In the medium voltage direct current (MVDC) shipboard grid, the inherent inertial support from the DC capacitors is too small to resist step changes or fluctuations from the high power pulse load and propulsion load, which results in lower DC voltage quality. This study proposes a decentralised control algorithm for the MVDC shipboard hybrid energy storage system (HESS) to enhance the onboard survivability. This algorithm adjusts the droop control coefficient based on the bus voltage change rate adaptively, meanwhile the voltage differential signal processing and filtering are implemented by the trace differentiator. In addition, the proposed algorithm also has state-of-charge (SOC) balancing and SOC recovery abilities between multiple groups of energy storage devices. The parameter selection principle are analysed, and a variety of working conditions are simulated and verified in PSCAD. Theoretical analysis and simulation show that, HESS can achieve good power distribution and SOC management performance without communication compared to fixed droop coefficient control strategies, and the dynamic characteristics of bus voltage have been significantly improved.

This study presents a method for small-signal analysis of an advanced, multi-variable control system for variable speed hydropower (VSHP) plants. A model predictive controller (MPC) optimises the power plant performance. In parallel, a virtual synchronous generator-type (VSG) converter control ensures that the VSHP contributes to virtual inertia and frequency control of the power system. The aim of the small-signal analysis is to parametrise the cost function of the MPC to minimise oscillatory modes between the VSHP hydraulic system and the power system. A state-space representation of the MPC is developed by assuming a stable steady-state operating point equal to the reference values of the MPC cost function, and that no constraints are active. This state-space representation allows for small-signal analysis of the power system, including the MPC. The results show that the modes between the hydraulic system and the power system are well-damped and negligible when the costs of deviations in the hydraulic system are low compared to the cost of deviations in the VSG power reference. Thus, these modes do not constrain the tuning of the VSG. The VSHP power output can, therefore, be optimised independently through the VSG controller to damp power oscillations and reduce frequency deviations.

This paper presents a hierarchical coordination method to improve operation conditions between the medium-voltage distribution network (MVDN) and low-voltage distribution networks (LVDN) considering high-level residential PV units integrating LV distribution networks. It addresses the following issues: (i) rapid power fluctuations and unbalance caused by high-level residential PV units passes to the MV distribution network via the point of common coupling (PCC) and thus increase operation challenges; (ii) power fluctuations, voltage violations and unbalance simultaneously happens in the LVDNs. A hierarchical coordination structure the MVDN and LVDN is proposed. In the MV level, a centralized dispatch method based on three-phase optimal power flow is developed in the timescale of minutes to minimize the power losses, unbalance and PCC adjustments, which are achieved by regulating energy storage systems (ESSs) and PV inverters in the LV level. In the LV level, a distributed control model based on a consensus algorithm is proposed in the timescale of seconds to track the reference active (or reactive) power at the PCC given by the MV level and mitigate its fluctuations. Simulations studies are performed to verify the proposed method.

The uncertainties in tire cornering stiffness can degrade the path following the performance of autonomous vehicles, especially in low adhesive conditions, to deal with this problem, a novel multi-model adaptive predictive control is proposed in this study. Firstly, a model predictive path following controller is designed based on a combined model of vehicle dynamics and road-related kinematics relationship. Then, to deal with the model uncertainties, the multiple model adaptive theory is introduced, and the recursive least adaptive law is proposed with its convergence proved by Lyapunov theory. Finally, the multiple-model adaptive law is combined with the proposed model predictive control by a convex polytope of tire cornering stiffness. In this way, the proposed algorithm can be adaptive to the uncertainties of tire cornering stiffness. Simulation results show the effectiveness and robustness of the proposed method to the uncertainties of the tire cornering stiffness resulting in an excellent performance in any road condition without introducing conservativeness.

Autonomous emergency braking (AEB) is an active safety technology which aims to prevent collisions by operating harsh braking. When AEB is activated on split-μ roads, the yaw moment generated by the asymmetric braking forces will lead to losing control of the vehicle. In this study, a coordinated control scheme of steer-by-wire (SBW) and brake-by-wire (BBW) is developed to solve this problem. An anti-lock braking system is achieved by a sliding mode controller. Besides, two model predictive controllers based on a 7-degree-of-freedom vehicle dynamics model are proposed for different road adhesion conditions. According to the simulation results, the proposed scheme can maintain the stability of the vehicle and achieve a satisfying braking efficiency under various friction differences between the two-side wheels. At last, the experiments are also carried out to verify the effectiveness and the real-time performance of the proposed scheme on a hardware-in-loop bench of BBW and SBW.

This study investigates the neural-network (NN)-based adaptive decentralised controller design issue for a class of large-scale non-linear non-strict-feedback interconnected systems with time-varying asymmetric output constraints and dead-zone inputs. The existences of the non-strict-feedback structure, time-varying asymmetric output constraints, and dead-zone inputs lead to the construction of the controller of each subsystem is very difficult. By applying the inherent property of Gaussian functions employed in radical basis function NNs, the non-strict-feedback structure is skillfully handled, and the adaptive backstepping method is used to construct the desired controllers of all subsystems. Furthermore, Lyapunov stability analysis shows that all the signals of the closed-loop system are ultimately bounded, and each subsystem output can converge to an arbitrarily small and predefined time-varying range with the corresponding constraint is always satisfied by employing a barrier Lyapunov function. Finally, simulation results based on a practical example prove the effectiveness of the proposed design strategy.

This study is dedicated to develop an adaptive output-feedback (OPFB) tracking control scheme for continuous-time linear systems to achieve the infinite-horizon linear quadratic tracking (LQT) solution. The existence conditions of the OPFB control for LQT solution are proposed and an upper bound is found for the discount factor to assure the stability of the OPFB solution. To develop an online learning solution without knowing the system drift dynamics, a novel value iteration (VI) algorithm is presented based on the integral reinforcement learning technique which requires measured augmented system states. Moreover, a convergence analysis is proposed for the VI algorithm. Compared to the policy iteration method, the VI algorithm relaxes the initial stabilising control policy requirement. Specifically, in order to further obviate the knowledge of system states, a neural network-based adaptive observer is used during learning and it is no longer needed after the online learning algorithm converges. Effectiveness of the proposed learning scheme is illustrated through an interesting application into a single-phase grid-connected PV power inverter.

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.

This study addresses an observer-based model predictive control (MPC) algorithm for a networked control system (NCS) under denial of service (DoS) attacks and actuator saturation via an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model. With few studies undertaking the cyber security problems in the research of MPC algorithm, this study considers the data transmission problem when DoS attacks occur. Under DoS attacks, signals in the communication networks will be interfered. The probability of error-free packet reception depends on signal-to-interference-plus-noise ratio of the wireless transmission networks. In order to reduce the on-line computation, an off-line fuzzy observer is devised to estimate the system states. Meanwhile, an on-line MPC algorithm is proposed to minimise the performance objective function and obtain the secure model predictive controller gain. Besides, the recursive feasibility is ensured by refreshing the bound of estimation error. Finally, illustrative examples certificate the effectiveness of the presented method.

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.