asthe “leader” and “follower” in the Stackelberg game, and the agent systems constitutes a graphical game. This is more general than the existing integration design of fault estimation as well as fault-tolerant control, as it better reveals the interaction between the agent system and its fault estimator. To achieve interactive Stackelberg equilibrium, an auxiliary controller for fault estimator is designed, which exhibits non cooperative properties with the fault-tolerant controller of the agent. Then, a finite-time distributed estimator for discrete-time case is developed for the first time to rapidly estimate the matrix and state of the leader agent. The coefficient matrices of the considered fault signals obey a hidden semi-Markov switching rule, which can encompass the existing actuator and sensor fault models. Further, two critic neural networks are established to obtain approximate solutions of interactive Stackelberg equilibrium online by employing the adaptive dynamic programming technology. Finally, simulation examples are provided to verify the efficacy and applicability of the proposed scheme.

This paper proposes a GPU-accelerated optimization framework for collision avoidance problems where the controlled objects and the obstacles can be modeled as the finite union of convex polyhedra. A novel collision avoidance constraint is proposed based on scale-based collision detection and the strong duality of convex optimization. Under this constraint, the high-dimensional non-convex optimization problems of collision avoidance can be decomposed into several low-dimensional quadratic programmings (QPs) following the paradigm of alternating direction method of multipliers (ADMM). Furthermore, these low-dimensional QPs can be solved parallel with GPUs, significantly reducing computational time. High-fidelity simulations are conducted to validate the proposed method’s effectiveness and practicality.

This paper aims to investigate the security problem of nonlinear cyber-physical systems (CPSs), which poses a challenge to handle compared with linear CPSs. A series of optimization problems for nonlinear CPSs under injection attack are constructed, which are based on a general model of the nonlinear systems with repetitive operation characteristics and a novel introduction of the key technical lemma. These optimization results are more general than the existing injection attack results and the requirements for attackers to obtain system information are relaxed. Also, the form of switching applied to the attack strategy possesses several advantages, including high stealthiness, lower cost, and more flexibility. Therefore, the new optimal injection attack strategies are expected to be more widespread and provide a basis for the design of defense strategies. The key to acquiring the designed optimal attack strategies is to adopt the linear input/output (I/O) data model for these systems via introducing an estimation term of the improved projection estimation method into the linear model. Finally, a networked GLUON-6L3 manipulator example validates the effectiveness of the proposed methods. Note to Practitioners—The main purpose of this paper is to study the cyber security of nonlinear cyber-physical systems from the perspective of attackers, which can help defenders fully understand the behavior of attackers. Most of the existing attack strategies aimed at linear systems or have a priori knowledge of the attacked systems. However, there are great limitations and difficulties in practical application. In this paper, the new attack strategies are proposed by combining system identification, iterative learning and control theory, which relaxes the requirement of the attacker’s ability. In detail, the attacker only needs to obtain the control input and output data of the attacked system and design the attack strategy according to it. Among them, the first-in, first-out queue storage method is applied to remove the requirements for the storage capability of the attacker. In practical applications, the attacker does not need to store the I/O data continuously, but only store the initial value and the data of two successive iterations. The mathematical analytic forms of two optimal attack strategies are given, and then their effectiveness are verified on a welding work of a networked six-axis manipulator. In the future, we will investigate the design of attack strategies for nonlinear systems with heterogeneous dynamics and multiple delays.

Distributed machine learning has emerged as a promising data processing technology for next-generation communication systems. It leverages the computational capabilities of local nodes to efficiently handle large datasets, creating highly accurate data-driven models for analysis and prediction purposes. However, the performance of distributed machine learning can be significantly hampered by communication bottlenecks and node dropouts. In this article, a novel unmanned aerial vehicle (UAV)-enabled hierarchical distributed learning architecture is proposed to support machine learning applications, e.g., regional monitoring. Multiple UAV receivers (URs) are introduced as wireless relays to improve the communication between the UAV transmitters (UTs) and the cloud server. Our objective is to identify the optimal UT-UR association to maximize the social welfare of the network, which is distinctly different from the existing works that focus on the unilateral profit-maximizing problem. We formulate a two-side many-to-one matching game to model the UT-UR association problem, and a two-phase many-to-one matching algorithm is designed to identify the stable matching. The validity of our proposed scheme is verified through in-depth numerical simulations.

In this paper, the cooperative output regulation problem is investigated for heterogeneous nonlinear multi-agent systems allowing frequent arrivals and departures of agents. Inspired a generally switched systems with multi modes and multi dimensions, the migration of agents is described by a switched communication network, where the number of agents and the graph of topology could be simultaneously changed at the switching instant. In this paper, this switching topology is modeled as a quasi-mode-dependent persistent dwell time switched system with size-varying modes for more generalization. Furthermore, the assumption of existing a directed spanning tree can be removed during the special time interval with a limit on the maximum dwell time, which makes the stability analysis for such switching directed topology more challenging. Hence, a quasi-time-dependent Lyapunov function is utilized to construct a novel distributed reference generator, which can approach the exosystem in a globally uniformly exponential manner. And based on the Takagi-Sugeno fuzzy set theory, a fuzzy cooperative controller is developed for each agent to track the estimating signal. Finally, a multi-pendulum system is adopted to illustrate the feasibility and applicability of the derived results.

This work investigates stability and localizability of local-bearing-based multiagent systems without common orientation in the presence of measurement noise, which are more general but also more challenging to deal with than global-bearing-based multiagent systems under ideal environment. Based on local-bearing unbiased estimator constructed from the historical information and a newly designed time-varying gain, a robust prescribed-time orientation estimation algorithm is proposed to ensure that the local reference frame of the follower agent is aligned with the global one. The local bearing information is more easily obtained than global one. Therefore, the new orientation estimation result is expected to be more widely applicable. The robust orientation estimation algorithm is then applied to the problem of localization estimation, and a prescribed-time distributed localization estimation algorithm is developed. The distinctive advantage of this work is that only the local bearing information is used, the fast and controllable localization estimation is achieved. The global convergence is derived based on the cascade system. Some simulation and experiment results are provided to prove the effectiveness of the proposed estimation algorithms.

This article investigates optimal control for a class of large-scale systems using a data-driven method. The existing control methods for large-scale systems in this context separately consider disturbances, actuator faults, and uncertainties. In this article, we build on such methods by proposing an architecture that accommodates simultaneous consideration of all of these effects, and an optimization index is designed for the control problem. This diversifies the class of large-scale systems amenable to optimal control. We first establish a min-max optimization index based on the zero-sum differential game theory. Then, by integrating all the Nash equilibrium solutions of the isolated subsystems, the decentralized zero-sum differential game strategy is obtained to stabilize the large-scale system. Meanwhile, by designing adaptive parameters, the impact of actuator failure on the system performance is eliminated. Afterward, an adaptive dynamic programming (ADP) method is utilized to learn the solution of the Hamilton-Jacobi-Isaac (HJI) equation, which does not need the prior knowledge of system dynamics. A rigorous stability analysis shows that the proposed controller asymptotically stabilizes the large-scale system. Finally, a multipower system example is adopted to illustrate the effectiveness of the proposed protocols.

This article is concerned with the real-time localization problem for the dynamic multi-agent systems with measurement and communication noises under directed graphs. The barycentric coordinates are introduced to describe the relative position between agents. A novel robust distributed localization estimation algorithm based on iterative learning is proposed. The relative-distance unbiased estimator constructed from the historical iterative information is used to suppress the measurement noise. The designed stochastic approximation method with two iterative-varying gains is used to inhibit the communication noise. Under the zero-mean and independent distributed conditions on the measurement and communication noises, the asymptotic convergence of the proposed methods is derived. The numerical simulation and the QBot-2e robot experiment are conducted to test and verify the effectiveness and the practicability of the proposed methods.

Understanding 3-D scene geometry from videos is a fundamental topic in visual perception. In this article, we propose an unsupervised monocular depth and camera motion estimation framework using unlabeled monocular videos to overcome the limitation of acquiring per-pixel ground-truth depth at scale. The photometric loss couples the depth network and pose network together and is essential to the unsupervised method, which is based on warping nearby views to target using the estimated depth and pose. We introduce the channelwise attention mechanism to dig into the relationship between channels and introduce the spatialwise attention mechanism to utilize the inner-spatial relationship of features. Both of them applied in depth networks can better activate the feature information between different convolutional layers and extract more discriminative features. In addition, we apply the Sobel boundary to our edge-aware smoothness for more reasonable accuracy, and clearer boundaries and structures. All of these help to close the gap with fully supervised methods and show high-quality state-of-the-art results on the KITTI benchmark and great generalization performance on the Make3D dataset.

This article investigates the formation tracking-containment control problem for multiple unmanned aerial vehicles (UAVs), while considering collision avoidance in the three-dimensional (3-D) environment. A distributed model predictive formation control method is developed for UAVs with obstacle avoidance. Unlike most existing trajectory tracking control schemes, the proposed method is derived from a smooth shifting function and a distributed Lyapunov-based model predictive controller, together with two collision-free functions, to obtain an improved control algorithm with the following characteristics: 1) the swarm system of UAVs realizes the cooperative hunting process from tracking formation to containing formation, fencing a target to their convex hull; 2) the Lyapunov-based model predictive formation control method inherits the stability properties of backstepping technique and adopts the receding horizon optimization of model predictive control (MPC) technique; and 3) by exploiting the Lyapunov-based MPC algorithm and collision-free functions, the swarm system of UAVs can avoid collision with obstacles or collision with each other in 3-D scenarios with multiple obstacles. Finally, the simulation and comparison results confirm that the proposed controller outperforms the traditional backstepping controller in terms of safety and tracking performance.

This paper addresses the bearing-only distributed localization, which is more accurate and more reliable than traditional localization technologies. A new bearing-only distributed localization framework is proposed to solve localization problems for both static sensor networks and mobile multi-agent systems (MASs). Based on the complex coordinate representation, a novel bearing-only distributed localization algorithm for sensor networks is proposed. The algorithm combines the orientation estimation and the position estimation to solve the orientation alignment problem, which makes the compasses no longer needed for the localized networks. The localization for the mobile MASs is also studied and the corresponding localization algorithm is designed, which is more general and more challenging. The key to obtaining positions of moving agents, is the velocity estimator in the proposed localization algorithm which makes the estimated positions and velocities converge to the true value simultaneously. A distinctive advantage of the localization framework proposed in this paper is that the bearing-only localization algorithms can be applied to more general systems, such as sensor networks and MASs. Simulations and experiment are presented to verify the proposed algorithms.

This article investigates the output consensus problem of heterogeneous linear multiagent systems under directed communication graphs. A novel adaptive dynamic event-triggered mechanism is proposed, intending to further save system resources consumed in communication between agents and controller update of agents themselves, and remove the assumption that the global information associated with the communication topology should be known in advance for the design of control parameters. Unlike the existing related adaptive event-triggered algorithms, the proposed algorithm could theoretically guarantee the existence of a strictly positive constant on the interevent time intervals for both communication between agents and controller update. Furthermore, it is shown by the simulation that the addition of a dynamic variable has the potential to further optimize the control cost when compared to the addition of exponential function signal or$L_{1}$signal usually adopted in existing adaptive event-triggered mechanisms. Then, the obtained results are extended from the strongly connected graph to the directed communication graph only containing a spanning tree. Finally, numerical simulation results are conducted to demonstrate the effectiveness of the proposed mechanism.

In this article, a model-free predictive control algorithm for the real-time system is presented. The algorithm is data driven and is able to improve system performance based on multistep policy gradient reinforcement learning. By learning from the offline dataset and real-time data, the knowledge of system dynamics is avoided in algorithm design and application. Cooperative games of the multiplayer in time horizon are presented to model the predictive control as optimization problems of multiagent and guarantee the optimality of the predictive control policy. In order to implement the algorithm, neural networks are used to approximate the action-state value function and predictive control policy, respectively. The weights are determined by using the methods of weighted residual. Numerical results show the effectiveness of the proposed algorithm.

A connected vehicle platoon with unknown input delays is studied in this article. The control objective is to stabilize the connected vehicles, ensuring all vehicles are traveling at the same speed while maintaining a safety spacing. A decentralized control law using only onboard sensors is designed for the connected vehicle platoon. A novel switching-type delay-adaptive predictor is proposed to estimate the unknown input delays. By using the estimated unknown input delays, the control law can guarantee the stability of the successive vehicles. The platoon control adopts a one-vehicle look-ahead topology structure and a constant time headway (CTH) policy, which makes the desired spacing between vehicles vary with time. In this framework, the stability of the connected vehicles can be derived through the analysis of each pair of two successive vehicles in the platoon. Finally, an example is presented to illustrate the applicability of the obtained results.

For time-varying delayed nonlinear systems with parameter uncertainties and persistent disturbances, an online and an offline robust fuzzy model predictive control algorithms are proposed in this article. Both methods guarantee the input-to-state stability of the system. Furthermore, a novel alternative optimization (AOP) approach and a dual-mode optimal control (OP)/AOP strategy are proposed to prevent the performance deterioration of the online OP approach due to the challenges in addressing the bilinear matrix inequality (BMI) constraints. With the established AOP and dual-mode OP/AOP techniques, the optimization problem constrained by BMIs is transformed into convex, and a significantly more precise approximation of the ellipsoidal minimal robust positively invariant set can be calculated. Besides, the system can eventually converge into a more compact ellipsoidal set. These two alternative optimization methods can be easily extended to various nonlinear systems. A numerical example and a continuous-time stirring tank example are provided to validate the effectiveness and advantages of the established methodologies.

This article studies the asynchronous sliding mode control for nonlinear Markov jump systems (NMJSs), whose transition probabilities is piecewise-constant and subjected to the persistent dwell-time switching rule. By resorting to the interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy set theory, the nonlinearity and uncertainty features of the investigated systems are modeled. Taking the uncertain membership functions (MFs) of IT2 T-S fuzzy systems into consideration, the nonparallel distributed compensation strategy is employed for designing the MFs of the controller such that they do not need to be obtained in time. Furthermore, in view of the difficulty of accurately acquiring the mode of NMJSs with complex transition probabilities, the hidden Markov model is employed to provide a detected mode for constructing the sliding mode law. Afterward, a novel fuzzy sliding surface is proposed, and an asynchronous IT2-fuzzy-model-based sliding mode control law is designed for forcing the closed-loop systems to move on the predefined sliding surface. Thereafter, a novel two-mode-dependent Lyapunov-like function is conceived for the studied systems, where modes of NMJSs and transition probabilities are permitted to change simultaneously. By applying an improved technique to handle the parameteric matrix inequalities, some less-conservative sufficient conditions in the form of linear matrix inequality are obtained for ensuring the stochastic exponentially asymptotical stability of the sliding mode with$H_\infty$performance. Finally, a mass-spring mechanical system is presented to substantiate the validity of the proposed main results.

This article investigates the real-time localization problem of dynamic multiagent systems with repetitive operation characteristics under directed graph. A distributed localization estimation algorithm based on iterative learning is proposed. The barycentric coordinates calculated based on the relative distance are used to estimate the real coordinates of the agent. Different from the traditional estimation methods along the time axis, the proposed method utilizes the information of iteration axis simultaneously. In this method, the current estimation coordinates are updated by using the estimation coordinates of the same sampling time in previous iteration, the estimation accuracy is improved, and the velocity constraint is removed. Additionally, the real-time localization problem of dynamic multiagent systems under arbitrary deployment is concerned. An improved distributed localization estimation algorithm with signed coefficients based on iterative learning is proposed. Meanwhile, the results are also extended to the localization estimation of multiagent systems with arbitrary deployment in 3-D space. By introducing Richardson iteration and infinite norm, the global asymptotic convergence of the proposed methods is guaranteed. Finally, numerical simulations and the Qbot-2e robot experiment are provided to show the effectiveness and validity of the obtained results.

This article studies the decentralized control of large-scale systems with sparsity and communication delays. The large-scale system is defined over a directed connected graph and the information structure is partially nested. Based on the decomposition of the noise history, the optimal problem of the overall large-scale system can be decomposed into independent subproblems. Hence, the data-driven decentralized control method is investigated to find the optimal controllers using adaptive dynamic programming (ADP), which could release the dependence on the knowledge of model. In addition, state feedback and output feedback policy iteration algorithms are developed, respectively. Rigorous stability analysis shows that the proposed algorithms can stabilize the large-scale systems asymptotically. Finally, the effectiveness of the proposed theoretical methods is demonstrated by the application of heavy duty vehicle (HDV) platooning.

The concepts between Internet of Things (IoT) and edge computing are increasingly intertwined, as an edge-computing architecture generally comprises a (large) number of diverse IoT devices. This, however, increases the potential attack vectors since any one of these connected IoT devices can be targeted to facilitate other malicious cyber activities. Physical attacks are generally harder to mitigate and less studied, in comparison to their cyber counterparts. Thus, in this article we present an attack framework targeting true random number generators (TRNGs), which are a key component in cryptosystems for edge devices. We then demonstrate how such a framework can guide our investigation of a commercial ASIC chip that runs ring-oscillator-based TRNG. Specifically, we show that our template power attack, low voltage fault attack, and voltage glitch fault attack do not require prior knowledge of the TRNG implementation.

Real-time semantic segmentation aims to generate high-quality prediction in limited time. Recently, with the development of many related potential applications, such as autonomous driving, robot sensing and augmented reality devices, semantic segmentation is desirable to make a trade-off between accuracy and inference speed with limited computation resources. This paper introduces a novel effective and light-weighted network based on Separable Pyramid Module (SPM) to achieve competitive accuracy and inference speed with fewer parameters and computation. Our proposed SPM unit utilises factorised convolution and dilated convolution in the form of a feature pyramid to build a bottleneck structure, which extracts local and context information in a simple but effective way. Experiments on Cityscapes and Camvid datasets demonstrate our superior trade-off between speed and precision. Without pre-training or any additional processing, our SPMNet achieves 71.22% mIoU on Cityscapes test set at the speed of 94 FPS on a single GTX 1080Ti GPU card.

The problem of the stochastic event-based distributed fusion estimation for a class of Gaussian systems is investigated. Considering the deterministic event-triggers destroying the Gaussian property of system states, the stochastic event-triggered mechanisms (SETMs) are used, which also can relieve the network transmission burden. Under the stochastic transmission schedules, a two-step fusion estimation method is developed. The first step, with the consideration of channel fading, the local estimation of each sensor is proposed by using the measurements from itself and its neighbors. The second step, the fusion algorithm is designed to eliminate the disagreements among local estimations of each sensor. Finally, experiment is carried out to demonstrate the advantages of the proposed distributed fusion estimation.

In this article, the output synchronization problem of passive multiagent systems (MASs) with transmission delays and switching graphs is addressed by a novel logic-based distributed switching mechanism. Our result shows that synchronization is reached for arbitrarily large and bounded constant, time varying, or distributed delays, which, compared with the existing results for passive MASs, has an obvious advantage. This delay robustness holds under the very weak connectivity assumptions on the underlying graph, that is, as long as the graph is uniformly jointly strongly connected and switches with a dwell time. The proposed algorithm is applied to the position synchronization problem of multiple robotic manipulators to show its applicability.

This article investigates the consensus problem of general linear multiagent systems under directed communication graphs with event-triggered mechanisms. First, a novel distributed static event-triggered mechanism with a state-dependent threshold is proposed to solve the consensus problem, both with a positive lower bound on the average time interval of the communication among agents and updates of controllers. Thus, the Zeno behavior is excluded for communication among agents and controller updates. Next, to further reduce the frequencies of communication among agents and updates of controllers, a distributed dynamic event-triggered mechanism is introduced. By applying the static and dynamic mechanisms, the problem can be addressed with the reduced use of system resources compared with that in most existing control algorithms. Finally, numerical simulations are presented to verify the effectiveness of the results.

In this article, the cooperative output regulation problem of heterogeneous multiagent systems has been investigated. It is assumed that only a few agents could know the system matrix of the exosystem and no agent knows the topological information. Under these conditions, a novel distributed online algorithm is proposed to estimate the information relevant to the topology. Based on this algorithm, a distributed event-triggered adaptive observer is designed such that each agent can observe the exosystem. It is theoretically shown that the proposed distributed controller will make the multiagent system achieve cooperative output regulation asymptotically. Finally, a simulation is presented to show the effectiveness of the result.

In this article, the problem of the distributed$H_\infty$filtering in finite-time horizon is investigated for a class of Takagi–Sugeno (T-S) fuzzy systems with sensor saturation and unknown bounded noise. Considering the network bandwidth limitation, a dynamic event-triggered mechanism (ETM) is proposed. Due to an asynchronous problem of premise variables induced by the dynamic ETM, the partition approach is applied. Under the partition region, a novel piecewise T-S fuzzy distributed$H_\infty$filtering is derived. By constructing the Lyapunov function, sufficient conditions for the finite-time$H_\infty$performance of the estimation error system are given. Furthermore, to minimize the interference from disturbance to distributed filtering, the optimal problem of disturbance attenuate parameter$\gamma$is solved. Finally, a simulation example is presented to verify the effectiveness of the proposed algorithm.

This article investigates the optimally distributed consensus control problem for discrete-time multiagent systems with completely unknown dynamics and computational ability differences. The problem can be viewed as solving nonzero-sum games with distributed reinforcement learning (RL), and each agent is a player in these games. First, to guarantee the real-time performance of learning algorithms, a data-based distributed control algorithm is proposed for multiagent systems using offline system interaction data sets. By utilizing the interactive data produced during the run of a real-time system, the proposed algorithm improves system performance based on distributed policy gradient RL. The convergence and stability are guaranteed based on functional analysis and the Lyapunov method. Second, to address asynchronous learning caused by computational ability differences in multiagent systems, the proposed algorithm is extended to an asynchronous version in which executing policy improvement or not of each agent is independent of its neighbors. Furthermore, an actor-critic structure, which contains two neural networks, is developed to implement the proposed algorithm in synchronous and asynchronous cases. Based on the method of weighted residuals, the convergence and optimality of the neural networks are guaranteed by proving the approximation errors converge to zero. Finally, simulations are conducted to show the effectiveness of the proposed algorithm.

This article proposes a unified framework to investigate the leader-following and leaderless consensus problem of general linear multiagent systems under the directed communication topology only containing a spanning tree. To further reduce the use of resources for the control objective, an energy-saving control algorithm is introduced composed of double dynamic event-triggered mechanisms that work independently: one intends to control the communication of agents with their neighbors and the other to decide the update of controllers. It is shown that the control algorithm performs well compared to most existing control algorithms in terms of the communication cost between agents and the update cost of controllers. In addition, a new procedure to choose parameters is obtained by the developed Lyapunov stabilty method, with the potential of achieving less conservative parameters than related results. Finally, the effectiveness of the proposed control scheme is verified by numerical simulations.

In this article, the problem of a nonlinear system states estimation with multisensor stochastic scheduling is investigated. In order to solve the non-Gaussian property induced by the nonlinear transformation, the unscented transformation (UT) technique is applied. Since the sensor networks channel is limited, the stochastic event-triggered mechanisms (SETMs) are proposed to reduce the network transmission burden. Under the SETMs, the modified unscented Kalman filter is proposed. Additionally, the sufficient conditions are given to guarantee the stabilities of the error covariance and the estimation error. Finally, extensive examples are carried out. Performances evaluation and comparison with existing methods are given to demonstrate the superiority of the proposed methods.

This paper studies the fixed-time distributed localization problem for directed network based on bearing measurements. The orientation of the global coordinate system is not available to nodes whose local coordinate systems do not to be aligned. First, the barycentric coordinate representation is obtained relying only on the bearing information. Then, a fixed-time distributed network localization algorithm is proposed. By applying the proposed algorithm, the localization problem is converted to the consensus tracking problem for localization errors. When nodes distribution and communication topology meet the requirements, one can prove that the position estimates can convert to the truth after a fixed time. Finally, the simulation verifies the validity of the algorithm.

This paper is concerned with the stability of the vehicular platoon consisting of a leader and multiple cooperative autonomous driving followers. The objective of the platoon control is to ensure all vehicles traveling at the same speed while maintaining a safety spacing. To achieve the objective, a novel control framework consisting of the distributed adaptive event-triggered observer and the car-following control protocol is proposed for the vehicular platoon control. The condition that only few following vehicles can access the information of the leader is considered. In order to design controllers while avoid using any global information, such as the system matrix and the state of the leader, a distributed event-triggered observer is proposed, such that each vehicle can observe the dynamics of the leader. Based on this observer, both collision avoidance and limited communication source of each vehicle are simultaneously considered in the design of the observer. It is shown that under the proposed control framework, the platoon can achieve asymptotical stable, meanwhile, the amount of transmission data and communication cost among vehicles can be reduced. Finally, numerical simulations are presented to show the applicability of the obtained results.

By incorporating a logic-based switching mechanism into high-gain linear controllers, global output-feedback adaptive stabilization is achieved for a class of nonlinear systems with unknown input-output-dependent lower-triangular growth rate and uncertain control coefficient. When a controller candidate, associated with a Lyapunov candidate, is connected into the closed-loop, the logic unit constantly supervises the change rate of the Lyapunov candidate. If it does not decrease as rapidly as predicted, another controller candidate will be switched in to replace the former one. It is theoretically proved that there is only a finite number of switching times, and asymptotic stability of the closed-loop system is guaranteed. Compared with existing results, the logic-based switching adaptive control approach can tolerate strong input-output-dependent nonlinearities, as well as large uncertainties from the system dynamics including the control coefficient. Finally, a numerical example is provided to illustrate the feasibility and the effectiveness of the proposed approaches.

This paper investigates the distributed formation control problem of multiple nonholonomic wheeled mobile robots within real environments. The formation pattern of the system adopts leader-follower structure and the communication topology among the multirobot system is modeled by a directed graph. Slippage is hard to avoid due to the possible existence of ice, sand, or muddy roads. To overcome the effect of slippage, an adaptive trajectory tracking controller for leader robot is designed, such that the leader robot can keep up with the virtual reference trajectory and estimate the real value of unknown slipping ratio accurately. In addition, it is difficult for each follower robot to obtain leader's states in a large formation system, so distributed formation controllers are designed based on distributed observers. It is shown that the proposed controllers can realize formation objective and overcome the slippage constraints at the same time. Finally, the effectiveness of the proposed controllers is verified by simulation results.

In this paper, the distributed remote state estimation problem for conditional dynamic linear systems in mobile sensor networks with an event-triggered mechanism is investigated. The distributed mixture Kalman filtering method is proposed to track the state of the maneuvering target, which uses particle filtering to estimate the nonlinear variables and apply Kalman filtering to estimate the linear variables. An event-based distributed filtering scheme is designed, which is an energy-efficient way to transmit data between sensors and estimators. In addition, by using the mutual information theory, an optimal control problem is formed to control the position of sensors so that the target tracking process can be achieved quickly. Finally, a simulation example about the maneuvering target tracking is provided to corroborate the effectiveness of the filtering method and the control performance for sensors.

This paper is concerned with the adaptive backstepping control problem for a cloud-aided nonlinear active full-vehicle suspension system. A novel model for a nonlinear active suspension system is established, in which uncertain parameters, unknown friction forces, nonlinear springs and dampers, and performance requirements are considered simultaneously. In order to deal with the nonlinear characteristics, a backstepping control strategy is developed. Meanwhile, an adaptive control strategy is proposed to handle the uncertain parameters and unknown friction forces. In the cloud-aided vehicle suspension system framework, the adaptive backstepping controller is updated in a remote cloud based on the cloud storing information, such as road information, vehicle suspension information, and reference trajectories. Finally, simulation results for a full vehicle with 7-degree of freedom model are provided to demonstrate the effectiveness of the proposed control scheme, and it is shown that the addressed controller can improve the performances more than 80% compared with passive vehicle suspension systems.

Autonomous vehicles require a collision-free and comfortable motion trajectory at every time instant. It is a common method to generate a feasible path to the target state and append an optimization-based method for path postprocessing. In this paper a Hybrid A* based motion planning algorithm is presented for autonomous vehicles under unstructured circumstances. Firstly, the Hybrid A* algorithm is improved with a better heuristic function and a better search policy to realize a less-time consuming graph search in consideration of vehicle's motion model. Then, nonlinear optimization algorithm is applied to optimize the generated path further and realizes high quality of security and smoothness of the discrete trajectory. Finally, Catmull-Rom interpolation is combined to make waypoints continuous and easy-to-control. Simulation results concerning different tasks are described to demonstrate the validity of the proposed algorithm.

This paper addresses the distributed adaptive event-triggered H∞filtering problem for a class of sectorbounded nonlinear system over a filtering network with timevarying and switching topology. Both topology switching and adaptive event-triggered mechanisms (AETMs) between filters are simultaneously considered in the filtering network design. The communication topology evolves over time, which is assumed to be subject to a nonhomogeneous Markov chain. In consideration of the limited network bandwidth, AETMs have been used in the information transmission from the sensor to the filter as well as the information exchange among filters. The proposed AETM is characterized by introducing the dynamic threshold parameter, which provides benefits in data scheduling. Moreover, the gain of the correction term in the adaptive rule varies directly with the estimation error and inversely with the transmission error. The switching filtering network is modeled by a Markov jump nonlinear system. The stochastic Markov stability theory and linear matrix inequality techniques are exploited to establish the existence of the filtering network and further derive the filter parameters. A co-design algorithm for determining H∞filters and the event parameters is developed. Finally, some simulation results on a continuous stirred tank reactor and a numerical example are presented to show the applicability of the obtained results.

This paper is concerned with the target tracking problem over a filtering network with dynamic cluster and data fusion. A novel distributed consensus-based adaptive Kalman estimation is developed to track a linear moving target. Both optimal filtering gain and average disagreement of the estimates are considered in the filter design. In order to estimate the states of the target more precisely, an optimal Kalman gain is obtained by minimizing the mean-squared estimation error. An adaptive consensus factor is employed to adjust the optimal gain as well as to acquire a better filtering performance. In the filter's information exchange, dynamic cluster selection and two-stage hierarchical fusion structure are employed to get more accurate estimation. At the first stage, every sensor collects information from its neighbors and runs the Kalman estimation algorithm to obtain a local estimate of system states. At the second stage, each local sensor sends its estimate to the cluster head to get a fused estimation. Finally, an illustrative example is presented to validate the effectiveness of the proposed scheme.

The Narrow-Band Internet of Things network (NB-IoT), which solves the communication problem for IoT, is growing mature rapidly and has become commercialized gradually. Network operators are investigating in the new technique. Compared with the traditional short range wireless communication technique, the entity model of the NB-IoT network is more complicated. Research on the security characteristics and the means of attack in the NB-IoT network is not sufficient by now. Problems such as effective binding between the components of the user equipment, loss or forgery of user equipment, illegal closure of the data security features, intercept of wireless communication, illegal acquisition of user privacy data still exist in the ubiquitous network. During the large scale application of such networks, numerous attacks on the user equipment and the network will occur and will cause serious security risk. Physical Unclonable Function (PUF) is the unique identity of a chip based on its physical characteristics. In this paper we first give a summery analysis of the security risks in the NB-IoT network. Then we propose the chip binding and anti-counterfeiting technique by having the PUF integrated in the chip of the NB-IoT user equipment. Considering the low power requirement of NB-IoT, we design a secure communication protocol based on PUF. Comparing with the PKI mechanism, the protocol simplifies the distribution of secret keys and certificates. Comparing with the TLS protocol, the proposed protocol simplifies the key agreement process and can still keep the high security level.

In this paper, we use the combination of graph theory and consensus algorithm to realize the formation control of nonholonomic wheeled robots. A novel edge-weight function is designed so that the desired formation-shape can be achieved. In addition, the consensus problem is usually solved on the assumption that the robots are modeled as particle model, but this assumption is not suitable when we are dealing with real robots, so we design a novel algorithm to meet the nonholonomic constraints of wheeled robots. Finally, the effectiveness of the proposed method is verified by the simulation results of an example.

Monocular ORB-SLAM has been proved to be one of the best open-source SLAM method. However, it is still unsatisfying especially in low illumination indoor environment, which is caused by scale recovery and wrong feature matching. In this paper, we proposed a vehicle model based monocular ORBSLAM method supplemented by April-Tag to improve the performance of original algorithm. This approach is practical when autonomous driving in low-light and less-feature environment like garages and tunnels. We achieve this by proposing a vehicle model based initialization method fusing April-Tag measurement to recover scale. During tracking procedure, the outliers ORB feature points will be removed by checking reprojection error calculated from April-Tag. In addition, considering vehicle model can only obtain 2D motion, the vertical transition is estimated from camera model. Afterwards, a local Bundle Adjustment(BA) is applied to optimize camera pose both from frame to frame and frame to keyframe which will reduce accumulative error of the vehicle model. Finally, a convincing result is obtained from the testing drive in a garage.

In this paper, an approach for real time obstacle avoidance of autonomous vehicles is presented. A model predictive control (MPC) scheme based on convex quadratic programming (CQP) is developed to generate safety trajectories. To reduce the computational burden in optimizing the performance index of MPC, linear time-varying MPC is adopted and a unique single dimension artificial potential fields (SDAPF) method to utilize the obstacle information is proposed. Autonomous vehicles with proposed method can track the desired path if there is no obstacle on it and avoid both static and dynamic obstacles if the path is occupied. Simulation results show the validity of the approach and its superior real time performance, which is critical to autonomous vehicles.

In this paper, an adaptive output feedback control strategy is presented to solve the stabilization problem of nonholonomic systems in chained form with strong nonlinear drifts and uncertain parameters using output signals only. The control law is developed using input-state scaling and backstepping techniques. The objective is to design adaptive nonlinear output feedback laws which can steer the closed-loop systems globally converge to the origin, while the estimated parameters remain bounded. An adaptive output feedback controller is proposed for a class of uncertain chained systems. Simulation results demonstrate the effectiveness of the proposed controllers.

Adaptive smart neural network controller design is presented in this paper for wheeled mobile robots with unknown dynamics. The controller is constructed at the dynamical level. The smart neural control scheme is designed such that the current control action not only can utilize the knowledge that neural networks learned from the past experience, but also keep the learning ability in the operational phase and finish the same control task in a 'smarter' way. The proposed neural control scheme can act smartly in the operational phase after the networks have been well trained in the training phase, in a way similar to the control process of human in learning to accomplish some complicated control tasks. All the system states are shown to be able to track the desired trajectory. Numerical simulation is conducted to verify the effectiveness of the proposed method

In this paper, robust adaptive control strategy is presented for a wheeled mobile robot in the presence of model perturbations that violates the nonholonomic assumption. The nonholonomic constraint of the vehicle is assumed to be violated by an unknown slippage. Consequently, a perturbed kinematic model of the system is obtained. Using backstepping, the proposed controller is constructed at the dynamical level. The robust adaptive controller is to eliminate the needs for the LIP form of the system dynamics and the exact bounds of the system dynamics. All the system states are shown to be able to track the desired trajectory. The simulation results demonstrate the effectiveness of the proposed controllers.

In this paper, robust adaptive neural network (NN) control is presented to solve the control problem of nonholonomic systems in chained form with unknown virtual control coefficients and strong drift nonlinearities. The robust adaptive NN control laws are developed using state scaling and backstepping. Uniform ultimate boundedness of all the signals in the closed-loop are guaranteed, and the system states are proven to converge to a small neighborhood of zero. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. The proposed adaptive NN control is free of control singularity problem. An adaptive control based switching strategy is used to overcome the uncontrollability problem associated with x0 (t0) = 0. The simulation results demonstrate the effectiveness of the proposed controllers.

Model-free controllers are presented for multi-link smart materials robots. The controllers are derived from the basic energy-work relationship in the absence of the system model which is complex and difficult. To obtain for multi-link smart materials robots. The smart materials bonded along the links are used to apply additional control to suppress the residue vibration effectively. One can achieve not only the closed-loop stability of the original system, but also the asymptotic stability of the truncated system, which is obtained through representing the deflection of each link by an arbitrary finite number of flexible modes. Simulation results are provided to show the effectiveness of the presented approach.