With the vigorous development of the electric vehicle (EV) industry, the demand for charging has surged. However, the relative lag in the construction of charging infrastructure has led to a series of problems for drivers, such as difficulty in finding available charging stations and long waiting times for charging. To address this, this paper proposes an EV charging guidance framework based on an Informer network and Multi-Agent Reinforcement Learning (MARL), aiming at achieving efficient EV charging guidance. Firstly, this paper regards charging stations as independent agents, integrating information from vehicles, charging stations, and traffic, transforming the multi-objective optimization problem of EV charging guidance into a multi-agent reinforcement learning task. Then, an Actor-Critic algorithm combined with the Informer is designed, utilizing the Informer in the Critic network to model the interactions between different charging stations, thereby reducing the complexity of policy learning and enhancing coordination among agents. Subsequently, after calculating the advantage function for the agents, the Actor network is updated to improve learning efficiency. The proposed algorithm was simulated and validated in two different EV charging scenarios. The simulation results show that compared with several state-of-the-art methods, our algorithm achieved the best results in multi-objective optimization, demonstrating its superiority and practicality of our proposed algorithm.

In this paper, the prescribed-time fault-tolerant control for multiple input multiple output (MIMO) linear time-varying (LTV) systems is investigated. Both uncertain lexicographically fixed and non-lexicographically fixed LTV systems with precisely known models have been considered. By leveraging the special structure of controllable canonical form and employing the properties of the parametric Lyapunov equation, a global prescribed-time fault-tolerant controller is designed. By choosing a suitable Lyapunov-like function, it will be demonstrated that the state converges to zero within the designated time. In addition, the designed controller is bounded and maintains a linear, concise and smooth form. Ultimately, the effectiveness of the controller is verified through the simulation of the elliptical orbital rendezvous system.

This paper proposes a three-step interval estimation method in the framework of optimization for discrete-time switched systems. The proposed method is realized based on observers design via reachability analysis and measurement update. In order to obtain interval estimation, a peak-to-peak index is used to improve the robustness caused by disturbances and noise. The calculation of zonotopes and boxes is used for reachability analysis. A new iterative algorithm is proposed to reduce computational complexity. For improving estimation accuracy, a method of 1-norm optimization is proposed, which can reduce the conservatism of computing intersections.

Model predictive control (MPC) has been widely applied in the field of autonomous driving; however, it still suffers from poor control accuracy in extreme conditions. To improve the smoothness and robustness of intelligent vehicle trajectory tracking, this study tries to investigate an improved MPC trajectory tracking control for intelligent vehicle, fuzzy adaptive dynamic model predictive control (FADMPC), with prediction time-domain dynamic optimization and objective function weight adaptive regulation. First, in the improved MPC framework, a vehicle dynamics model with three degrees of freedom is established as the theoretical vehicle model of MPC controller, and the recursive least square method (RLS) with forgetting factor is used to timely estimate tire cornering stiffness considering nonlinear error of the control-oriented vehicle model. Second, the relationship between the prediction time domain of the model and factors such as vehicle speed and road adhesion coefficient is analyzed through simulation. Then, a dynamic time-domain regulation scheme is determined by using the whale optimization algorithm (WOA). Third, a fuzzy controller is used to dynamically adjust the weight coefficient of the lateral displacement tracking error in the objective function. Last, a series of experiments is performed on a hardware-in-the-loop platform to validate the comparison effect of different trajectory tracking controllers on jointed and bisected adhesion condition roads. Results indicate that the proposed FADMPC can effectively improve trajectory tracking accuracy and vehicle stability compared with LQR and even ADMPC controller.

This paper presents a collaborative control strategy for landing the nose of an aircraft on a mobile target platform, addressing the issue of total landing gear malfunction during the landing phase. The strategy guides the aircraft along a desired trajectory while controlling the target platform’s speed to ensure simultaneous arrival at the designated position. The system model comprises the kinematic model of a Boeing 747 and a ground platform. A nonlinear aircraft controller, based on the sliding mode control technique, is designed for autonomous landing maneuvers. System stability is ensured using Lyapunov stability theory. The proposed controller’s effectiveness is demonstrated through numerical simulations and compared with the conventional proportional-integral controller, validating its superior performance.

This paper investigates a load frequency control issue for multi-area interconnected unknown power systems with random communication delays. First, a model-free adaptive control scheme is established by building an equivalent data relationship model between area control error and corresponding control input. Then, a dynamic event-triggered scheme is designed to improve resource utilization and reduce computational burden. Furthermore, random communication delays in the feedback and forward channels are considered. The results show that the proposed method is independent of any model information about the power system, only using the controlled system's control input and output data. Several simulation results validate the effectiveness of the proposed control scheme.

This work investigates three energy-shaping control approaches to address the trajectory-tracking problem for specific classes of underactuated mechanical systems. In particular, the notions of contractive systems and dynamic extensions are utilized to solve the trajectory-tracking problem while addressing implementation issues such as the lack of velocity sensors and the presence of constant disturbances. To this end, a first tracking controller is developed without the need for velocity measurements. A second controller is introduced, solving the trajectory-tracking problem while rejecting matched constant disturbances. Then, a third approach establishes conditions to combine both mentioned controllers. It is shown that the proposed design methods guarantee exponential convergence of closed-loop systems for specific classes of underactuated mechanical systems. The third method is illustrated with an example.

Remaining Useful Life (RUL) has become widely recognized as a key metric for evaluating system reliability within the field of Prognostic and Health Management (PHM). It plays a crucial role in predicting a system’s health status and its expected lifespan. While significant efforts have been directed towards accurately estimating RUL, the process of decision-making regarding health management and potential adjustments to equipment lifespan still poses questions. This study introduces a state-space methodology designed to effectively manage RUL and, thus, the end-of-life of a degrading system, particularly focusing on controlled operation systems. Through an investigation of the relationship between the operational dynamics of the system and its aging process, the study specifically concentrates on regulating the degradation rate experienced during operation. A robust design method of an observer and a controller is presented to effectively govern the degradation trajectory, allowing for the achievement of an acceptable level of deterioration within a desired average lifetime. To demonstrate the performance and practical application of the proposed approach, it is applied to a variable-speed wind turbine with a flexible shaft subjected to torsion effects. The results highlight the benefits of using RUL control as a Health-Aware Control (HAC) strategy, aiming to find an optimal balance between reliability and production objectives. This study makes a valuable contribution to the field of reliability engineering by providing insight into managing the end-of-life of controlled processes suffering from aging. It is showed that the RUL control problem can be conveniently expressed as the problem of controlling a polytopic uncertain system. For this reason, a discrete-time robust LQR controller design is presented for solving the polytopic control design problem.

This paper studies the tracking problem for a class of nonlinearly parameterized multi-agent systems with the powers being unknown time-varying functions. The essence of the multi-agent systems and the serious uncertainties distinguish the question in this paper from the existing works. By combing the algebraic graph theory, adding a power integrator method and adaptive technique, a new distributed adaptive tracking controller is presented with a dynamic estimating the unknown parameters, which guarantees that both the system state and the control input are globally bounded and meanwhile, the tracking error can be adjusted to arbitrarily small. Finally, we offer a simulation to interpret the validity of the control scheme design.

This paper studies the problem of obstacle avoidance and trajectory tracking for a class of uncertain nonlinear systems with unmeasured states and actuator faults. The main difficulty is that actuator faults may cause significant transient tracking errors, which might lead to collisions. To overcome this difficulty, an adaptive observer is developed to estimate system states and compensate for actuator faults. Additionally, the integral-multiplicative Barrier Lyapunov function (BLF) is integrated into the backstepping procedure to overcome the dynamics mismatching problem of the existing SUM-type BLF. The proposed adaptive scheme can avoid collisions in a multi-obstacle environment even if the actuator faults occur, and all the signals are uniformly ultimately bounded. Simulation results demonstrate the effectiveness of this approach.

In this paper, we investigate affine formation control problem for a class of multi-agent systems (MASs) where the formation information is only accessible to a small group of agents. We propose a class of generic formation maneuver (GFM) functions and unified analysis method to study different convergence properties including finite-time, fixed-time and predefined-time strategy. We first propose a consensus-based formation control of leaders and then a stress-matrix-based formation control for followers. Different GFM functions are specified for finite-time, fixed-time and predefined-time formation control of leader and follower groups, respectively. Finally, a number of simulation examples are provided to prove the effectiveness of the proposed algorithm.

This paper mainly solves the problem of synchronous control of the master-slave manipulator in an underwater teleoperation system with hardware performance limitations, model uncertainties, and unknown external disturbances. Firstly, in order to meet the limitations of hardware conditions such as the motor speed in the hardware platform, and a prescribed performance function(PPF) is proposed to constrain the speed error, effectively improving the transient performance of the system. Secondly, a prescribed-performance-based adaptive nonsingular integral sliding mode controller(ANISMC) is proposed. Compared with the existing integral sliding mode controllers, the controller in this paper has a faster convergence speed. Then, the Lyapunov method is used to prove that the transformation error and the sliding surface can converge to zero within a fixed time, solving the problem that the integral sign is difficult to cancel when the integral sliding mode controller is used to prove fixed-time stability with the Lyapunov method, and there is no need to know the upper limit of uncertainties and disturbances in advance. Finally, the simulation comparison experiment shows the superiority of the proposed PPF-based ANISMC

This paper investigates the formation composition and keeping problem for multi-quadrotor systems under time-varying communication delays. First, a distributed model predictive control (MPC) approach is employed to transform the formation composition and keeping of multi-quadrotor systems into an online rolling optimization issue, accompanied by the event-triggered mechanism to reduce solution frequency and communication load. Second, to address the asynchrony and time-varying communication delays introduced by event-triggered distributed MPC, a set of constraints is designed to restrict deviations between the current predictive state and previously broadcasted states. Consequently, based on the previously predicted states of neighbors, the cooperation of the multi-quadrotor systems is achieved under asynchronous communication and time-varying delays. This approach guarantees robust asymptotic stability and satisfactory formation performance for multi-quadrotor systems under various delay scenarios. Finally, the numerical and software-in-the-loop (SIL) simulations validate the effectiveness of the proposed algorithm of multi-quadrotors under communication delays.

Steering a nonlinear system from one state to a desired one is a common task in control. While a nominal trajectory can be obtained rather systematically using a model, for example via numerical optimization, heuristics, or reinforcement learning, the design of a computationally fast and reliable feedback control law that guarantees robust asymptotic stability of the found trajectory can be much more involved. An approach that does not require high online computational power and is well-accepted in the industry is gain-scheduling. The results presented here pertain to the stability guarantees and the region of attraction of gain scheduled control laws, based on subsequent linearizations along the reference trajectory. The approach bounds the uncertainty arising from the linearization process, builds polytopic sets of linear time varying systems covering the nonlinear dynamics along the trajectory, and exploits sufficient conditions for robust stability to attempt the derivation of the desired gain-scheduled controller, via the solution of Linear Matrix Inequalities (LMIs). A result to estimate an ellipsoidal region of attraction is provided too. Moreover, arbitrary scheduling strategies between the control gains are considered in the analysis, and the method can be used also to check/assess the stability properties obtained with an existing gain-scheduled law. The approach is demonstrated experimentally on a small quadcopter as well as in simulation to design a scheduled controller for a chemical reactor model and to validate an existing control law for a gantry crane model.

This paper investigates the prescribed-time output consensus (PTOC) of heterogeneous multi-agent systems (MASs) based on sampled data. Firstly, a novel dynamic compensator is designed for each agent and its state is used to interact with its neighbors. Then, a hybrid sampling strategy (HSS), including a dynamic event-triggered sampling (ETS) and a time-triggered sampling (TTS), is developed to determine when to sample and broadcast the compensator state of agents. The execution of the HSS is divided into two steps, a dynamic ETS is presented before the first prescribed-time (PT) where the dynamic threshold decays to zero as time approaches the first PT. Subsequently, a TTS with a constant sampling period (CSP) is introduced after the first PT. With the proposed HSS, all compensators achieve the state consensus at the first PT while excluding the Zeno behavior. Based on the proposed dynamic compensators and the HSS, a fully distributed controller with high scalability and flexibility is developed. Through the Lyapunov stability theory, it is proved that the heterogeneous MASs with the proposed controller achieve the output consensus at the second PT (after the first PT). Finally, a simulation example on 8-wheeled mobile robots is performed to verify the validity of the theoretical results.

The paper addresses the problem of transforming observable, time-reversible and multi-input single-output discrete-time state equations into the extended observer form which comprises a linear observable component and a nonlinear injection term depending on the inputs, output, and a finite number of their past values. The intrinsic necessary and sufficient conditions for existence of the extended observer form are provided in terms of a certain vector field, defined by the system output and its past values. The algorithm is presented to find a parametrized state transformation that takes the state equations into the considered extended observer form. Two examples, one of them academic, illustrate the theory.

Real-world dynamical control systems are often subject to input saturation due to the physical constraints of actuators. This causes the response performance of the system to be reduced due to the effect of input saturation. To this end, we propose a finite-time performance enhanced bounded control (FTPE-BC) method based on sliding mode control for finite time tracking of linear systems with input saturation. In this method, an approach rate based on inverse tangent function is proposed to make the system converge in finite time. A bounded controller based on sliding mode control is proposed, in which two control parameters are introduced to reduce steady-state error, settling time and overshoot. Meanwhile, the finite-time stability of the system is proved in a domain of attraction. The parameters selection principle of the controller is given to improve the transient and steady-state performance of the closed-loop system. The simulation results verify the superiority of the proposed method in the transient and steady-state performance enhancement.

The paper deals with the security control stabilization problem of uncertain Markov jump power systems with input dead zone under stochastic denial-of-service (DoS) attack. DoS attack is modeled as a discrete-time Markov process. Dual hidden Markov models are respectively used to detect the modes of the original power systems and the one under DoS attack. Based on the detected modes and neural networks (NNs), adaptive NN-based security asynchronous control strategies are proposed, where both state feedback and output feedback are studied simultaneously. With the developed control laws, all trajectories of the closed-loop systems are bounded stable in the stochastic setting. Simulation results demonstrate the correctness and usefulness of the proposed techniques.