This paper investigates a novel post-disaster power recovery scenario in which shelter batteries are subject to dynamic charging time constraints determined by their state-of-charge ($SoC$) dynamics. In this context, electric vehicles (EVs) serve as both transportation resources and mobile energy storage units. The objective is to determine an EV delivery plan that satisfies the power demand of shelter batteries within their dynamic charging time constraints while minimizing the total fleet travel costs. In addition, road-grade effects on EV energy consumption during travel are incorporated. In the first phase, a reduced graph is extracted from the original disaster-affected network to simplify the network topology. In the second phase, the problem is formulated as a mixed-integer linear programming (MILP) model. To obtain a high-quality delivery plan, an adaptive large neighborhood search (ALNS) algorithm is developed with problem-specific destroy and repair operators. The performance of the proposed ALNS is evaluated through comprehensive numerical experiments on benchmark instances of varying sizes constructed from a public road network. The results demonstrate that the proposed ALNS outperforms state-of-the-art methods. Finally, sensitivity analyses are conducted to quantify the impacts of uncertainties, time constraints, road grades, and reduced-graph construction.

In the past decades, the energy optimization strategy for human-driver hybrid electric vehicles (HEVs) has been widely investigated to improve fuel economy. With the highly increasing penetration of connected and automated HEVs, fuel consumption reduction can be achieved further. This paper explores the potential to reduce the whole fuel consumption by jointly optimizing speed dynamic and HEV powertrain operation for a large population of connected and automated HEVs. To avoid the computation burden of the large population of HEVs, a novel decentralized control scheme is designed by employing the mean field game (MFG) theory. Thanks to the communication by vehicle to everything (V2X) that the real-time velocity distribution of the large population of HEVs is available for each HEV, the receding horizon sense is introduced to MFG to avoid the open-loop prediction error of velocity distribution within the optimization horizon. The solution is derived from a novel numerical method for the general nonlinear MFG problem. A virtual connected traffic simulation platform is built in MATLAB/Simulink, and simulations are conducted to show the effectiveness of the proposed strategy.

This paper presents a new approach for solving the optimal merging control problem for hybrid electric vehicles (HEVs) under a connected environment. To achieve a reduction in energy consumption and save travel time, this paper focuses on deriving a decentralized feedback control law that provides not only the optimal velocity trajectory for merging but also a torque distribution strategy for the HEV powertrain. For this purpose, a distance domain-based optimal control problem is first proposed to avoid a free end-time cost function formulation that usually arises due to considering the minimization of traveling time. Then, the vehicle dynamics take into account the constraint of the optimization problem to evaluate the energy consumption at the power device level instead of the acceleration, unlike the common method used for reducing energy consumption in previous studies of merging control with linear models. The proposed optimization problem is solved by Pontryagin’s maximum principle, and a traffic-in-the-loop powertrain simulation platform with a real-world emulated traffic scenario and high-fidelity HEV powertrain model is constructed to eliminate the randomly generated merging scenario. Finally, the simulation results obtained on the platform are demonstrated to validate the effectiveness of the proposed decentralized merging control law.

In this article, a novel integral reinforcement learning (IRL) algorithm is proposed to solve the optimal control problem for continuous-time nonlinear systems with unknown dynamics. The main challenging issue in learning is how to reject the oscillation caused by the externally added probing noise. This article challenges the issue by embedding an auxiliary trajectory that is designed as an exciting signal to learn the optimal solution. First, the auxiliary trajectory is used to decompose the state trajectory of the controlled system. Then, by using the decoupled trajectories, a model-free policy iteration (PI) algorithm is developed, where the policy evaluation step and the policy improvement step are alternated until convergence to the optimal solution. It is noted that an appropriate external input is introduced at the policy improvement step to eliminate the requirement of the input-to-state dynamics. Finally, the algorithm is implemented on the actor-critic structure. The output weights of the critic neural network (NN) and the actor NN are updated sequentially by the least-squares methods. The convergence of the algorithm and the stability of the closed-loop system are guaranteed. Two examples are given to show the effectiveness of the proposed algorithm.

When a group of autonomous surface vehicles (ASVs) sail from a wide waterway to a narrow waterway, one difficulty is to keep relative formation with collision avoidance. Scaling the formation sizes with formation shapes invariant is a promising way. This article investigates such a formation scaling control problem of ASVs with uncertainties and input saturation. A novel bearing-based adaptive neural formation scaling control scheme for ASVs is developed. The main idea of this formation scheme is as follows. Choose a small number of leader ASVs based on bearing rigidity theory and program their trajectories according to the kinematics of formation scaling maneuver. Steer remaining ASVs to follow leader ASVs via adaptive neural techniques and the formation sizes can be scaled only by leaders without redesigning control inputs of followers. To deal with the uncertainties of ASVs, weights updating of neural networks is simplified into one-parameter estimation in each control channel. Auxiliary systems are introduced for each ASV to reduce the effect of limited actuator capability. It is shown that desired formation scaling maneuver of ASVs can be achieved with the proposed formation scheme if the augmented formation is infinitesimally bearing rigid. Formation errors are guaranteed to be uniformly ultimately bounded. The main advantage of our scheme over existing results is that directional, computational, and actuator constraints are satisfied simultaneously in the formation scaling control of ASVs. Simulations and comparisons are provided to illustrate the effectiveness of theoretical results.

This paper investigates an energy management problem of parallel hybrid electric vehicles (HEVs), which can be modeled as a finite horizon optimal control problem for the discrete dynamical systems. Taking the essential characteristics of plug-in HEVs into account, a logical-based optimization approach is applied to realize the equivalent energy cost minimization of the powertrain system. Then, based on semi-tensor product, an effective algorithm for obtaining an approximate optimal solution is proposed by using the logical network-based approach. Finally, simulation results are presented to illustrate and show the effectiveness of the proposed optimal control scheme and the corresponding algorithm.

This brief investigates the infinite horizon optimal control problem for stochastic multivalued logical dynamical systems with discounted cost. Applying the equivalent descriptions of stochastic logical dynamics in term of Markov decision process, the discounted infinite horizon optimal control problem is presented in an algebraic form. Then, employing the method of semitensor product of matrices and the increasing-dimension technique, a succinct algebraic form of the policy iteration algorithm is derived to solve the optimal control problem. To show the effectiveness of the proposed policy iteration algorithm, an optimization problem of p53-Mdm2 gene network is investigated.

In spark-ignited (SI) engines, the spark advance (SA) controls the combustion phase that has a significant impact on the efficiency. Online self-optimizing control (SOC) of SA to maximize the indicated fuel conversion efficiency (IFCE) forms a stochastic optimization problem for a static map due to the stochasticity of combustion. Gradient-based optimization algorithms using periodic dithers are effective methods of dealing with such problems. However, decision sequences corrupted by periodic dithers are undesirable in SA online SOC. To choose a proper decision sequence for this problem, a gradient descent-based dither-free SOC scheme iteratively updates the decision based on probabilistic guaranteed gradient learning (PGGL). The PGGL approach uses the statistical distribution of the past samples to approximate the gradient on which the sample size can be adaptively adjusted to achieve the probabilistic target. The proposed scheme not only guarantees the accuracy of gradient learning but also adaptively adjusts the sample size in the learning process, achieving a tradeoff between a rapid response and a stable decision sequence. Moreover, the convergence performance of the obtained decision sequence is analyzed with respect to the probability distribution. Finally, experimental validations performed on an SI engine test bench show that the proposed PGGL-based SOC scheme successfully manages engine operation around the optimal IFCE with a fast response and stable SA behavior, under both steady and mild transient conditions.

In the paper, the speed control problem for spark-ignition (SI) engine with alternator is investigated. The problem is addressed from the engine side and the alternator side, respectively, and two coordinated control schemes are proposed for two different operating modes. From the engine side, the torque produced by the alternator is treated as the external disturbance, and a state feedback controller with the magnetic torque feedforward is designed for regulating the engine speed. And from the alternator side, the engine torque ripple is considered as the external disturbance of the alternator rotational dynamics. A disturbance rejection method is proposed to achieve the$L_{2}$-gain performance for the speed servo error. Finally, to demonstrate the validity of the proposed approaches, some simulation results will be shown.

Recently, a renewal interesting is focused on time-delay systems, particularly nonlinear time-delay systems. Krasovskii functional or Razumikhin function based methods have been investigated widely, since the time-delay system is described by the functional differential equation, and the stability analyzing tools are provided early with the functional or function as a parallel way with the Lyapunov stability theory. In the spirit of Lyapunov function based design method, some interesting results on backstepping design for the time-delay systems have been given, however, this is an issue that easily leads a misunderstanding. This paper would show that the problem to construct a Krasovskii functional or Razumikhin function is not a trivial problem.

The slewing motion of a truss arm driven by a V-gimbaled control-moment-gyro is studied. The V-gimbaled control-moment-gyro consists of a pair of gyros that must precess synchronously. The feedback linearization technique is utilized to partially linearize the nonlinear nominal model, where two specific output functions are chosen to satisfy the system tracking and synchronization requirements. The system tracking dynamics are bounded by properly determining system indices and command signals. For the partially linearized system, the backstepping tuning function design approach is employed to design an adaptive nonlinear controller. The dynamic order of the adaptive controller is reduced to its minimum. The performance of the proposed controller is verified by simulation.