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Simultaneous Localization and Mapping (SLAM) is the foundation of autonomous navigation for unmanned systems. The existing SLAM solutions are mainly divided into the visual SLAM(vSLAM) equipped with camera and the lidar SLAM equipped with lidar. However, pure visual SLAM have shortcomings such as low positioning accuracy, the paper proposes a visual-inertial information fusion SLAM based on Runge-Kutta improved pre-integration. First, the Inertial Measurement Unit (IMU) information between two adjacent keyframes is pre-integrated at the front-end to provide IMU constraints for visual-inertial information fusion. In particular, to improve the accuracy in pre-integration, the paper uses the Runge-Kutta algorithm instead of Euler integral to calculate the pre-integration value at the next moment. Then, the IMU pre-integration value is used as the initial value of the system state at the current frame time. We combine the visual reprojection error and imu pre-integration error to optimize the state variables such as speed and pose, and restore map points' three-dimensional coordinates. Finally, we set a sliding window to optimize map points' coordinates and state variables. The experimental part is divided into dataset experiment and complex indoor-environment experiment. The results show that compared with pure visual SLAM and the existing visual-inertial fusion SLAM, our method has higher positioning accuracy.

Almost all event-triggered control (ETC) strategies were designed for discrete-time or continuous-time systems. In order to unify these existing theoretical results of ETC and develop ETC strategies for nonlinear systems, whose state variables evolve steadily at one time and change intermittently at another time, this article investigates quasisynchronization of delayed neural networks (NNs) on time scales with discontinuous activation functions via ETC approaches. First, the existence of the Filippov solutions is proved for discontinuous NNs with finite discontinuities. Second, two static event-triggered conditions and two dynamic event-triggered conditions are established to avoid continuous communication between the master-slave systems under algebraic/matrix inequality criteria. Third, under static/dynamic event-triggered conditions, a positive lower bound of event-triggered intervals is demonstrated to be greater than a positive number for each event-based controller, which shows that the Zeno behavior will not occur. Finally, two numerical simulations are carried out to show the effectiveness of the presented theoretical results in this article.

Although state estimation using a bad data detector (BDD) is a key procedure employed in power systems, the detector is vulnerable to false data injection attacks (FDIAs). Substantial deep learning methods have been proposed to detect such attacks. However, deep neural networks are susceptible to adversarial attacks or adversarial examples, where slight changes in inputs may lead to sharp changes in the corresponding outputs in even well-trained networks. This article introduces the joint adversarial example and FDIAs (AFDIAs) to explore various attack scenarios for state estimation in power systems. Considering that perturbations added directly to measurements are likely to be detected by BDDs, our proposed method of adding perturbations to state variables can guarantee that the attack is stealthy to BDDs. Then, malicious data that are stealthy to both BDDs and deep learning-based detectors can be generated. Theoretical and experimental results show that our proposed state-perturbation-based AFDIA method (S-AFDIA) can carry out attacks stealthy to both conventional BDDs and deep learning-based detectors, while our proposed measurement-perturbation-based adversarial FDIA method (M-AFDIA) succeeds if only deep learning-based detectors are used. The comparative experiments show that our proposed methods provide better performance than state-of-the-art methods. Besides, the ultimate effect of attacks can also be optimized using the proposed joint attack methods.

This paper aims to solve the problem of decentralized event-based finite-time control for a class of high-order interconnected nonlinear systems with an asymmetric output constraint. In order to achieve finite-time stability and reduce the update frequency of the controller, a novel controller and its event-trigger mechanism is proposed by adding a power integral technique and utilizing sign function. To fulfill the requirement of asymmetric output constraints, an asymmetric barrier Lyapunov function (BLF) is constructed, which is different from tantype and log-type BLFs. Based on the contradictory idea and finite-time stability theroy, it is proved that there is no Zeno phenomenon under this event-trigger mechanism and all state variables of the closed-loop system attenuate to the origin in the finite-time while the output signals never violate the constraint. Furthermore, the result in this paper is expanded to fixed-time control. Finally, a simulation example is given to demonstrate the effectiveness of the presented strategy

Pole-zero cancellation technique has drawn much attention to tackle the filter resonance of the LCL-type grid-connected inverter for simplifying the stability-oriented design. However, its potential stability risk was rarely concerned. As one representative of this technique, the weighted average current (WAC) control is assessed. It is found that the LCL resonance in the state variable WAC is cancelled out by pole-zero cancellation, but the ones in some uncontrollable or unobservable state variables such as the grid current are still inherited, resulting in harmonic instability. From this perspective, this paper appeals that this technique should be cautiously used or even avoided. An extended WAC control which avoids pole-zero cancellation is proposed accordingly. The theoretical expectations are finally validated by simulation and experimental results.

In this article, a neural-network-based constrained output-feedback control is considered for microelectromechanical system (MEMS) gyroscopes subject to scarce transmission bandwidth and lumped disturbances resulting from model uncertainties, dynamic coupling, and environmental disturbances. First, a hybrid quantizer capable of achieving an adjustable communication rate and quantization density is proposed to convert continuous control signals into discrete values, allowing for reduced chattering behavior even when control actions vary within large regions and enhanced tracking accuracy can be ensured. Subsequently, by applying two types of nonlinear mapping, all state variables of MEMS gyroscopes are restrained within the predefined time-varying asymmetric functions without imposing stringent feasibility conditions on virtual control laws. Furthermore, an echo-state network-based minimal learning parameter neural observer is developed to simultaneously recover the unmeasurable velocity-state variables, matched as well as unmatched disturbances in constrained MEMS gyroscopes dynamics, enabling an output-feedback control solution with a decreased online learning complexity. It is shown via the Lyapunov stability and nonsmooth analysis that all signals in the closed-loop system remain ultimately uniformly bounded even with discontinuous control actions. Comparison simulations are produced to certify the effectiveness of the presented controller.

This article deals with the problems of stability and control of the interconnected system (IS) with unknown time-varying delays via decentralized time-delay control using partial variables with measurable states. First, the model of the IS with time delays is established, and the relevant control scheme is proposed. The control scheme just needs to control all or partial state variables corresponding to the elements on the main diagonal of the gain matrices, which can reduce the control cost and improve the flexibility of control. In addition, there are no additional restrictions in the process of designing the controller. Second, relevant lemmas are derived. The exponential boundedness and stability analysis of the IS with time delays are presented, respectively, by stability theory, and related results are derived. Meanwhile, the stability domain of the IS is estimated. Besides, the obtained results can also be used for many practical systems, such as the interconnected power system, the multislave teleoperation systems, the brushless dc motor (BLDCM) system, and the chaotic system. Finally, the effectiveness and application of the obtained results are verified by several examples.

This paper presents general modeling approaches to achieve accurate real-time simulation of high switching frequency converters. The proposed methods are based on the direct mapped method (DMM) and make use of decoupling techniques when appropriate. The DMM links state variables to diode statuses and provides an exact and noniterative solution to network equations. An electric vehicle battery charger (EVBC) test case comprised of a full bridge rectifier, an interleaved boost, and a three-phase LLC is used to demonstrate the high accuracy achieved by the proposed methods compared to a conventional approach. FPGA implementations are proposed and shown to achieve from 75 ns to 175ns real-time simulation time-steps for this test case circuit, allowing its accurate simulation while switched at 200 kHz. To further validate the effectiveness of the FPGA-based simulator, a resonant boost converter is also implemented and simulated in real-time.

A stabilization approach for the average consensus of homogeneous multi-agent systems (MASs) entailing non-uniform, asymmetric, and time-varying delays in communication networks is proposed. Agents are modeled using a continuous-time linear dynamical system with a local discrete-time controller. To address communication delays, we propose two logic functions, namely, a packet selection algorithm that constantly selects and updates the latest received control signal packets and a synchronization algorithm that asymmetrically synchronizes these packets to preserve the average value of state variables. Based on the Lyapunov theory, we establish a linear matrix inequality (LMI) condition that sufficiently guarantees the stability and consensus of the overall system. To establish this condition, only the time delay upper bound is required, without any special assumptions on the delay pattern. We thoroughly discuss the proposed algorithm and evaluate its effectiveness via numerical simulations.

We propose a dynamic investment model to study investment behavior for power grid-stabilizing, flexibility-providing energy projects. Our model adds to the literature in several novel ways. First, we use time series-based optimization approaches (i.e., a single-unit commitment model) to determine, validate and parametrize exponential Ornstein–Uhlenbeck (OU) revenues as a state variable for the real options model in a biogas plant flexibilization case study. Second, from a theoretical perspective, our findings suggest that for specific revenue processes (i.e., exponential OU processes) the well-known option-driven incentive to wait acts alongside a new uncertainty-driven intrinsic investment incentive. Hence, higher uncertainty may lead to earlier investment. Third, we show how the results influence managerial decision making in practice and can be used to shape the design of an optimal renewable support scheme.

A cable-driven segmented manipulator (CDSM) has superior dexterity for operations in confined space due to its light-slender body and redundant degree of freedoms (DOFs). However, its inverse kinematics resolving and configuration planning are very challenging due to the complex structure and strict constraints. In this article, we propose a two-layer geometric iteration (TLGI) method for inverse kinematics resolving and configuration-constrained Cartesian path planning. The computation efficiency is largely improved and singularities are avoided. First, the end-effector attitude is decomposed into a direction vector and a rotation angle. The former and the end-effector position are combined into state variables of the inner layer, and the latter is treated separately as the state variable of the outer layer. Then, the TLGI method enables to rapidly reach the desired 6-DOF pose by two-layer iterations, i.e., the inner and outer loop iteration. Second, during the inner loop iteration, the CDSM is modeled as an equivalent articulated arm whose end-effector position and direction is the same as that of CDSM, but its links length and joint angles depend on the current configuration of CDSM. Then, the efficient forward and backward reaching inverse kinematics (FABRIKs) method is extended to apply on CDSM so that it can fast reach the inner state variables. During the outer loop iteration, three different rotation cases, i.e., the rotating around the end, root, and both end and root, are designed to switch automatically to reach the outer state variable iteratively. Moreover, by parameterizing geometric constraints of the environment, a TLGI-based configuration-pose simultaneous planning method is also put forward to efficiently achieve additional configuration constraints for operations of CDSM in confined space. Finally, the proposed method is verified by both the simulations and experiments.

This article addresses the trajectory consensus problem of coordinating the trajectories of vehicles at multiple future time points. The objective is the consensus of the geometry of the vehicles' planned trajectories. The geometric feature of trajectories is parameterized by a set of trajectory states defined as required lengths along the trajectory to reduce the distance to its ending point to specific values. To solve this special consensus problem involving coupled state variables, the conventional consensus model is extended by attaching it to a mapping from the state variables to the trajectory's geometry. This mapping is established using a homotopic structure that creates a compact and efficient form for the mapping. The geometry of the homotopic structure is based on the shapes of its envelopes, and the elements in the structure are derived from their deformation. Through a homotopic search in the structure, an asymptotic consensus of trajectory states is achieved. Simulation results show the proposed coupled state consensus method can achieve better performance on the consensus of multiple vehicles than the conventional isolated state consensus method.

We present a dynamical modeling of integrated (end-to-end) container terminal operations using finite state machine (FSM) framework where each state machine is represented by a discrete-event system (DES) formulation. The hybrid model incorporates the operations of quay cranes (QC), internal trucks (IT), and yard cranes (YC) and also the selection of storage positions in container yard (CY) and vessel bays. The QC and YC are connected by the IT in our models. As opposed to the commonly adapted modeling in container terminal operations, in which the entire information/inputs to the systems are known for a defined planning horizon, in this research we use real-time trucks, crane, and container storage operations information, which are always updated as the time evolves. The dynamical model shows that the predicted state variables closely follow the actual field data from a container terminal in Tanjung Priuk, Jakarta, Indonesia. Subsequently, using the integrated container terminal hybrid model, we proposed a model predictive algorithm (MPA) to obtain the near-optimal solution of the integrated terminal operations problem, namely the simultaneous allocation and scheduling of QC, IT, and YC, as well as selecting the storage location for the inbound and outbound containers in the CY and vessel. The numerical experiment based on the extensive Monte Carlo simulation and real dataset show that the MPA outperforms by 3-6% both of the policies currently implemented by the terminal operator and the state-of-the-art method from the current literature.

Underactuated dual boom crane (DBC) systems exhibit complicated nonlinearity and strong coupling due to the lack of independent actuators. Moreover, plant parameters may change in different transportation tasks and are difficult to be measured accurately, which leads to inaccurate gravity (torque) compensation and further brings positioning errors. Hence, most existing controllers based on exact model knowledge cannot ensure satisfactory control performance any longer. In order to handle the above issues, this article designs an adaptive sliding mode tracking controller for DBC systems based on the original complicated nonlinear dynamics without any linearization/simplification operations, which is the first one to effectively achieve both anti-swing and trajectory tracking control in the presence of parametric uncertainties. Not only can the state variables converge to the proposed sliding surface within finite time but also payload swing angles can be completely eliminated; consequently, the working efficiency and operation safety are further guaranteed. The corresponding stability and convergence for the equilibrium point of the closed-loop system are proven by rigorous mathematical analysis based on the Lyapunov techniques and Barbalat's lemma. Hardware experimental results demonstrate the effectiveness and robustness of the presented controller.

This paper presents a computationally efficient algorithm for eco-driving along horizons of over 100km. The eco-driving problem is formulated as a bi-level program, where the bottom level is solved offline, pre-optimising gear as a function of longitudinal velocity (kinetic energy) and acceleration. The top level is solved online, optimising a nonlinear dynamic program with travel time, kinetic energy and acceleration as state variables. To further reduce computational effort, the travel time is adjoined to the objective by applying necessary Pontryagin's Maximum Principle conditions, and the nonlinear program is solved using real-time iteration sequential quadratic programming scheme in a model predictive control framework. Compared to average driver's driving cycle, the energy savings of using the proposed algorithm is up to 11.60%.

This paper suggests a methodology for the identification, classification, and evaluation of various types of interactions that may occur in an HVDC link based on modular multi-level converters (MMC). The methodology incorporates the most suitable analytic tools for the frequency-domain study of each interaction category. To do so, a detailed nonlinear model of an MMC-based HVDC link that consists of master and slave MMCs, AC grids, and the DC transmission system is derived. Then, it is linearized to obtain a multi-input multi-output (MIMO) linear model that represents the dynamics of the complete MMC-based HVDC link. Based on the control loops of interest, interactions are classified as (1) state variable interactions, (2) disturbance interactions, (3) control loop interactions, and (4) overall system interactions. Then, through the application examples, the mentioned four categories of interactions are studied in frequency domain via the relevant analytic tools. The results obtained from the frequency-domain analysis are validated by time-domain simulation.

The adaptive neural network (NN) output-feedback optimal control issue has been investigated for a quarter-car active electric suspension systems, where the suspension stiffness is unknown and partial state variables are unavailable for measurement. NNs are utilized to identify unknown nonlinearities, and an NN state observer is devised to estimate the unmeasurable states. For each backstepping step, via reinforcement learning (RL), a critic-actor architecture is designed to get the approximation solution of Hamilton-Jacobi-Bellman (HJB) equations and actual and virtual optimization controllers are designed, in which the input saturation constraint and road interference are considered. It is analytically proved that all controlled system signals remain bounded, while the power of the control input signal, as well as the amplitude of the vertical displacement, has been minimized. A comparative simulation is eventually given to elaborate the feasibility of the developed control algorithm.

This paper investigates the transient stability of power systems co-dominated by different types of grid-forming (GFM) devices. Synchronous generators (SGs and VSGs) and droop-controlled inverters are typical GFM devices in modern power systems. SGs/VSGs are able to provide inertia while droop-controlled inverters are generally inertialess. The transient stability of power systems dominated by homogeneous GFM devices has been extensively studied. Regarding the hybrid system jointly dominated by heterogeneous GFM devices, the transient stability is rarely reported. This paper aims to fill this gap. It is found that the synchronization behavior of the hybrid system can be described by a second-order motion equation, resembling the swing equation of SGs. Moreover, two significant differences from conventional power systems are discovered. The first is that the droop control dramatically enhances the damping effect, greatly affecting the transient stability region. The second is that the frequency state variable exhibits a jump at the moment of fault disturbances, thus impacting the post-fault initial-state location and stability assessment. The underlying mechanism behind the two new characteristics is clarified and the impact on the transient stability performance is analyzed and verified. The findings provide new insights into transient stability of power systems hosting heterogeneous devices.

This paper proposes a novel, disturbance observer-based decentralized frequency control method for interconnected power systems. The method employs Extended Kalman Filter (EKF) as an observer to estimate inaccessible dynamic states of the system, including the total disturbance as one of the state variables. An optimal decentralized disturbance observer based controller (DOBC) is suggested for multi-area power systems that compensates the estimated disturbance and further based on minimizing the joint error energies of state estimation error and state tracking error provides its value to the controller for regulating the frequency variation. The proposed method is mathematically designed to be robust against parametric and non-parametric uncertainties. The efficacy and accuracy of the proposed control method is verified considering different types of practical operation scenarios. The results confirm the brilliant and superiority of the proposed method in controlling the frequency in power systems with high renewable shares.

Due to the modulator free structure, the finite-control-set model predictive control (FCS-MPC) needs a high sampling frequency (20kHz-50kHz) for power converter applicationwhile the typical switching frequency is around 20-25% of the sampling frequency. To obtain a high switching frequency, it is not always practical to increase the sampling rate by considering the computational burden in a digital processor. Therefore, increasing the switching frequency without using a high sampling frequency is a critical task for FCS-MPC, particularly applied to silicon carbide (Sic) and gallium nitride (GaN)-based high switching frequency power converters. To solve this problem, this article proposes the multi-rate FCS-MPC (MRFCS-MPC), where the control frequency is allowed to be faster than the sampling frequency. Consequently, the switching frequency can be significantly increased without changing the sampling frequency. The lifting model is built to predict the fast rate state variables based on the low sampling output. Then, the fast rate control inputs within one sampling interval are solved efficiently with a good tradeoff between computational burden and optimized system performance. The experimental motor drive system tests are carried out to verify the effectiveness of the proposed MRFCS-MPC.

The objective of the design and operation of any water distribution network (WDN) includes meeting the desired demand at sufficient pressure at all nodes. However, this requires situational awareness; in other words, the knowledge of system state variables such as pressure and flow throughout the network. In this work, a hybrid approach is developed for sensor placement (SP) and state estimation (SE) that exploits the underlying correlation structure in the data, along with the principles governing the flow through circular pipes. The problem of SP in WDN is addressed since measuring the state variables throughout the network is not practical. The problem of SE that maps to a matrix completion problem under certain physical and logical constraints is solved later. The completed matrix represents the state of WDN at any given time. Benchmark networks used in literature were used to evaluate the proposed approach. The mean absolute percentage error (MAPE) of less than 5% was obtained while estimating the head available at nodes. The knowledge of the states in the entire network could help operate the network adaptively.

This brief is concerned with a new problem of designing a robust adaptive backstepping controller for nonlinear systems where state constraints are an explicit function of time and state variables, i.e., pure state constraints. Furthermore, a disturbance observer is designed to cope with the disturbances in the systems. Finally, a numerical example is demonstrated to show the efficacy of the proposed theoretical results.

A wireless power transfer (WPT) system in domino structure is a high-order system with a number of state variables. Thus, the controller design for the power electronic converters has been very challenging. With the aid of phasor transformation, a simple representation of the system could be derived. In this article, the procedure of creating a dynamic model for a WPT system in domino structure is introduced. The model allows the estimation of receiver-side operating condition form the transmitter. Using the phasor transformation technique, the varying phasors of the resonant voltages and currents in the WPT system are taken as state variables, rather than their rapid changing real-time values. Compared to traditional analytical methods, lower computational cost is significantly benefited from the system order reduction. The dynamic model is validated by experimental measurement results. The results show good accuracy of input and output voltage and current prediction. The dynamic model provides a deep insight into the system behavior. The dynamic model could play an important role in the future development of control algorithms to further advance the performance of the WPT system.

In this paper, an improved deadbeat predictive stator flux control (DPSFC) based on disturbance observer is proposed to improve the control performance of in-wheel permanent magnet synchronous motors (PMSMs) with parameter mismatch and disturbance. First, the sensitivity of conventional deadbeat predictive current control to the parameter variation, including flux linkage, stator resistance and stator inductance, is analyzed. Then, a reduced-order observer based on additional disturbance state variables is designed to predict the future stator flux and observe the system disturbance caused by parameter mismatch. The proposed DPSFC method is able to enhance the robustness of the drive performance effectively via the compensations of one-step delay and stator voltage. Finally, the performance of the proposed control method is validated by simulations and experiments on a prototype of an in-wheel PMSM drive.

This work presents a novel methodology for systematically processing the time series that report the number of positive, recovered and deceased cases from a viral epidemic, such as Covid-19. The main objective is to unveil the evolution of the number of real infected people, and consequently to predict the peak of the epidemic and subsequent evolution. For this purpose, an original nonlinear model relating the raw data with the time-varying geometric ratio of infected people is elaborated, and a Kalman Filter is used to estimate the involved state variables. A hypothetical simulated case is used to show the adequacy and limitations of the proposed method. Then, several countries, including China, South Korea, Italy, Spain, UK and the USA, are tested to illustrate its behavior when real-life data are processed. The results obtained clearly show the beneficial effect of the social distancing measures adopted worldwide, confirming that the Covid-19 epidemic peak is left behind in those countries where the outbreak started earlier, and anticipating when the peak will take place in the remaining countries.

In this study, a modeling method for investigating the dynamic characteristics of a hydraulically driven shell manipulator with revolute clearance joints is presented. This model accounts for the effect of the clearance, the flexibility of the rotating beam, and the coupled dynamic characteristics of the hydraulic cylinder. A modified contact force model was developed to simulate the physical properties of realistic revolute joints with small clearances, heavy loads, and variable contact stiffnesses and damping coefficients with variations of the indentations. Considering the strong coupling relationship between the hydraulic cylinder and the flexible beam, a system equation of motion combining the state variables of the hydraulic cylinder and mechanical system was established. The complex nonlinear friction force of the hydraulic cylinder motion was constructed using a modified LuGre model, and the parameters of the friction model were identified using intelligent identification algorithms. Moreover, a test system for the shell manipulator was established to achieve experimental validation. Finally, the effects of the clearance size and the stiffness of the cylinder support on the dynamic response were investigated.

The current small-signal models of inverters that mimic synchronous generators usually use the steady-state equation of feeder line and load, and ignore the dynamic characteristics of voltage and current controller of inner loop. Thus, some important dynamics of the system will be omitted. To capture the dynamics more accurately, firstly, a small-signal model of a three-phase isolated inverter that mimics a synchronous generator was developed in this paper, and a comparison between the corresponding simulink model and the small-signal model was presented to validate the model correctness. Then, participation factor was calculated, which showed the relation between the state variables and eigenvalues. Based on the model, several root locus plots as a function of the system parameters was obtained. An individual eigenvalue sensitivity analysis of a certain eigenvalue close to the real axis disclosed different parameters’ contribution to the oscillation mode. Finally, a disturbance test with simulink model and the small-signal model under multiple sets of parameters was carried out and the results can accurately reflect the oscillation mode, which verified the correctness of the parameters effect on the conjugate eigenvalues.

Tematika ovog rada vezana je uz proračun estimacije stanja u prijenosom sustavu. Estimacija stanja ili procjena stanja je alat koji se koristi za analizu i kontrolu elektroenergetskog sustava. Svrha rada je ta da se objasni proračun estimacije stanja u prijenosnom sustavu. U teoretskom dijelu opisana je estimacija stanja, SCADA te detekcija loših podataka. U praktičnom dijelu izvršena je simulacija estimacije stanja na generičkoj mreži pomoću programskih paketa Neplan 360 i Torrit. Program Neplan 360 služio je kao SCADA, a program Torrit kao pomoćni program za estimaciju stanja. The topic of this paper is related to the state estimation calculation in the transmission system. State estimation is a tool used to analyze and control the power system. The purpose of the work is then to explain the state estimation calculation in the transmission system. State estimation, SCADA and detection of bad data are described in the theoretical part. In the practical part, a simulation of state estimation was performed on a generic network using the Neplan 360 and Torrit software packages. The Neplan 360 program served as SCADA, and the Torrit program as an auxiliary program for state estimation.

We propose a framework to study optimal trading policies in a one-tick pro-rata limit order book, as typically arises in short-term interest rate futures contracts. The high-frequency trader has the choice to trade via market orders or limit orders, which are represented respectively by impulse controls and continuous controls. We model and discuss the consequences of the two main features of this particular microstructure: first, the limit orders sent by the high frequency trader are only partially executed, and therefore she has no control on the executed quantity. For this purpose, we model cumulative executed volumes by compound Poisson processes. Second, the high frequency trader faces the overtrading risk, which is the risk of brutal variations in her inventory. The consequences of this risk are investigated in the context of optimal liquidation. We study the regular/impulse control problem using dynamic programming methods, which leads to a characterization of the value function in terms of an integro quasi-variational inequality. We then provide the associated numerical resolution procedure, and convergence of this computational scheme is proved. Next, we examine several situations where we can one one hand simplify the numerical procedure by reducing the number of state variables, and on the other hand focus on specific cases of practical interest. We examine both a market making problem and a best execution problem in the case where the mid-price process is a martingale. We also detail a high frequency trading strategy in the case where a (predictive) directional information on the mid-price is available. Each of the resulting strategies are illustrated by numerical tests.

This article investigates the leader-following output consensus problem of heterogeneous linear multiagent systems, where the nonidentical followers are subject to norm bounded parameter uncertainties. Two novel distributed event-triggered consensus strategies based on state feedback and output feedback, respectively, are proposed to solve the leader-following output consensus problem. A dynamic event-triggering mechanism which involves internal state variables is proposed to determine the next triggering instant. It should be noted that the controllers can be designed without the knowledge of some global graph information, such as the eigenvalues of the Laplacian matrix associated with the corresponding communication graph and the number of agents. In addition, the interevent time can be prolonged and the Zeno behavior can be excluded with the proposed dynamic triggering mechanism. Finally, an example is presented to illustrate the effectiveness of the proposed controllers.

For reduce the high inrush current caused by the low impedance of the resonant tank of LLC resonant converter during the start-up process, this paper proposes a PWM strategy based on the event triggered control (ETC) method. In order to select the trigger event, the segmented state space modeling is used to analyze the relationship between the system state variables and the driving signals. Then, it selects the current and the capacitor voltage in the resonant tank to establish the ETC function to obtain the PWM strategy for the start-up process. When the output voltage is gradually established, the amplitude of the resonant current will be less than the pre-set threshold. Then the resonant capacitor voltage can be used to trigger the event. Finally, by comparing with the experimental results using the traditional soft start-up strategy, the proposed PWM strategy can limit the current and the capacitor voltage in the resonant tank during the start-up process to below the pre-set threshold, while greatly reducing the settling time of output voltage. In addition, the ETC method can achieve smooth switching from the start-up process to the steady state.

This paper proposes a systematic framework of AC/DC grid state estimation with higher accuracy, computational tractability, and robustness against measurement and control input errors. First, tractable lossy models of bidirectional isolated DC/DC voltage source converters (VSCs), bidirectional DC/AC VSCs, and DC lines for state estimation are developed. Then, the state variables of the DC grid are selected, and measurement equations are derived. Finally, combined with the state estimation model of AC grids, a general framework for the robust AC/DC transmission grid state estimation against both measurement errors and control input errors is presented. Simulation results show that the proposed framework can effectively capture the operating state of AC/DC grid, and can suppress the impact of simultaneous measurement errors and control input errors.