A cooperative output-feedback secure control scheme based on observers is proposed for connected and autonomous vehicle systems with intermittent denial-of-service (DoS) attacks on communication. Firstly, the dynamic model of the longitudinal connected and autonomous vehicle system is analyzed, and the feedback is linearized to obtain the linear dynamic equations. Secondly, by using the common Lyapunov function, a secure control scheme is designed to make the cooperative tracking error asymptotically stable. Finally, for maximizing the duration of DoS attacks, appropriate parameters are selected to design an optimization algorithm, in order to ensure the safe operation of the connected and autonomous vehicle system. The experiment simulated a networked vehicle system consisting of 4 followers and 1 leader, and the simulation results verified the effectiveness of the proposed method. The experiment is conducted by simulating a connected and autonomous vehicle system consisting of 4 followers and 1 leader. The simulation results verify the effectiveness of the proposed method.

Accurate trajectory tracking is the key for the intelligent vehicle to achieve autonomous motion control. A novel robust adaptive sliding mode control strategy is proposed to dealing with the issue of system uncertainty affecting the accuracy of trajectory tracking control. Firstly, a two-degree-of-freedom vehicle dynamics model is established based on the principles of vehicle kinematics. Secondly, a sliding surface of proportional integral derivative (PID) type with adaptive properties is designed based on trajectory tracking errors, and the accuracy and robustness of trajectory tracking control are improved by designing adaptive update laws to estimate the sliding mode control gains and the upper bound of system uncertainty in real-time. Thirdly, the control parameters of the controller are optimized by the particle swarm optimization algorithm, which further improves the trajectory tracking control performance. Finally, the proposed control strategy is verified by the simulation under different road conditions and vehicle speeds. The simulation results show that the proposed control strategy can ensure that the intelligent vehicle tracks the target trajectory under the influence of system uncertainty, and its control performance is superior to the fractional order PID control.

To solve the problem that the existing traffic accident risk prediction models lack the extraction of regional spatial correlation and dynamic spatiotemporal features, a traffic accident risk prediction model is constructed based on the spatiotemporal convolutional network with regional similarity. Firstly, a spatial-channel attention multi-graph convolutional network is constructed based on the graph convolutional network, in order to comprehensively capture local geospatial similarity and global semantic attributes. Secondly, spatiotemporal attention is introduced to learn the dynamic representation of the accident features adaptively. Finally, spatial dependencies are captured through multi-head graph attention networks, and temporal correlation of long sequence is modeled by using gated units with bidirectional temporal convolution. The proposed model is tested on two real traffic accident datasets. The experimental results show that the prediction performance of the proposed model for traffic accident risk is superior to that of benchmark models such as long short-term memory neural network.

To address the challenges of excessive computational resource consumption caused by trust evaluation and updating in heterogeneous vehicle platoons under a zero-trust framework, as well as the consistent control of vehicle platoons, an innovative zero-trust hybrid event-triggering control strategy is proposed. Firstly, the update frequency of trust between vehicles is improved, different trust update frequencies are set according to the importance of vehicles in the platoon, and the trust of vehicles is introduced into the spacing control strategy of the vehicle platoon. Secondly, a dynamic hybrid event-triggering condition is designed in combination with the update frequency of trust, and the controller is designed considering the communication delay. Finally, the proposed control strategy is verified by simulation. The simulation results show that the proposed control strategy realizes the consistent control of the vehicle platoon under the zero-trust framework.

The wayside energy storage device (WESD) has the advantages of large capacity, high power and high conversion efficiency, which provides a new solution for the future energy-saving operation of rail transit. To solve the problem of multi-train scheduling and energy-saving operation optimization after delays, a scheduling optimization method that comprehensively considers energy-saving operation and passengers’ demand is proposed, combining WESD with train operating state switching control and timetable adjustment. In order to improve the direct utilization rate of regenerative braking energy and effectively reduce the energy storage capacity requirements for WESD, coordinated regulation and optimization of the arrival/departure time, operating speed and working conditions of multiple trains in the same power supply section, such as motoring, coasting and regenerative braking, are carried out considering the slope. The simulation results show that the proposed method can reduce the cumulative energy storage capacity of WESD and achieve energy-saving operation of the train.

To solve the limited driving range and long recharging time during the distribution cost process of electric vehicles, the electric vehicle routing problem considering recharging mode decision is proposed, a mixed integer programming model is constructed with the goal of minimizing the total distribution cost. In response to the characteristics of this problem, an improved adaptive large neighborhood search algorithm driven by recharging and swapping features is designed. Based on the flexibility of charging time and the close correlation between charging stations and customers, neighborhood operators such as affiliation destroy and comparison repair for recharging stations are introduced. The experimental results show that compared with the large neighborhood search algorithm, the proposed algorithm can obtain better solutions when solving large-scale examples. Reasonable selection of the recharging mode decision can effectively shorten the recharging time of electric vehicles and reduce the total distribution cost.

In the intelligent manufacturing process, there is a need for operators to perform pulling movements on robots, which are often random and non-linear. To make the robot accurately recognise the operator’s intention and achieve smooth tracking for nonlinear targets, a tracking controller is proposed. Firstly, the flexible interaction process between the operator and the six-degree-of-freedom robot is mathematically modelled, and the operator’s command intention is analysed based. Seondly, the Pade approximation method is used to establish the relationship between the system state derivatives and the robot motion delay. Thirdly, a state feedback controller for nonlinear target tracking motion control system is designed, and the stability conditions of the system are analysed. Finally, the relationship among the nonlinear tracking target of the system, the system state and the system outputis analysed for boundedness. The simulation results show that the proposed controller can achieve bounded tracking for the bounded nonlinear target.

For the multi-quadrotor unmanned aerial vehicle (QUAV) system with dynamic uncertainty and external disturbances, a predefined-time formation control algorithm based on the leader-follower method is proposed. Firstly, a predefined-time command filter is introduced to solve the “complexity explosion” problem caused by repeated derivation of virtual signals, and a non-smooth error compensation mechanism is constructed to eliminate the effect of filtering error on the system. Secondly, Lyapunov stability theory is used to prove that the predefined-time formation controller can make the closed-loop system reach a stable state within the predefined time, all the signals in the closed-loop system are bounded within the predefined time, and the formation tracking errors of the multi-QUAV system converge to a neighborhood near the origin within the predefined time. Finally, the proposed algorithm is tested through simulation using a multi-QUAV system consisting of 1 leader and 4 followers. The simulation results demonstrate the effectiveness of the proposed algorithm.

To overcome the shortcomings of traditional direct-current (DC) motor speed control methods in terms of reliability, intelligence and debugging efficiency, a DC motor speed control optimization method based on an improved sparrow search algorithm is proposed, and the optimization results are applied to virtual debugging based on digital twin technology. Firstly, the golden sine and Cauchy mutation strategies are adopted to improve the position update of the discoverer and the joiner in the sparrow search algorithm respectively, so as to increase the diversity of the search and the global search ability. Secondly, the improved sparrow search algorithm is applied to the DC motor speed control system to optimize the parameters of the proportional integral (PI) controller in the speed loop. Finally, a digital twin model of the DC motor speed control system is constructed, and the interaction between the optimization algorithm and the digital twin model is realized based on the OPC server. The experimental results show that the improved sparrow search algorithm enhances the control accuracy and robustness of the DC motor speed control system, and the optimization results of control parameters obtained from simulation can be applied to the digital twin model to improve the debugging efficiency of DC motors.

An intelligent lifting planning framework is established, in order to enhance the obstacle avoidance capability and dynamic stability of tower crane systems within complex constrained spaces through a dual-chain innovation encompassing path search and trajectory optimization. Firstly, a nonlinear model of the system is derived based on dynamic analysis, and differential flatness analysis is performed to provide a straightforward representation for motion planning. Then, to address the path planning challenge for tower cranes in complex environments, a directional-biased bidirectional rapidly-exploring random tree* (DB-BiRRT*) algorithm is proposed. The algorithm optimizes the node expansion process by introducing a target biasing mechanism incorporating regional probability sampling and a direction guidance mechanism based on an enhanced potential field function, thereby improving the efficiency and quality of path planning. Finally, comprehensively considering full-state system constraints such as obstacle avoidance and swing suppression, a multi-objective optimization approach utilizing non-uniform rational B-spline curves is employed for trajectory planning to obtain the optimal trajectory that minimizes the lifting time, energy consumption, and load swing angle. The simulation results verify the effectiveness and superiority of the proposed method.

Conventional static job-shop scheduling methods often suffer from dynamic changes in actual manufacturing environments, which leads to poor adaptability. Most existing dynamic job-shop scheduling methods are only applicable in an environment with a single uncertain factor, but often fail to achieve the expected results in a complex environment with multiple uncertain factors. To further explore the robustness of scheduling methods in uncertain scenarios, processing time fluctuation, new job insertions and job priority adjustment are introduced, and scheduling scenarios with two uncertain factors are designed. Then, in uncertain scenarios, the genetic algorithm, simulated annealing algorithm, first-in-first-out rule, shortest processing time rule and longest processing time rule are applied to multiple instances of scheduling problems. The experimental results show that the simulated annealing algorithm exhibits remarkable robustness in dealing with uncertain scenarios, while the three basic scheduling rules show relatively low scheduling efficiency and robustness.

In order to better solve the flexible job-shop scheduling problem, a mathematical model is established with the goal of minimizing the maximum completion time, and the grey wolf optimization algorithm is improved. Firstly, a new formula for individual position update is proposed, which reduces the guiding role of the alpha wolf on the grey wolf population. Secondly, since the linear convergence factor cannot fully exploit the performance of the grey wolf optimization algorithm, a nonlinear convergence factor is introduced to enhance its global exploration ability in the early stage and local exploitation ability in the later stage. Thirdly, in order to address the excessive guidance of optimal individuals in the grey wolf optimization algorithm, two neighborhood exploration strategies are proposed, allowing some individuals to conduct self-exploration and key individuals to undergo machine-based mutation, thereby reducing the completion time. Finally, an elite solution update mechanism combined with the Hamming distance is proposed. The experimental results show that the proposed improvement strategies are all effective, the improved grey wolf optimization algorithm can better solve the flexible job-shop scheduling problem and enhance the production efficiency of the job-shop.

Process industries, as the main sources of energy consumption and carbon emissions, have particularly significant issues related to energy efficiency optimization. To tackle the uncertainties of the time-of-use electricity price, a real-time demand response scheduling methodology for energy systems in the cement production industry is proposed, which combines the long short-term memory (LSTM) neural network with reinforcement learning. Firstly, mathematical models are established for the main energy-consuming equipment and storage bin in each cement production process, and a constrained Markov decision process is constructed to reflect the energy consumption characteristics and production demands. Then, the sequence processing capability of the LSTM neural network is utilized to handle the uncertainties of the time-of-use electricity price, thereby providing robust data support for the development of the scheduling strategy. Finally, a reinforcement learning agent is employed to sense the environment and optimize the scheduling strategy to achieve the goal of energy efficiency optimization. The simulation results verify the feasibility and reliability of the proposed method in the demand response scheduling of the energy system in the cement production industry. A new idea for achieving energy efficiency improvement and sustainable development is provided in the industries.

The barrel temperature is one of the key parameters affecting the quality of injection molded products. The proportional integral differential (PID) controller is widely used in barrel temperature control systems. However, the parameter optimization of the PID controller heavily relies on the operator’s experience, which has the problems of high cost, low efficiency and poor accuracy. To address these issues, parameter optimization of the PID controllers for the barrel temperature is studied in this paper. Firstly, for the performance optimization of the barrel temperature control system, performance evaluation metrics are proposed, and a parameter optimization framework of the PID controller is designed. Then, based on a knowledge-informed data-driven optimization strategy, the simplex search algorithm is improved, and a knowledge-informed simplex search algorithm based on historical quasi-gradient estimation is proposed to enhance the efficiency of parameter optimization for the PID controller. The experimental results show that, compared with the simplex search algorithm, the improved algorithm sacrifices a small amount of optimization accuracy but significantly enhances the optimization efficiency, and reduces the cost of parameter optimization of the PID controller.

In order to explore the impact of flexible loads on the economic operation of microgrids and users’ satisfaction with electricity usage, an incentive-based demand response microgrid model is proposed. Considering that the original salp swarm algorithm has a small number of parameters, but it is very sensitive to the selection of certain parameters, then, a hybrid salp swarm algorithm is proposed. Decay factors and adaptive distribution weights are introduced in the leader position update of the original algorithm, and inertia weight strategies and position offset coefficients are introduced in the follower position update. The simulation results of the optimization of benchmark functions show that the solution accuracy and speed of the hybrid salp algorithm are superior to those of the classical algorithms. The simulation results of flexible loads participating in the operation of the microgrid verify the effectiveness and superiority of the hybrid salp algorithm in solving the economic operation problem of the microgrid.

The current elevator group control scheduling system has insufficient debugging safety, high difficulty in design evaluation, and high debugging costs. Therefore, a simulation model for elevator group control is developed. Based on this model, the elevator group control scheduling scheme is dynamically evaluated according to indicators such as the average waiting time of passengers, long-time waiting rates, and the energy consumption of the elevators. To address the issues of low elevator operation efficiency and poor passenger comfort during elevator rides, a deep Q network (DQN) is developed to realize optimized scheduling for elevator group control. The corresponding state space, reward signals, and agent structure are designed by the primary factors affecting elevator transportation efficiency. Considering the lengthy duration of online training for reinforcement learning agents and the challenge of providing real-time decision support, the operational data from the elevator group control simulation model is utilized to train a feedforward neural network, and an elevator group control environment prediction model is developed to serve as the agent training environment. Simulation experiments are conducted by using the elevator group control simulation model. The results showed that, compared with the prevalent minimum response time strategy, the proposed strategy reduces the average waiting time of passengers, average time that passengers spend in the elevator, long-time waiting rate, the number of elevator starts and stops, and increases the average number of arrivals within 5 minutes.

A bi-level scheduling model is proposed to optimize resource scheduling while ensuring distribution network security. The upper-level model focuses on coordinated optimization of distributed resources, in order to minimize operational costs with decision variables including photovoltaic output, charging/discharging power of energy storage devices, charging/discharging power of electric vehicles, and load of the air conditioning cluster. The lower-level model employs an optimal power flow method to minimize voltage deviation of the distribution network, allocates the charging/discharging loads of the electric vehicle cluster determined by the upper-level model to each node according to their spatial distribution in the distribution network. The simulation results demonstrate that the proposed model can effectively reduce operational costs of distributed resources and significantly enhance the voltage stability of the distribution network, providing robust technical support for large-scale integration of distributed resources.

To solve the operation scheduling problem of the unmanned electric loader cluster, an ant colony algorithm based on snake optimization is proposed. This approach leverages the snake optimization algorithm to refine the core parameters of the traditional ant colony algorithm, and adopts a combined global asynchronous and elite strategy for pheromone concentration update to ensure the operational efficiency of the algorithm. In the experiment, examples of different customer number from the Solomon test set are used to conduct a comparative analysis of the ant colony algorithm, the elite ant colony algorithm, the ant colony algorithm based on particle swarm optimization, and the ant colony algorithm based on snake optimization. Besides, the ant colony algorithm based on snake optimization is applied to a real case of operation scheduling of the unmanned electric loader cluster. The experimental results of the Solomon test set show that for the C1 type, R1 type and RC1 type cases, the optimization ability of the ant colony algorithm based on snake optimization is higher than that of other algorithms, and the obtained scheduling schemes achieve lower operational costs. The experimental results of the real case verify the effectiveness of the ant colony algorithm based on snake optimization.