This article investigates distributed Nash equilibrium (NE) seeking for multirobot systems with nonlinear dynamics, time-varying disturbances, and individual inequality constraints under switching communication topologies. A novel control architecture is developed by integrating adaptive radial basis function (RBF) neural networks with projection-based pseudogradient dynamics. The proposed method enables each robot to estimate and track its local NE strategy in a fully distributed manner, without requiring global information or prior knowledge of the disturbances. Unlike existing methods that rely on static graphs or known disturbance bounds, our approach ensures constraint satisfaction and disturbance rejection simultaneously under a jointly strongly connected switching network. Numerical simulations involving five 2-degrees of freedom robotic manipulators demonstrate the effectiveness of the proposed strategy, achieving convergence to the NE within 20 s, strict adherence to inequality constraints.

Dynamic behaviors of the classical Kuramoto models have been widely studied. The dynamics of the all-to-all connected oscillator Ising machines (OIMs) is similar to that of the classical Kuramoto models, with the main difference being that there is an additional term in OIMs, called the second harmonic term. However, the dynamic behavior of an all-to-all connected OIM is significantly different and its intricate properties are largely unexplored. In this article, we study in detail the properties of the all-to-all connected OIMs and explore their application as associative memory. The number of patterns such an OIM can store increases exponentially with respect to the number of oscillators. To improve the performance of the OIMs for associative memory, we propose a new harmonic term so that the resulting OIM achieves pattern retrieval with high accuracy in the presence of a high level of noise.

In this study, we present a method for online estimation of the mean performance output in microgrid systems subject to high-dimensional and dynamic uncertainties. We integrate an efficient Multivariate Probabilistic Collocation Method (MPCM) based sampling strategy with a Copula-based conditional probability distribution. This integrated method enables online evaluation of system outputs with high estimation accuracy and efficiency. The online evaluation algorithm is developed, and its theoretical analysis is provided. Real Time Digital Simulator (RTDS) experiments validate the method, demonstrating its feasibility for practical applications.

In this paper, we investigate the problem of reference tracking for a class of linear systems subject to actuator saturation and propose a global model recovery anti-windup (MRAW) strategy with prescribed performance. We adopt the classic MRAW framework, where one of the outputs of the anti-windup compensator is regarded as the tracking error, reflecting the difference between the unconstrained system and the saturated system. Then, instead of employing the commonly usedL2gain, we present the prescribed performance functions (PPFs) to characterize the real-time tracking error such that the transient performance can be captured more specifically. In particular, to avoid singular issues arising from the occurrence of saturation, we modify the existing PPFs with an auxiliary system when saturation occurs. Based on these modified performance functions, we follow a modified performance control approach and design the remaining output that is injected into the anti-windup activation module. Such a design procedure is constructive, making it conducive to its extension to nonlinear systems. Theoretical results establish the boundedness of all signals in the closed-loop system. Simulation results verify the effectiveness of our design strategy.

In this paper, within the prescribed performance control framework, we revisit the output tracking problem of a nonlinear system with input saturation, user-specified performance and external disturbances. To handle the intrinsic conflict between actuator saturation and performance requirements, we introduce a novel auxiliary system, which is driven by an artificial saturation nonlinearity, to generate modification signals for relaxing the prescribed performance functions. To further alleviate the adverse effects caused by actuator saturation, we incorporate a new performance-based anti-windup compensator into the control framework. In particular, the auxiliary system is designed to adjust the external performance requirements, and the anti-windup compensator is used for improving the internal saturation handling capability. Moreover, we employ a predetermined weighting coefficient to balance between the auxiliary system and the anti-windup compensator for better control performance. To deal with external disturbances, we construct a series of disturbance observers. With the help of dynamic surface control strategies, we develop a state-feedback control law. Theoretical analysis establishes the boundedness of all signals. Finally, an academic example and a tunnel diode circuit system are provided to demonstrate the effectiveness of our control strategy.

This article concerns distributed generalized Nash equilibrium (GNE) seeking in an N-player game with linear dynamics over a jointly strongly connected switching network. The main challenge of this problem is the design of appropriate updating laws that ensure convergence under a jointly strongly connected switching network. Such a design must also respect inequality constraints and address the complexity of linear dynamics. Projection-based pseudo-gradient method is proposed to seek the GNE while satisfying both the individual and the shared inequality constraints. Furthermore, the jointly strongly connected switching network, which may be disconnected at any time instant, entails resorting to the generalized Barbalat's lemma in the convergence analysis. We also discuss an application to doubly fed induction generators (DFIGs) subject to total power limitations and individual power ranges, providing simulation results to verify the proposed algorithm.

In this study, we investigate the resilience of DC microgrids in the face of disturbances that could induce boost converter failures. We associate the converter failure conditions with disturbances and implement a power buffer control system, which prevents voltage collapse and promotes system stability. A new resilience model is proposed that considers general power mismatches for a comprehensive resilience evaluation. We further evaluate the resilience of an interconnected DC microgrid where the stability of the system is ensured through proofs and examine the role of power buffer control in enhancing resilience against disturbances. The results validate the significance of power buffer control in augmenting DC microgrid resilience. The hardware-in-the-loop experiment study demonstrates over 32% improvement of resilience using the proposed control.

We study the model-based dynamic event-triggered distributed control for linear physically interconnected systems. For each subsystem, a distributed event-triggered control law, along with a model-based dynamic event-triggering mechanism, is proposed. The resulting closed-loop system is shown to be exponentially stable. A positive minimum interevent time excludes the Zeno behavior for each subsystem and is shown to be larger than the one guaranteed by the conventional zero-order-hold approach. Numerical studies on coupled inverted pendulums and experimental results on networked power buffers validate the proposed methodology.

This article addresses the problem of global stabilization of continuous-time linear systems subject to control constraints using a model-free approach. We propose a gain-scheduled low-gain feedback scheme that prevents saturation from occurring and achieves global stabilization. The framework of parameterized algebraic Riccati equations (AREs) is employed to design the low-gain feedback control laws. An adaptive dynamic programming (ADP) method is presented to find the solution of the parameterized ARE without requiring the knowledge of the system dynamics. In particular, we present an iterative ADP algorithm that searches for an appropriate value of the low-gain parameter and iteratively solves the parameterized ADP Bellman equation. We present both state feedback and output feedback algorithms. The closed-loop stability and the convergence of the algorithm to the nominal solution of the parameterized ARE are shown. The simulation results validate the effectiveness of the proposed scheme.

This paper presents the design of an optimal controller for solving tracking problems subject to unmeasurable disturbances and unknown system dynamics using reinforcement learning (RL). Many existing RL control methods take disturbance into account by directly measuring it and manipulating it for exploration during the learning process, thereby preventing any disturbance induced bias in the control estimates. However, in most practical scenarios, disturbance is neither measurable nor manipulable. The main contribution of this article is the introduction of a combination of a bias compensation mechanism and the integral action in the Q-learning framework to remove the need to measure or manipulate the disturbance, while preventing disturbance induced bias in the optimal control estimates. A bias compensated Q-learning scheme is presented that learns the disturbance induced bias terms separately from the optimal control parameters and ensures the convergence of the control parameters to the optimal solution even in the presence of unmeasurable disturbances. Both state feedback and output feedback algorithms are developed based on policy iteration (PI) and value iteration (VI) that guarantee the convergence of the tracking error to zero. The feasibility of the design is validated on a practical optimal control application of a heating, ventilating, and air conditioning (HVAC) zone controller.

This article investigates distributed event-triggered control (ETC) of power buffers in a direct current (DC) microgrid. In order to facilitate the control design, a linear interconnected system model is derived that captures the physical coupling among power buffers. Then, a distributed ETC law regulates the stored energy and input impedance of each power buffer, and a decentralized dynamic event-triggering mechanism determines when each power buffer communicates with its neighboring buffers. This strategy eliminates the need for both continuous controller updates and continuous communication among the power buffers. The resulting closed-loop system is shown to be exponentially stable under a mild assumption on the communication network. The proposed event-triggering mechanism guarantees not only the exclusion of the Zeno behavior but also the existence of a positive minimum interevent time that can be adjusted by the control design parameters. Simulation studies validate the effectiveness of the proposed theoretical results for a multibuffer DC microgrid.

Over the past decades, the synchronization of complex networks with directed topologies has received considerable attention owing to its extensive applications in the realistic world. Design of proportional-integral-derivative (PID) control protocols for achieving synchronization with directed networks is known to be a challenging task. The purpose of this paper is to establish a connection between the PID control protocols and synchronization of complex dynamical networks with directed topologies. Based on the classical complex network model, we investigate global synchronization with PD controller of a balanced strongly connected directed network and global synchronization with PI controller of a strongly connected directed network, and a directed network containing a spanning tree, respectively. Several sets of sufficient conditions are established under which the network reaches global synchronization. The simulation examples are presented to verify the efficiency of the theoretical results.

In visual object tracking, robust and accurate scale estimation of a target is a challenging task. Despite the associated computational expense, existing tracking methods cannot accommodate large scale variations. Here, we propose a scale searching scheme that obtains robust and accurate scale estimation by incorporating a novel and robust criterion, the average peak-to-correlation energy, into a multi-resolution translation filter framework. To address the problem of computational expense, we introduce an expeditious search strategy. The resulting system is named FAST: Fast and Accurate Scale estimation for Tracking. Comprehensive evaluation using the publicly available tracking benchmark datasets demonstrates that the proposed scale searching framework can accommodate large scale variation while also yielding computational efficiency.

In this paper, we propose a model-free solution to the linear quadratic regulation (LQR) problem of continuous-time systems based on reinforcement learning using dynamic output feedback. The design objective is to learn the optimal control parameters by using only the measurable input-output data, without requiring model information. A state parametrization scheme is presented which reconstructs the system state based on the filtered input and output signals. Based on this parametrization, two new output feedback adaptive dynamic programming Bellman equations are derived for the LQR problem based on policy iteration and value iteration (VI). Unlike the existing output feedback methods for continuous-time systems, the need to apply discrete approximation is obviated. In contrast with the static output feedback controllers, the proposed method can also handle systems that are state feedback stabilizable but not static output feedback stabilizable. An advantage of this scheme is that it stands immune to the exploration bias issue. Moreover, it does not require a discounted cost function and, thus, ensures the closed-loop stability and the optimality of the solution. Compared with earlier output feedback results, the proposed VI method does not require an initially stabilizing policy. We show that the estimates of the control parameters converge to those obtained by solving the LQR algebraic Riccati equation. A comprehensive simulation study is carried out to verify the proposed algorithms.

Robust and accurate scale estimation of a target object is a challenging task in visual object tracking. Most existing tracking methods cannot accommodate large scale variation in complex image sequences and thus result in inferior performance. In this paper, we propose to incorporate a novel criterion called the average peak-to-correlation energy into the multi-resolution translation filter framework to obtain robust and accurate scale estimation. The resulting system is named SITUP: Scale Invariant Tracking using Average Peak-to-Correlation Energy. SITUP effectively tackles the problem of fixed template size in standard discriminative correlation filter based trackers. Extensive empirical evaluation on the publicly available tracking benchmark datasets demonstrates that the proposed scale searching framework meets the demands of scale variation challenges effectively while providing superior performance over other scale adaptive variants of standard discriminative correlation filter based trackers. Also, SITUP obtains favorable performance compared to state-of-the-art trackers for various scenarios while operating in real-time on a single CPU.

A characteristic model based all-coefficient adaptive control law was recently implemented on an experimental test rig for high-speed energy storage flywheels suspended on magnetic bearings. Such a control law is an intelligent control law, as its design does not rely on a pre-established mathematical model of a plant but identifies its characteristic model while the plant is being controlled. Extensive numerical simulations and experimental results indicated that this intelligent control law outperforms a μ -synthesis control law, originally designed when the experimental platform was built in terms of their ability to suppress vibration on the high-speed test rig. We further establish, through an extensive simulation, that this intelligent control law possesses considerable robustness with respect to plant uncertainties, external disturbances, and time delay.

The cruise control problems for high-speed trains are investigated in this paper. Both a single train and multiple trains on a railway line are considered. The cars in a single train are modeled as a group of ordered particles connected by flexible couplers. Each car is viewed as an intelligent agent that communicates with its neighbors, making the train a multi-agent system. The information transmission topology among these agents is represented by a connected undirected graph. Distributed cooperative control laws are constructed that achieve displacement and speed consensus among cars at a desired profile, while guaranteeing the coupler displacements to be within a safety range and converge to the nominal value. For multiple trains on a railway line, each train has access to the information of the trains within its wireless communication range, making all cars in these trains a multi-agent system. The underlying communication topology is now a state-dependent undirected graph. Distributed control laws are designed such that, besides achieving coordinated control of cars among each train, consensus among trains at the desired displacement and speed profile and connectivity among trains are also achieved, while avoiding collision. Extensive simulation results are presented to illustrate the theoretical conclusions we have reached.

As demand for rail transportation continues to increase rapidly, significant challenges have emerged in many railway systems in terms of Capacity, Safety and Customer Satisfaction. To deal with these challenges, intelligent technologies, such as artificial intelligence, big data, and machine learning, have been gradually introduced to rail transportation. It is therefore timely and appropriate to have a focused investigation and discussion about Intelligent Rail Transportation. This special issue provides a forum for scientists and engineers working in academia, industry, and government to present their latest research findings and engineering experiences in developing and applying intelligent technologies to improve railway’s autonomy, cooperation, and integration.

Approximate dynamic programming (ADP) and reinforcement learning (RL) have emerged as important tools in the design of optimal and adaptive control systems. Most of the existing RL and ADP methods make use of full-state feedback, a requirement that is often difficult to satisfy in practical applications. As a result, output feedback methods are more desirable as they relax this requirement. In this paper, we present a new output feedback-based Q-learning approach to solving the linear quadratic regulation (LQR) control problem for discrete-time systems. The proposed scheme is completely online in nature and works without requiring the system dynamics information. More specifically, a new representation of the LQR Q-function is developed in terms of the input-output data. Based on this new Q-function representation, output feedback LQR controllers are designed. We present two output feedback iterative Q-learning algorithms based on the policy iteration and the value iteration methods. This scheme has the advantage that it does not incur any excitation noise bias, and therefore, the need of using discounted cost functions is circumvented, which in turn ensures closed-loop stability. It is shown that the proposed algorithms converge to the solution of the LQR Riccati equation. A comprehensive simulation study is carried out, which illustrates the proposed scheme.

We consider the distributed secondary voltage control problem for microgrids subject to delays in a connected communication topology. For each distributed generator (DG), a distributed event-triggered control law, which uses its own voltage and those of its neighbors, along with a strategy for its triggering, are designed. Under these distributed event-triggered control laws, the magnitudes of the output voltages of all DGs can be synchronized to their reference value subject to a bounded time-varying delay that can be arbitrarily large, and no Zeno behavior will occur. The simulation results illustrate the theoretical conclusions.

Reliable cell counting and segmentation of oligodendrocyte progenitor cells (OPCs) are critical image analysis steps that could potentially unlock mysteries regarding OPC function during pathology. We propose a saliency-based method to detect OPCS and use a marker-controlled watershed algorithm to segment the OPCS This method first implements frequency-tuned saliency detection on separate channels to obtain regions of cell candidates. Final detection results and internal markers can be computed by combining information from separate saliency maps. An optimal saliency level for OPCS (OSLO) is highlighted in this work. Here, watershed segmentation is performed efficiently with effective internal markers. Experiments show that our method outperforms existing methods in terms of accuracy.

This paper focuses on the construction of distributed observers in the presence of arbitrarily large communication time delays. In contrast with the traditional centralized observer with the ability to acquire full output of the plant, we design a set of distributed observers, each having access to partial output of the plant through a distributed sensor network. More specifically, each observer obtains partial plant output and communicates with its neighboring observers through consensus protocols. The communication among the network is subject to arbitrarily large time delays. We consider three representative network topologies and for each topology establish conditions to guarantee the observation error systems be exponentially stable. We also consider the design of a pinning synchronization problem as a dual problem of the design of distributed observers. Numerical simulation is carried out to verify the effectiveness of our theoretical analysis.

Optical switching technologies offer a cost-and power-efficient approach for handling the DataCenter Network (DCN) oversubscription problem. We propose a hybrid DCN architecture named Optical Switch in the Middle (OSM), which offers increased flexibility (when compared to prior hybrid architectures) for supporting multiple simultaneous high-speed TOR-to-TOR paths through an Optical Circuit Switch (OCS) and a core-level Electrical Packet Switch (EPS). A multilayer SDN controller supports advanced-reservation scheduling of optical circuits, and the integration of storage in the core EPS increases the usage rate of optical circuits. To effectively use the OSM architecture, we propose four modifications to Hadoop, and illustrate the potential of this architecture for achieving higher compute-resource utilization while simultaneously offering users shorter job completion times.

The design of networked observers has recently received much consideration. This paper is concerned with the design of distributed observers with arbitrarily large communication time delays. A traditional centralized observer assumes access to the full output the plant. In the real world situations, the output is often measured by distributed senors connected through a communication network. We propose the design of a set of distributed observers with time delays in the communication channels. Each observer is regarded as an agent, which obtains local information of the plant and communicates with other observers through an undirected communication network, with arbitrarily large time delays. Conditions are established under which the states of all observers converge exponentially to the plant state. Numerical results are proposed to illustrate our results.

This paper revisits the distributed adaptive control problem for synchronization of multiagent systems where the dynamics of the agents are nonlinear, nonidentical, unknown, and subject to external disturbances. Two communication topologies, represented, respectively, by a fixed strongly-connected directed graph and by a switching connected undirected graph, are considered. Under both of these communication topologies, we use distributed neural networks to approximate the uncertain dynamics. Decentralized adaptive control protocols are then constructed to solve the cooperative tracker problem, the problem of synchronization of all follower agents to a leader agent. In particular, we show that, under the proposed decentralized control protocols, the synchronization errors are ultimately bounded, and their ultimate bounds can be reduced arbitrarily by choosing the control parameter appropriately. Simulation study verifies the effectiveness of our proposed protocols.

Multiagent systems (MASs) are ubiquitous in our real world. There is an increasing attention focusing on the consensus (or synchronization) problem of MASs over the past decade. Although there are numerous results reported on the convergence of a discrete-time MAS based on the infinite products of matrices, few results are on the convergence rate. Because of the switching topology, the traditional eigenvalue analysis and the Lyapunov function methods are both invalid for the convergence rate analysis of an MAS with a switching topology. Therefore, the estimation of the convergence rate for a discrete-time MAS with time-varying delays remains a difficult problem. To overcome the essential difficulty of switching topology, this paper aims at developing a contractive-set approach to analyze the convergence rate of a discrete-time MAS in the presence of time-varying delays and generalized coupling coefficients. Using the proposed approach, we obtain an upper bound of the convergence rate under the condition of joint connectivity. In particular, the proposed method neither requires the nonnegative property of the coupling coefficients nor the basic assumption of a uniform lower bound for all positive coupling coefficients, which have been widely applied in the existing works on this topic. As an application of the main results, we will show that the classical Vicsek model with time delays can realize synchronization if the initial topology is connected.

For a large-scale distributed system, distributed model predictive control (DMPC) is a method of choice because of its ability to explicitly accommodate constraints and to achieve good dynamic performance. In the design of a DMPC, guaranteeing stability with a strong global performance is known to be a challenge. In this paper, we consider a large-scale distributed system whose input is constrained to given sets in their respective spaces and propose a stabilizing DMPC design, where each subsystem-based model predictive control (MPC) optimizes the cost function of the entire system over the region it directly impacts on. Consistency constraints and stability constraints, which bound the estimation errors of the interaction sequences among subsystems, are designed to guarantee that, if an initially feasible solution can be found, subsequent feasibility of the algorithm is guaranteed at every update, and that the closed-loop system is asymptotically stable. A key feature of the proposed DMPC is that it coordinates the MPCs of the subsystems by redefining the impact region of a subsystem according to the coordination strategy. Simulation results show that the performance of the proposed DMPC is very close to that of a centralized MPC.

In this paper, we study properties of distributed consensus in layered sensor networks of the multi-layer multi-group (MLMG) structure. We show that properly designed MLMG networks maintain decentralized communication, whereas show the advantage of centralized structures. In particular, they require less number of transmissions required to reach consensus. This feature is critical for efficient distributed computing in large-scale sensor network applications. For typical classes of MLMG networks, we mathematically characterize the reduced number of transmissions compared to equivalent egalitarian decentralized structures of the same consensus dynamics. This explicit characterization based on simple graphical characteristics of MLMG structures permits an efficient design of large-scale network structures to meet desired performance requirements. In addition, we characterize the asymptotic and transient properties of consensus in MLMG networks of limited channel rates, using the probabilistic quantization schemes.

In traditional control theory, a single observer has access all the measured outputs of the plant to estimates its asymptotically. In many real world engineering systems, it may be difficult to build a single observer that has access to all the measured outputs. One way around this difficulty is to build a network of cooperative observers, each of which obtains a portion of the measurement outputs, that collectively produce an asymptotic estimate of the plant state. In this paper, we construct a network of such observers for a continuous-time linear system. Assuming that these observers are connected through an undirected connected network, we establish a necessary and sufficient condition on the plant parameters under which the network of observers will achieve asymptotic omniscience. A network of cooperative observers is said to achieve asymptotic omniscience if their states all converge to the plant state asymptotically. Numerical simulation results are presented to validate theoretical results. The design of cooperative observers sheds some light on the solution of some other real-world problems, such as the design of networked location-based services and sensor networks.

This paper studies the flocking problem for a multiagent system, where each agent is a vehicle with nonholonomic dynamics. In particular, we consider the case where the agents are subjected to an arbitrarily large communication delay. A distributed low gain control law is derived based on the gradient of an artificial potential function. We demonstrate using the Lyapunov functional approach that the proposed control law drives the multiagent system into the stable flocking behavior. The effectiveness of the proposed control law is verified in numerical simulation.

Target detecting and dynamic coverage are fundamental tasks in mobile robotics and represent two important features of mobile robots: mobility and perceptivity. This paper establishes the constrained motion model and sensor model of a mobile robot to represent these two features and defines the k -step reachable region to describe the states that the robot may reach. We show that the calculation of the k-step reachable region can be reduced from that of 2(k) reachable regions with the fixed motion styles to k + 1 such regions and provide an algorithm for its calculation. Based on the constrained motion model and the k -step reachable region, the problems associated with target detecting and dynamic coverage are formulated and solved. For target detecting, the k-step detectable region is used to describe the area that the robot may detect, and an algorithm for detecting a target and planning the optimal path is proposed. For dynamic coverage, the k-step detected region is used to represent the area that the robot has detected during its motion, and the dynamic-coverage strategy and algorithm are proposed. Simulation results demonstrate the efficiency of the coverage algorithm in both convex and concave environments.

Takagi-Sugeno (TS) fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input-output submodels. In this paper, the TS fuzzy modeling approach is utilized to carry out the stability analysis and control design for nonlinear systems with actuator saturation. The TS fuzzy representation of a nonlinear system subject to actuator saturation is presented. In our TS fuzzy representation, the modeling error is also captured by norm-bounded uncertainties. A set invariance condition for the system in the TS fuzzy representation is first established. Based on this set invariance condition, the problem of estimating the domain of attraction of a TS fuzzy system under a constant state feedback law is formulated and solved as a linear matrix inequality (LMI) optimization problem. By viewing the state feedback gain as an extra free parameter in the LMI optimization problem, we arrive at a method for designing state feedback gain that maximizes the domain of attraction. A fuzzy scheduling control design method is also introduced to further enlarge the domain of attraction. An inverted pendulum is used to show the effectiveness of the proposed fuzzy controller.