Currently, majorities of the robust H _∞ control methods are designed for active suspensions, and seldom take the active control of the in-wheel-motor (IWM) into consideration for IWM driven electric vehicles (EVs). In this study, a robust fault-tolerant H _∞ output feedback control strategy with finite-frequency constraint is proposed to synchronously control the active suspension and dynamic vibration absorber (DVA) for IWM driven EVs. Firstly, a DVA-based electric wheel model is developed, in which the IWM is designed as DVA. Furthermore, the spring-damper parameters of the DVA are matched by using particle swarm optimisation (PSO). Then, the robust fault-tolerant H _∞ output feedback control strategy is developed based on linear matrix inequality, in which the finite-frequency constraint is designed in the resonance frequency range of sprung mass. Finally, simulation results validate that the PSO can effectively optimise the spring-damper parameters of the DVA. The robust fault-tolerant H _∞ output feedback control with finite-frequency constraint can effectively improve the ride comfort and suppress the vertical vibration caused by IWM compared with entire frequency constraint. Meanwhile, the fault-tolerant effectiveness of the proposed method is demonstrated under the actuator faults concerning the actuator force noises and losses.

This article studies the problem of event-triggered distributed adaptive bipartite consensus control for multiple autonomous underwater vehicle (AUV) systems with a fixed topology. Different from the existing literature on multiple AUV systems, the competitive relationship among AUVs is taken into consideration. In this situation, the bipartite consensus control is implemented to complete the controller design. Furthermore, combing the relative threshold strategy and the backstepping method, an adaptive event-triggered control scheme is developed to decrease the updating frequency of control input. Through the Lyapunov analysis, the proposed protocol ensures that the position tracking error of the considered system converges to a small neighbourhood near the origin. Finally, a simulation example is given to show the effectiveness of the control scheme.

In this study, a fault-tolerant position tracking control of a quadrotor unmanned aerial vehicle (UAV) is addressed by proposing a model reference interval type-2 (IT-2) fuzzy-model-based sliding mode tracking control methodology. Considering the underactuated characteristic of the quadrotor UAV, first, the authors separate the overall dynamics of the quadrotor into the attitude, altitude, and position subsystems. Moreover, each of them is represented via IT-2 fuzzy model to deal with uncertainties of its membership functions. After then, a linear reference model supposed to be tracked by each subsystem is designed, on which each tracking error dynamics is derived. Given the tracking error dynamics and additive actuator faults, an IT-2 integral fuzzy sliding surface is proposed to enhance the robust tracking performance of the entire system. As a result, a linear matrix inequality-based sufficient condition is derived to guarantee the asymptotic stabilisation as well as satisfying an tracking performance. Furthermore, a reachability condition of the designed sliding surface is also proposed. Finally, the authors provide design examples to demonstrate the effectiveness of the proposed position tracking control methodology.

This study presents an integrated control scheme of a diesel engine which aims to ensure quick torque response while limiting the average NOx emission below the certain specification. To start with, the sensitivity analysis is performed to recognize the key factors determined to balance emission, torque and fuel consumption. Considering the strong nonlinearity of the diesel engine, a multi-input multi-output (MIMO) linear parameter varying (LPV) model is developed. Unlike the conventional state-depended LPV modelling process, the proposed LPV model is built based on a kernel function with a novel form of state-free predictive equation so that the system outputs are directly computed. The control objective of coordinating torque tracking and emission reduction is then accomplished by designing a model predictive controller that regulates the fuel injection quantity, fuel injection angle, exhaust gas recirculation (EGR) rate and the opening degree of variable geometry turbine (VGT) valve. Finally, the developed LPV model and the MPC controller are testified with a high-fidelity commercial diesel engine model. The results show that the LPV-MPC controller is able to satisfy the NOx emission specification and the engine torque tracks the desired reference fast in transients, while the control variables are suitably actuated within the actuator constraints.

In this study, the non-linear deadbeat direct torque and flux control (N-DB-DTFC) scheme is proposed for switched reluctance motor (SRM) to achieve torque and flux precise control. For torque control part, the Fourier series form of inductance is used to establish the non-linear torque model of SRM. As for the flux control, stator flux-oriented is adopted to achieve the flux control and simplified calculation in N-DB-DTFC. In addition, the synthetic voltage vectors are applied by pulse-width modulation. In order to assess the performance of N-DB-DTFC, the comparison of torque ripple is analysed among the traditional deadbeat controls in the simulation and experiment. Although, for the torque transient response, three deadbeat controls show the strong robustness and adaptability. In the results, the proposed N-DB-DTFC strategy shows better reflection in terms of lower torque ripple. Moreover, the results of simulation and experiment are based on a three-phase 12/8-poles SRM.

In this study, adaptive prescribed finite-time stabilisation of uncertain single-input and single-output non-linear systems is considered in the presence of unknown states, unknown parameters, external load disturbance, and non-symmetric input saturation. A prescribed finite time disturbance observer is designed to approximate the unmeasured external disturbance. Also, a non-singular prescribed finite time terminal sliding mode control is proposed for the closed-loop control of the system with the non-symmetric input saturation. Extended Kalman filter algorithm is employed for the real-time estimations of the states and unknown parameters of the system. Moreover, a particle swarm optimisation algorithm is used to obtain the design parameters of the proposed disturbance observer and controller. To show the performance of designed control scheme, the proposed approach is employed to guarantee prescribed finite time stabilisation of non-linear vibration of a non-local strain gradient nanobeam. The Galerkin projection method is used to reduce the non-dimensional form of the governing non-linear partial differential equation of Euler–Bernoulli nanobeam to the ordinary differential equation. Finally, numerical simulations are performed to illustrate the effectiveness and performance of the developed adaptive control scheme for the vibration control of nanobeam in comparison with the conventional sliding mode control.

This work presents a new algorithm for flocking with a virtual leader by introducing a new sampling scenario, so-called multi-rate sampling. In the multi-rate sampling, the period of receiving data from different sources is different, but the updating time for all individuals is the same. Here, the authors assume that the period of receiving data from neighbour agents is T , and that from the virtual leader is an integer multiple of it, that is, mT . An upper bound for period T is attained from the upper bound of the energy function that guarantees the neighbouring network to be connected and collision to be avoided between agents. Also, an upper bound on m , which ensures the velocity of informed and uninformed agents to tend the virtual leader's velocity, is derived. The convergence analysis demonstrates that whatever the acquiring period of the virtual leader's information mT is further, then the convergence rate of the group's velocity to the virtual leader's velocity will be greater. Finally, to show the validity of the results, they present a simulation.

The fixed-time master–slave trajectory tracking control problem of the human-in-the-loop teleoperation systems with mixed communication delays (including time-varying delays and random delays), parametric uncertainties, and external disturbances is discussed in this study. Based on the non-singular terminal sliding mode control technique, a novel neuro-adaptive fixed-time control algorithm is designed to solve the above-mentioned problem. Moreover, to improve the steady-state performance of the presented control algorithm, the combination of the radial basis function neural networks, parametric adaptive laws and fixed-time control method is newly-designed and used to reduce the effects of parametric uncertainties and external disturbances. By employing the Lyapunov argument, the sufficient conditions on the control parameters for guaranteeing the fixed-time stability of the controlled teleoperation system are derived. Besides, the authors also present the upper bound of the settling time and prove that it is independent of the initial condition. Finally, several simulation examples are performed to verify the effectiveness of the theoretical results.

Classical artificial potential approach of motion planning is extended for emulating human driving behaviour in two dimensions. Different stimulus parameters including type of ego-vehicle, type of obstacles, relative velocity, relative acceleration, and lane offset are used. All the surrounding vehicles are considered to influence drivers' decisions. No emphasis is laid on vehicle control; instead, an ego vehicle is assumed to reach the desired state. The study is on human-like driving behaviour modelling. The developed motion planning algorithm formulates repulsive and attractive potentials in a data-driven way in contrast to the classical arbitrary formulation. Interaction between the stimulus parameters is explicitly considered by using multivariate cumulative distribution functions. Comparison of two-dimensional (lateral and longitudinal) performance indicators with a baseline model and generative adversarial networks indicate the effectiveness and suitability of the developed motion planning algorithm in the mixed traffic environment.

The development of connected and automated vehicle technologies allows for cooperative control of vehicles and traffic signals at intersections. This study aims at exploring the cooperation between traffic signal control and eco-driving control for a connected hybrid electric vehicle (HEV) system. A two-level cooperative control method for integrating traffic signal control, vehicle speed control, and energy management is proposed with the objective of improving both traffic and fuel efficiency for HEVs at isolated intersections. In view of the energy management and recuperation system of HEV, the vehicle energy consumption characteristic is considered in the proposed method. More specifically, at the traffic level, a traffic signal control strategy is designed to minimise the total travel time and fuel consumption of all HEVs using dynamic programming, which explicitly considers the arrival time and recuperation information of vehicles. At the vehicle level, a hierarchical control architecture is applied to optimise the speed trajectories and powertrain of each HEV using model predictive control and adaptive equivalent consumption minimisation strategy. Simulation results show that compared with the fixed-time and cycle-based signal control strategies with eco-driving, the proposed method can significantly reduce the travel time and fuel consumption by up to 27 and 24%, respectively.