A hybrid genetic algorithm for the electric vehicle routing problem with time windows

Driven by the newlegislation on greenhouse gas emissions, carriers began to use electric vehicles (EVs) for logistics transportation. This paper addresses an electric vehicle routing problem with time windows (EVRPTW). The electricity consumption of EVs is expressed by the battery state-of-charge (SoC). To make it more realistic, we take into account the terrain grades of roads, which affect the travel process of EVs. Within our work, the battery SoC dynamics of EVs are used to describe this situation. We aim to minimize the total electricity consumption while serving a set of customers. To tackle this problem, we formulate the problem as a mixed integer programming model. Furthermore, we develop a hybrid genetic algorithm (GA) that combines the 2-opt algorithm with GA. In simulation results, by the comparison of the simulated annealing (SA) algorithm and GA, the proposed approach indicates that it can provide better solutions in a short time.     

Speed planning and energymanagement strategy of hybrid electric vehicles in a car-following scenario

The development of intelligent connected technology has brought opportunities and challenges to the design of energy management strategies for hybrid electric vehicles. First, to achieve car-following in a connected environment while reducing vehicle fuel consumption, a power split hybrid electric vehicle was used as the research object, and a mathematical model including engine, motor, generator, battery and vehicle longitudinal dynamics is established. Second, with the goal of vehicle energy saving, a layered optimization framework for hybrid electric vehicles in a networked environment is proposed. The speed planning problem is established in the upper-level controller, and the optimized speed of the vehicle is obtained and input to the lower-level controller. Furthermore, after the lower-level controller reaches the optimized speed, it distributes the torque among the energy sources of the hybrid electric vehicle based on the equivalent consumption minimum strategy. The simulation results show that the proposed layered control framework can achieve good car-following performance and obtain good fuel economy.     

Real-time energy optimization of HEVs under-connected environment: a benchmark problem and receding horizon-based solution

In this paper, we propose a benchmark problem for the challengers aiming to energy efficiency control of hybrid electric vehicles (HEVs) on a road with slope. Moreover, it is assumed that the targeted HEVs are in the connected environment with the obtainment of real-time information of vehicle-to-everything (V2X), including geographic information, vehicle-toinfrastructure (V2I) information and vehicle-to-vehicle (V2V) information. The provided simulator consists of an industriallevel HEV model and a traffic scenario database obtained through a commercial traffic simulator, where the running route is generated based on real-world data with slope and intersection position. The benchmark problem to be solved is the HEVs powertrain control using traffic information to fulfill fuel economy improvement while satisfying the constraints of driving safety and travel time. To show the HEV powertrain characteristics, a case study is given with the speed planning and energy management strategy.     

Fast adaptive regression-basedmodel predictive control

Model predictive control (MPC) is an optimal control method that predicts the future states of the system being controlled and estimates the optimal control inputs that drive the predicted states to the required reference. The computations of the MPC are performed at pre-determined sample instances over a finite time horizon. The number of sample instances and the horizon length determine the performance of the MPC and its computational cost. A long horizon with a large sample count allows the MPC to better estimate the inputs when the states have rapid changes over time, which results in better performance but at the expense of high computational cost. However, this long horizon is not always necessary, especially for slowly-varying states. In this case, a short horizon with less sample count is preferable as the same MPC performance can be obtained but at a fraction of the computational cost. In this paper,we propose an adaptive regression-based MPC that predicts the bestminimum horizon length and the sample count from several features extracted from the time-varying changes of the states. The proposed technique builds a synthetic dataset using the system model and utilizes the dataset to train a support vector regressor that performs the prediction. The proposed technique is experimentally compared with several state-of-the-art techniques on both linear and non-linear models. The proposed technique shows a superior reduction in computational time with a reduction of about 35–65% compared with the other techniques without introducing a noticeable loss in performance.     

Real-time energy management strategy based on predictive cruise control for hybrid electric vehicles

With the help of traffic information of the connected environment, an energy management strategy (EMS) is proposed based on preceding vehicle speed prediction, host vehicle speed planning, and dynamic programming (DP) with PI correction to improve the fuel economy of connected hybrid electric vehicles (HEVs). A conditional linear Gaussian (CLG) model for estimating the future speed of the preceding vehicle is established and trained by utilizing historical data. Based on the predicted information of the preceding vehicle and traffic light status, the speed curve of the host vehicle can ensure that the vehicle follows safety and complies with traffic rules simultaneously as planned. The real-time power allocation is composed of offline optimization results of DP and the real-time PI correction items according to the actual operation of the engine. The effectiveness of the control strategy is verified by the simulation system of HEVs in the interconnected environment established by E-COSM 2021 on the MATLAB/Simulink and CarMaker platforms.     

Efective Object Identification and Association by Varying Coverage Through RFID Power Control简

This paper presents an effective power scheduling strategy for energy efficient multiple objects identification and association. The proposed method can be utilized in many heterogeneous surveillance systems with visual sensors and RFID （radio-frequency identification） readers where energy efficiency as well as association rate are critical Multiple objects positions and trajectory estimates are used to decide the power level of RFID readers. Several key parameters including the time windows and the distance separations are defined in the method in order to minimize the effects of RFID coverage uncertainty. The power cost model is defined and incorporated into the method to minimize energy consumption and to maximize association performance. The proposed method computes the power cost using the range of the outermost position for possible single association and group associations at every sampling time. An RFID reader is activated with the proper coverage range when the power cost for the current time is lower than the power cost for the next time sample. The simplicity of the power cost model relieves the problematic combinatorial comparisons in multiple object cases. The performance comparison simulation with the minimum and maximum energy consumption shows that the proposed method achieves fast single associations with less energy consumption. Finally, the realistic comparison simulation with the fixed range RFID readers demonstrates that the proposed method outperforms the fixed ranges in terms of single association rate and energy consumption.     

Algebraic form and analysis of SIR epidemic dynamics over probabilistic dynamic networks

The outbreak of corona virus disease 2019 has profoundly affected people’s way of life. It is increasingly necessary to investigate epidemics over social networks. This paper studies susceptible-infected-removed (SIR) epidemics via the semitensor product. First, a formal susceptible-infected-removed epidemic dynamic model over probabilistic dynamic networks (SIRED-PDN) is given. Based on an evolutionary rule, the algebraic form for the dynamics of individual states and network topologies is given, respectively. Second, the SIRED-PDN can be described by a probabilistic mix-valued logical network. After providing an algorithm, all possible final spreading equilibria can be obtained for any given initial epidemic state and network topology by seeking attractors of the network. And the shortest time for all possible initial epidemic state and network topology profiles to evolve to the final spreading equilibria can be obtained by seeking the transient time of the network. Finally, an illustrative example is given to show the effectiveness of our model.     

Minimum energy optimal external torque control of human binocular vision

In this paper, we consider eyes from the human binocular system, that simultaneously gaze on stationary point targets in space, while optimally skipping from one target to the next, by rotating their individual gaze directions. The head is assumed fixed on the torso and the rotating gaze directions of the two eyes are assumed restricted to pass through a point in the visual space. It is further assumed that, individually the rotations of the two eyes satisfy the well known Listing’s law. We formulate and study a combined optimal gaze rotation for the two eyes, by constructing a single Riemannian metric, on the associated parameter space. The goal is to optimally rotate so that the convergent gaze changes between two pre-specified target points in a finite time interval [0, 1]. The cost function we choose is the total energy, measured by the L2 norm, of the six external torques on the binocular system. The torque functions are synthesized by solving an associated ‘two-point boundary value problem’. The paper demonstrates, via simulation, the shape of the optimal gaze trajectory of the focused point of the binocular system. The Euclidean distance between the initial and the final point is compared to the arc-length of the optimal trajectory. The consumed energy, is computed for different eye movement chores and discussed in the paper. Via simulation we observe that certain eye movement maneuvers are energy efficient and demonstrate that the optimal external torque is a linear function in time. We also explore and conclude that splitting an arbitrary optimal eye movement into optimal vergence and version components is not energy efficient although this is how the human oculomotor control seems to operate. Optimal gaze trajectories and optimal external torque functions reported in this paper is new.     

Real-time energy-efficient anticipative driving control of connected and automated hybrid electric vehicles

In this paper, we propose a real-time energy-efficient anticipative driving control strategy for connected and automated hybrid electric vehicles (HEVs). Considering the inherent complexities brought about by the velocity profile optimization and energy management control, a hierarchical control architecture in the model predictive control (MPC) framework is developed for real-time implementation. In the higher level controller, a novel velocity optimization problem is proposed to realize safe and energy-efficient anticipative driving. The real-time control actions are derived through a computationally efficient algorithm. In the lower level controller, an explicit solution of the optimal torque split ratio and gear shift schedule is introduced for following the optimal velocity profile obtained from the higher level controller. The comparative simulation results demonstrate that the proposed strategy can achieve approximately 13% fuel consumption saving compared with a benchmark strategy.     

Output digitization of simple measure‑preserving linear systems

We examine three simple linear systems from the viewpoint of ergodic theory. We digitize the output and record only the sign of the output at integer times. We show that even with this minimal output we can recover important information about the systems. In particular, for a two-dimensional system viewed as a flow on the circle, we can determine the rate of rotation. We then use these results to determine the slope of the trajectories for constant irrational flow on the two-dimensional torus. To achieve this, we randomize the system by partitioning the state space and only recording which partition the state is in at each integer time. We show directly that these systems have entropy zero. Finally, we examine two four-dimensional systems and reduce them to the study of linear flows on the two-dimensional torus.     

Energy-efficient power allocation for selfish cooperative communication networks using bargaining game

In commercial networks,user nodes operating on batteries are assumed to be selfish to consume their energy solely to maximize their own benefits,e.g.,data rates.In this paper,we propose a bargaining game to perform the power allocation for the selfish cooperative communication networks.In our system,two partner nodes can act as a source as well as a relay for each other,and each node is with an energy constraint to transmit one frame.Consider a selfish node is willing to seek cooperative transmission only if the data rate achieved through cooperation will not lower than that achieved through noncooperation by using the same amount of energy.The energy-efficient power allocation problem can be modeled as a cooperative game.We proved that there exists a unique Nash bargaining solution (NBS) for the game by verifying that the game is indeed a bargaining problem.Thus,the two objectives,i.e.,system efficiency and user fairness specified in the selfish networks can be achieved.Simulation results show that the NBS scheme is efficient in that the performance loss of the NBS scheme to that of the maximal overall rate scheme is small while the maximal-rate scheme is unfair.The simulation results also show that the NBS result is fair in that both nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance.     

Popular route planning with travel cost estimation from trajectories

With the increasing number of GPS-equipped vehicles,more and more trajectories are generated continuously,based on which some urban applications become feasible,such as route planning.In general,popular route that has been travelled frequently is a good choice,especially for people who are not familiar with the road networks.Moreover,accurate estimation of the travel cost(such as travel time,travel fee and fuel consumption)will benefit a wellscheduled trip plan.In this paper,we address this issue by finding the popular route with travel cost estimation.To this end,we design a system consists of three main components.First,we propose a novel structure,called popular traverse graph where each node is a popular location and each edge is a popular route between locations,to summarize historical trajectories without road network information.Second,we propose a self-adaptive method to model the travel cost on each popular route at different time interval,so that each time interval has a stable travel cost.Finally,based on the graph,given a query consists of source,destination and leaving time,we devise an efficient route planning algorithmwhich considers optimal route concatenation to search the popular route from source to destination at the leaving time with accurate travel cost estimation.Moreover,we conduct comprehensive experiments and implement our system by a mobile App,the results show that our method is both effective and efficient.     

Event-triggered H∞ consensus control for input-constrained multi-agent systems via reinforcement learning

This article presents an event-triggered H∞ consensus control scheme using reinforcement learning (RL) for nonlinear second-order multi-agent systems (MASs) with control constraints. First, considering control constraints, the constrained H∞ consensus problem is transformed into a multi-player zero-sum game with non-quadratic performance functions. Then, an event-triggered control method is presented to conserve communication resources and a new triggering condition is developed for each agent to make the triggering threshold independent of the disturbance attenuation level. To derive the optimal controller that can minimize the cost function in the case of worst disturbance, a constrained Hamilton–Jacobi–Bellman (HJB) equation is defined. Since it is difficult to solve analytically due to its strongly non-linearity, reinforcement learning (RL) is implemented to obtain the optimal controller. In specific, the optimal performance function and the worst-case disturbance are approximated by a time-triggered critic network; meanwhile, the optimal controller is approximated by event-triggered actor network. After that, Lyapunov analysis is utilized to prove the uniformly ultimately bounded (UUB) stability of the system and that the network weight errors are UUB. Finally, a simulation example is utilized to demonstrate the effectiveness of the control strategy provided.     

Output feedback adaptive super twisting sliding mode control for quadrotor UAVs

In this paper, a sliding mode control with adaptive gain combined with a high-order sliding mode observer to solve the tracking problem for a quadrotor UAV is addressed, in presence of bounded external disturbances and parametric uncertainties. The high order sliding mode observer is designed for estimating the linear and angular speed in order to implement the proposed scheme. Furthermore, a Lyapunov function is introduced to design the controller with the adaptation law, whereas an analysis of finite time convergence towards to zero is provided, where sufficient conditions are obtained. Regarding previous works from literature, one important advantage of proposed strategy is that the gains of control are parameterized in terms of only one adaptive parameter, which reduces the control effort by avoiding gain overestimation. Numerical simulations for tracking control of the quadrotor are given to show the performance of proposed adaptive control–observer scheme.     

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.     

On-board torquemanagement approach to the E-COSM benchmark problem with a prediction-based engine assignment

This paper proposes an energy management strategy for the benchmark problem of E-COSM 2021 to improve the energy efficiency of hybrid electric vehicles (HEVs) on a road with a slope. We assume that HEVs are in a connected environment with real-time vehicle-to-everything information, including geographic information, vehicle-to-infrastructure information and vehicle-to-vehicle information. The benchmark problem to be solved is based on HEV powertrain control using traffic information to achieve fuel economy improvements while satisfying the constraints of driving safety and travel time. The proposed strategy includes multiple rules and model predictive control (MPC). The rules of this strategy are designed based on external environment information to maintain safe driving and to determine the driving mode. To improve fuel economy, the optimal energy management strategy is primarily considered, and to perform real-time energy management via RHC-based optimization in a connected environment with safety constraints, a key issue is to predict the dynamics of the preceding vehicle during the targeted horizon. Therefore, this paper presents a real-time model-based optimization strategy with learning-based prediction of the vehicle’s future speed. To validate the proposed optimization strategy, a powertrain control simulation platform in a traffic-in-the-loop environment is constructed, and case study results performed on the constructed platform are reported and discussed.     

U-shaped energy loss curves utilization for distributed generation optimization in distribution networks

We propose novel techniques to find the optimal achieve the maximum loss reduction for distribution networks location, size, and power factor of distributed generation （DG） to Determining the optimal DG location and size is achieved simultaneously using the energy loss curves technique for a pre-selected power factor that gives the best DG operation. Based on the network＇s total load demand, four DG sizes are selected. They are used to form energy loss curves for each bus and then for determining the optimal DG options. The study shows that by defining the energy loss minimization as the objective function, the time-varying load demand significantly affects the sizing of DG resources in distribution networks, whereas consideration of power loss as the objective function leads to inconsistent interpretation of loss reduction and other calculations. The devised technique was tested on two test distribution systems of varying size and complexity and validated by comparison with the exhaustive iterative method （EIM） and recently published results. Results showed that the proposed technique can provide an optimal solution with less computation.     

A new energy optimal control scheme for a separately excited DC motor based incremental motion drive

This paper considers minimization of resistive and frictional power dissipation in a separately excited DC motor based incremental motion drive （IMD）. The drive is required to displace a given, fixed load through a definite angle in specified time, with minimum energy dissipation in the motor windings and minimum frictional losses. Accordingly, an energy optimal （EO） control strategy is proposed in which the motor is first accelerated to track a specific speed profile for a pre-determined optimal time period. Thereafter, both armature and field power supplies are disconnected, and the motor decelerates and comes to a halt at the desired displacement point in the desired total displacement time. The optimal time period for the initial acceleration phase is computed so that the motor stores just enough energy to decelerate to the final position at the specified displacement time. The parameters, such as the moment of inertia and coefficient of friction, which depend on the load and other external conditions, have been obtained using system identification method. Comparison with earlier control techniques is included. The results show that the proposed EO control strategy results in significant reduction of energy losses compared to the existing ones.     

Improving the classification accuracy using biomarkers selected from machine learning methods

High-dimensional data encountered in genomic and proteomic studies are often limited by the sample size but has a higher number of predictor variables. Therefore selecting the most relevant variables that are correlated with the outcome variable is a crucial step. This paper describes an approach for selecting a set of optimal variables to achieve a classification model with high predictive accuracy. The work described using a biological classifier published elsewhere but it can be generalized for any application.     

Support optimal scheduling with weighted random forest for operation resources

Operation-related resources are lots of manpower and material with the characteristics of high cost and high income in hospitals, and scheduling optimization is a very important research issue in medical service. In this paper, to cope with the actualities of operation resources scheduling, such as poor planning, lack of standardized scheduling rules, chaotic use of the operating rooms, and many human interference factors, we propose a systematic approach to optimize scheduling problems based on multiple characteristics of operating resources. We first design a framework that includes the composite dispatching rules (CDR), optimization ideology, and feedback mechanism, in which the CDR integrates flexible operating time, hold-up time of medical facilities, available time of medical staff, and multiple constraints. The optimization ideology is carried out through a learning model based on the weighted random forest (WRF) algorithm. The feedback mechanism enables the approach to realize closed-loop optimizations adaptively. Finally, the superiority of the systematic scheduling approach (SSA) is analyzed through numerical experiments on a simulation platform. Results of the simulation experiments show that the proposed scheduling method can improve performances significantly, especially in the waiting time of patients.