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This paper presents two stochastic model predictive control methods for linear time-invariant systems subject to truncated normal distributed additive uncertainties. The new methods are developed by transforming the chance constraints into deterministic constraints via re-building the robust tube-based model predictive control (RTMPC) framework with flexible initialization. Utilizing the one-step-ahead constraint, the ahead-step tube-based stochastic model predictive control (ATSMPC) algorithm is designed by applying the constantly tightened constraints in all prediction horizons. To further enhance the reliability, the cumulative-step tube-based stochastic model predictive control (CTSMPC) algorithm is developed by computing the tightened constraints based on the propagation of uncertainties along the prediction horizons. The effectiveness of the proposed methods are demonstrated via simulations.

This paper studies the model predictive control problem (MPC) of linear systems with unknown parameters both in system models and measurement models. The method that combines the estimation of system parameters and states with MPC is proposed, where the reinforcement learning (RL) is used to learn the optimal control strategies. Its characteristics are that the control and estimate can proceed simultaneously. Simulation studies verify that the designed algorithm can converge to the optimal linear feedback and the parameters converge as well.

This paper is concerned with the robust optimal control of uncertain multi-agent systems (MASs) with linearized unmanned surface vehicle (USV) models. Kinematics and dynamics of USVs are linearized as plant models of MASs, and a leaderless consensus control protocol is presented. Moreover, the robust optimization of performance index can be guaranteed by the presented protocol. Numerical example is provided to show the effectiveness of presented protocol.

This work addresses a class of distributed optimization problems where the global objective function is the sum of multiple local convex smooth functions privately held by a group of working agents. Upon modeling the unconstrained distributed optimization problem as a linearly constrained centralized one, a communication-efficient event-triggered first-order primal-dual algorithm that only requires light local computation at each generic time instant and peer-to-peer communication at sporadic triggering time instants is developed to solve the global problem. An $O\left( {\frac{1}{k}} \right)$ convergence rate is ensured, provided that the stepsize satisfies a condition that relates to the Lipschitz constant of the gradient and the Laplacian of the communication graph, and the time-varying triggering threshold is monotonically decreasing and summable. The proposed method is applied to a decentralized logistic regression problem to illustrate its effectiveness, especially in saving communication resources.

We study the distributed model predictive control (DMPC) problem for a group of linear discrete-time systems with both local constraints and global constraints in the presence of stochastic communication noise. The dual form of the DMPC optimization problem is transformed into a stochastic distributed consensus optimization problem by modeling the exchanged variables as stochastic ones and a novel stochastic alternating direction multiplier method (ADMM) is proposed to solve it in a fully distributed way. The effectiveness of the proposed stochastic ADMM algorithm is verified through an simulation example.

This paper investigates the distributed model predictive control (DMPC) problem for a network of linear discrete-time systems with coupled constraints over time-varying directed communication network. An centralized MPC optimization problem is first designed and further transformed into a dual form. A distributed optimization algorithm termed push-sum dual gradient (PSDG) is proposed to solve the dual problem in a fully distributed way. Finally, simulation studies verify the effectiveness of the proposed distributed optimization algorithm.

This work addresses multi-agent optimization problems with decoupled local objective functions and coupled inequality constraints. A distributed dual subgradient method with double averaging that is built on the dual decomposition, the subgradient method with double averaging, and the dynamic average consensus is developed to solve the global problem with only local computation and peer-to-peer communication. It is theoretically proved that, for the primal-dual sequence, both the dual objective error and the quadratic penalty for the coupled constraints have $O\left( {\frac{1}{{\sqrt t }}} \right)$ upper bounds, and the primal objective error asymptotically vanishes. The proposed algorithm is applied to optimize the Quality of Service (QoS) in wireless networks; numerical results verify the effectiveness of the proposed algorithm.

This article studies the distributed optimal control problem of multiple autonomous underwater vehicles (AUVs). The fuzzy approach is utilized to model the nonlinear dynamics of AUVs, and a distributed control protocol is presented to guarantee the consensus of multiple AUVs. Moreover, the optimization of cost function is achieved using the linear quadratic regulator (LQR) approach. A numerical example is reported to demonstrate the efficiency and improved performance of the presented protocol.

The model predictive control (MPC) of Cyber-Physical-Systems (CPSs) suffer from the drawbacks of heavy communication and computation cost. This paper studies the self-triggered model predictive control (STMPC) problem for continuous-time nonlinear systems subject to system constraints under cyber-physical scenarios. A novel STMPC strategy is proposed which allows adaptively to select sampling number during two triggering time instants, and new self-triggered condition is designed. Furthermore, the feasibility of the designed algorithm and the closed-loop stability is theoretically ensured. We show that the closed-loop system is asymptotically stable under the proposed self-triggered strategy. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithm.

This article studies the cooperative optimal attitude control problem of multiple rigid bodies with external disturbances. The fuzzy approach is utilized to model the attitude dynamics of rigid bodies. A sliding-mode control (SMC) protocol is presented, and the optimal nominal controller is designed. A numerical example is reported to demonstrate the efficiency of the presented protocol.

The paper studies the trajectory tracking control problem of autonomous underwater vehicles (AUV) with practical constraints via economic model predictive control (EMPC) strategy. The cost function in EMPC is designed with two parts. One part is the trajectory tracking cost and the other part is the economic cost which is utilized to keep the AUV tracking within one side of the trajectory. We term this type of trajectory tracking as the unilateral trajectory tracking, which can be applied for hazardous area tracking and obstacle avoidance of AUVs. The effectiveness of the proposed approach is verified via thorough simulation studies.

This paper investigates the L 2 -L ∞ -based output-feedback control for multi-vehicle formation problems. The vehicle formation model with a virtual leader is transformed into a linear multi-agent system (MAS), where the road roughness is regarded as the external disturbance. Afterwards, by applying the singular value decomposition (SVD) method, an output-feedback distributed controller subject to switching communication topologies is designed. Then the L 2 -L ∞ performance of closed-loop system is investigated with the help of LMI method. Numerical examples are finally provided to illustrate the effectiveness of theoretical results.

The distributed control of spacecraft attitude coordination problems is developed in this paper. An adaptive sliding-mode (SM) controller is designed to steer the attitude of multiple spacecrafts to synchronously reach zero. Then a modified chattering-free controller is proposed to eliminate the chattering of control inputs caused by the switching function. By utilizing the Lyapunov stability theory, we can prove that the presented approach ensures the multi-spacecraft attitude converge into a limited range. Finally, numerical examples are provided to illustrate that the modified control approach can achieve high control accuracy with eliminating the chattering in the presence of external disturbances and model uncertainties.

In this paper, we study the self-triggered distributed model predictive control problem of a class of nonholonomic systems subject to system constraints. A novel self-triggered DMPC algorithm with new sampling selection mechanism is developed. The theoretical results on feasibility of the designed algorithm and the stability of the closed-loop system are provided. A simulation example is offered to verify the effectiveness of the designed approach.

The communication load is a main concern for implementing model predictive control (MPC) to networked control systems. This paper investigates the self-triggered MPC problem for discrete-time linear systems under network environments to reduce communication load. We first propose a novel cost function which explicitly takes the communication cost into consideration. Based on the new cost function, an adaptive self-triggered MPC strategy is designed in the sense that the self-triggered time intervals can be adaptively adjusted via optimization. We further develop a simplified approach to solving the optimal control sequences and the optimal self-triggered time intervals. The stability of the closed-loop system is analyzed and we show that the closed-loop system is asymptotically stable if some mild controllability conditions are satisfied. Finally, the effectiveness of the designed algorithm is verified via simulation studies.

To deal with randomness and uncertainty problems, an adaptive discontinuous Galerkin time domain (A-DGTD) method is proposed to quantify the influence of defect factors. In order to prove the efficiency of the method, the standard statistical analysis is performed to calculate the mean of reflection coefficients caused by random/uncertain physical parameters on a dielectric slab.

This paper studies the model predictive formation control problems of unmanned surface vehicles (USVs). A two-layer model predictive control (MPC)-based formation control strategy is proposed. The proposed formation strategy artificially divides the model of each USV into two layers according to its kinematic model and dynamic model. Using the kinematic model, a distributed MPC algorithm is designed to ensure formation and coordination; with the generated command from the distributed MPC as tracking reference, an MPC is designed for the dynamic model to ensure optimal command tracking. Simulation and comparison studies show that the designed algorithm is computational more efficient in comparison with the centralized MPC formation algorithm.

In this paper, an event-triggered distributed receding horizon control algorithm is proposed for dynamically coupled continuous-time linear systems. First, an event-triggered rule that is realized by monitoring the error between subsystem state and its optimal prediction is designed, and an extra constraint that restricts the discrepancy between each subsystem's assumed and predicted state and input trajectories is introduced to local optimization problems. Combined with the triggering rule and the extra constraint, the mutual disturbances caused by dynamical coupling are bounded and the inter-event time is lower bounded to avoid the Zeno behavior. Based on this, the algorithm feasibility and closed-loop stability are rigorously studied, and sufficient conditions for guaranteeing them are established. Finally, simulation studies are conducted to verify the theoretical results.

In this paper, we propose a self-triggered robust model predictive control (MPC) algorithm for constrained linear discrete-time systems subject to additive disturbances. First, worst case scenarios are considered in the MPC problem formulation to achieve robust constraint satisfaction. Second, a self-triggered control scheduler is proposed to minimize the frequency of performing optimization and the closed-loop system is shown to be recursive feasible and input-to-state practical stable in its region of attraction. Finally, a numerical example demonstrates the effectiveness of the proposed control strategy.

Time-domain based electromagnetic (EM) field solvers choose electric and magnetic fields as their initial conditions which are the function of space and time. The variation of fields with respect to time in the computational domain is observed at some observation point and frequency response is obtained by using fast Fourier transformation (FFT). In order to perform broadband simulation up to the frequency of interest, the time domain signals must support the requisite spectrum coverage. This paper uses the standard theory of space decomposition in discontinuous Galerkin time domain (DGTD) method and analyses the effects of different types of sampling on spectrum coverage of gauss pulse. The computational domain is discretized into K number of nonoverlapping elements (known as h-refinement) and each element uses Nth order of polynomial interpolation (known as p-refinement). The interpolation is achieved by using N p number of interpolating nodes. The K elements can be uniform or nonuniform and the distribution of interpolating nodes inside each element can also be uniform or nonuniform. The combination of all these four schemes, i.e., i) Uniform h and uniform N p , ii) Uniform h and nonuniform N p , iii) Nonuniform h and uniform N p , and iv) Nonuniform h and nonuniform N p , have an effect on the sampling of the time-domain signal. This variation in the sampling rate of the time-domain signal causes variation in bandwidth coverage of the frequency-domain spectrum. The selection of suitable combination, defined by a different value of K and N, helps in the modeling of the excitation source for the DGTD based time-domain EM field solver. The Legendre-Gauss-Lobatto (LGL) nodes distribution is selected as nonuniform N p . After series of simulations, the best combination for the sampling of the time-domain source is selected to be the ‘Nonuniform h and nonuniform N p ’. The variation in the bandwidth coverage of the spectrum is also studied with respect to the variation in the smallest size of an element. The higher coverage of bandwidth can be achieved only by changing few sampling points in nonuniform h and nonuniform N p .

Generation of a mesh is the first step in the development of electromagnetic (EM) field solver and mesh quality plays vital role in defining the consumption of computational resources like execution time and memory usage. The modeling of regular structures (structures whose interfaces coincide with the mesh cells) using simple mesh grids is easy to achieve but for irregular structures (structures whose interfaces do not coincide with mesh cells) is hard to achieve. Different methods have already been proposed to deal with domain decomposition for the irregular structures, e.g., thin-layer problems. The methods include subgridding and fine-mesh generation in the locality of the thin layers. This high resolution in the domain decomposition leads to the redundancy of number of cells in the computational domain hence demanding for extra computational resources. This paper presents two adaptive mesh generation techniques in 1-D which have successfully integrated the complicated multilayer structure composed of irregular thicknesses. Adaptation in the mesh for the discontinuous Galerkin time domain (DGTD) based EM solver is achieved by using i) nodes displacement, and ii) node insertion methods. The two methods use an optimum number of nonoverlapping elements (K) for the domain decomposition and adapt the locations of the nodes to incorporate the complex structures. Algorithms and associated methodologies for the two techniques are presented and their validation is achieved by comparing the numerical results with the analytical results for an irregular structure composed of multilayer with different values of thickness. High accuracy is achieved by the both methods but tradeoff exists between the complexity of the algorithms and the execution time. The node displacement method has a complex algorithm and consumes lesser execution time while the node insertion method has the simplest algorithm but requires more execution time for solving the same problem.

This paper is concerned with the optimal consensus problem of multi-agent systems (MASs) with general unstable linear dynamics. The main tool for the design and analysis is the inverse optimality and set stability theory. Firstly, the optimal conditions for ensuring set stability are developed for discrete-time linear systems in terms of the inverse optimality principle. Then for the MASs with unstable dynamics, the approach to designing the optimal consensus protocol is proposed, and the detailed design conditions are developed. Based on the set stability theory, the consensus properties are proved. Finally, a simulation study is provided to verify the effectiveness of the proposed method.

This paper addresses the cooperative output regulation problem of linear discrete-time multi-agent systems with a dynamic network subject to stochastic data packet dropouts. Under some standard assumptions on the solvability of classical linear cooperative output regulation problem, a dynamic output feedback protocol is designed and the necessary and sufficient conditions under which the closed-loop system reaches cooperative regulation are proposed. A numerical example is provided to illustrate the validity of the proposed approach.

The cooperative control of marine vehicles finds many applications in a variety of marine missions and tasks. This paper investigates the receding horizon formation control problem of a feet of nonholonomic autonomous underwater vehicles (AUVs), in which the follower AUVs are required to keep a prescribed formation pattern with respect to the leader one. A novel optimization problem and a new receding horizon control (RHC) algorithm are proposed. Results on the feasibility of the receding horizon formation algorithm and on the stability of the closed-loop systems are provided. Simulation studies for the formation-keeping control of the nonholonomic AUVs are carried out, and the simulation results verifies the effectiveness of the proposed algorithm.

This paper studies the receding horizon control (RHC)-based stabilization problem of a class of constrained nonholonomic systems in power form. A non-quadratic cost function is constructed by using the homogeneous norm of the nonholonomic system in power form. Based on this novel cost function, a novel RHC algorithm is designed. The feasibility of the designed algorithm and the closed-loop stability are analyzed, and theoretically ensured under mild conditions. A simulation result is provided to verify the effectiveness of the proposed algorithm.

This paper investigates the distributed receding horizon control (RHC) problem of a vehicle platoon with nonlinear dynamics and subject to system constraints, where each vehicle can communicate with its immediate predecessor and follower. A novel optimization problem and detailed distributed RHC algorithm are designed in order to keep a constant distance between any two adjoining vehicles, and further to ensure neighbor string stability. The sufficient conditions on ensuring closed-loop stability and neighbor string stability are established, respectively. It is shown that the cooperations among vehicles are detrimental to the closed-loop stability, and thus the cooperation cannot be too strong in order to guarantee the closed-loop stability.

In this paper, the electromagnetic computation and simulation of a cavity filled with vacuum and dielectric are discussed. An efficient discontinuous Galerkin time domain (DGTD) method which employs an orthonormal basis (for spatial discretization) and Runge-Kutta method (for temporal integration) to solve Maxwell's equations is given in detail. Numerical experiments on different order of approximation and number of elements is performed. Compared with the exact analytical solves, the DGTD method obtains optimal convergence, slight error and robust.

Discontinuous Galerkin finite element method (DG-FEM) for electromagnetic simulation is implemented using the Julia language in this work. DG-FEM is a combination of finite element method and finite volume method and it employs high-order spatial and temporal basis to obtain high-order accuracy. Julia is a relative new language specially built for high performance computing. It is adopted in DG-FEM for two EM simulation examples and compared the results with both MATLAB implementation and analytical results. The comparisons prove the accuracy of both the DG-FEM method and the Julia language. Finally potential applications of this work are discussed.

This paper is concerned with the consensus estimation problem of wireless sensor networks (WSNs) subject to random packet dropouts. In the WSN, the sensor nodes are of general linear dynamics, and the packet dropouts are modeled as Bernoulli processes. The distributed receding horizon estimation (RHE) scheme is proposed to achieve consensus for state estimate. To design such a consensus-based RHE scheme, an optimization problem containing consensus term is first formulated, and then the explicit solution to the optimization problem is provided as the estimation protocol. The sufficient conditions on ensuring the stochastic consensus for state estimates are developed. It is shown that, if the cost functions are designed appropriately and the packet dropout processes fulfill certain conditions, the consensus gain can be designed such that the stochastic consensus is reached.

As complex cyber-physical systems, connected vehicle systems are composed of multiple interacting intelligent agents within traffic environments. In this demonstration, a lightweight multi-agent based testbed is presented for evaluating connected vehicle systems. Miniature smart vehicles and quadrotor vehicles are developed as the multi-agent components, which are equipped with embedded processors, ZigBee radio modules and WiFi modules as well. Through cooperative communication, collaborative control and video detection, the multi-agent system can realize the information interaction between air-to-ground, vehicle-to-vehicle, and vehicle-to-roadside. This research is beneficial to develop more intelligent connected vehicles in the near future.

This paper is concerned with the state-feedback ℌ ∞ control problem for a class of stochastic nonlinear systems with time-varying state delays, where both the disturbance and the white noise are considered. To synthesize the investigated system, the parameter-dependent storage functional is firstly constructed. By means of completing the square, the delay-dependent controller is designed such that the closed-loop system is exponentially stable in mean square and the prescribed disturbance attenuation level is achieved. Finally, the developed delay-dependent conditions are further specialized to synthesize a type of special delayed nonlinear systems by solving several linear matrix inequalities (LMIs) and a bilinear matrix inequality (BMI).

The design mind of SOPC (System on Programmable Chip), hardware & software co-design, will bring the system optimization with larger degree of freedom. It will establish the foundation on realizing the miniaturization and more efficient local preprocessing in smart cooperative sensor networks. In this contribution, a solution is proposed for smart multimedia sensor nodes based on the SOPC framework, aiming at improving the on-board processing capability and the degree of freedom in development. This paper mainly presents the structure and design method of the smart cooperative multimedia sensor node based on SOPC. The on-board feature extraction is realized and some correlative experimental data and results are presented as well. The experimental results evaluate the feasibility and efficiency of the proposed node design method.

Taking advantage of context aware computing, a novel vehicle terminal system is proposed to realize collaborative awareness, continuous monitoring and precise localization of vehicles for digital logistics. The integrated hardware and software design of the terminal system is presented, which merges together RFID, GPS and sensor technologies based on embedded systems. We use the ultra-low power 16-bit RISC mixed-signal microcontroller as the central control unit considering both small size and high efficiency. The intelligent vehicle terminal can provide mainly the automatic identification of a chauffeur and cargos, the real-time vehicle localization, personalized context-aware, the data and voice communication, and remote centralized monitoring as well. Testing and analysis results indicate that the integration of RFID, GPS and context sensors can enhance the reliability of the smart terminal, which further improve the accuracy, intelligence and efficiency of digital logistics vehicular management.

N-heterocyclic molecules, bearing nitrogen atoms and conjugate ring and having protogenic proton conductivity, can be used as proton conducting functional group for proton exchange membrane (PEM). In this paper, 5-(4-hydroxyphenyl)-1H- tetrazole (b) was synthesized using 4-cyanophenol (a) and sodium azide as main materials and water as solvent by click chemistry. The optimum reaction conditions were obtained by orthogonal test design and single factor experiments. The yield of (b) was 92.5% under n(4-cyanophenol): n(NaN3)=1:l.4, reflux at 115oC for 24h, acidifying at 50oC for 1.5h. Then 5-(4-(3-trimethoxy-silyl)-propoxy)phenyl)-1H- tetrazole(c) was formed by condensation using (3- chloropropyl) trimethoxysilane as precursor. The structures of (b) and proposed (c) were supported by FTIR, 1HNMR and 13CNMR. The results showed that this synthetic process was environmental benign due to water solvent and little waste discharge.

This paper is concerned with the H ∞ state feedback control problem for a class of nonlinear stochastic systems with time-varying state delays. Firstly, the stochastic dissipativity of the time-delay nonlinear system is established, based on which the H ∞ state feedback controller is synthesized. We show that the closed-loop system rendered by the designed controller can achieve the L 2 -gain performance and exponential stability in the sense of mean square. Finally, these sufficient conditions are further specialized as solving a linear matrix inequality (LMI) and a bilinear matrix inequality (BMI) for a class of special stochastic nonlinear delayed systems.

In this contribution, a novel vehicle terminal system is proposed to realize continuous monitoring and precise localization of cargos loaded on board for digital logistics. The integrated hardware and software design of the terminal system is presented, which combines RFID with GPS technologies based on embedded systems. We use the ultra-low power 16-bit RISC mixed-signal microcontroller as the central control unit considering both small size and high efficiency. The intelligent vehicle terminal system can provide mainly the automatic identification of cargos loaded, the real-time vehicle localization, the data and voice communication, and remote centralized monitoring as well. Experimental results indicate that the integration of RFID and GPS can enhance the reliability of the system, which further improve the accuracy and efficiency of digital logistics management.

This paper summarizes the structure of WinPcap and introduces the capture principle of Ethernet packets. Based on multithreading technology, WinPcap captures the network layer packets. The design and realization method are provided by this paper. The overall optimization of system has improved the ability of the packet capture and greatly reduced the rate of packet loss. Data contents of the captured packet will be carefully analyzed according to the layers of network protocol format (analysis of hexadecimal data of Ethernet frame net load). The design and research of system provides a theoretical basis for preventing hackers, network security, and intrusion detection technology.

In this paper, we present a method for lexicon size reduction which can be used as an important pre-processing for an off-line Arabic word recognition. The method involves extraction of the dot descriptors and PAWs (Piece of Arabic Word ). Then the number and position of dots and the number of the PAWs are used to eliminate unlikely candidates. The extraction of the dot descriptors is based on defined rules followed by a convolutional neural network for verification. The reduction algorithm makes use of the combination of two features with a dynamic matching scheme. On IFN/ENIT database of 26459 Arabic handwritten word images we achieved a reduction rate of 87% with accuracy above 93%.