The increasing penetration of the renewables and integration of the power electronic devices leads to lower system inertia, which is changeling the system stability of a multiterminal HVdc (MTdc) system. This article presents an improved adaptive predictive control with the multiobjective targets coordinating the key parameters that the dc voltage, ac frequency and power-sharing among the terminals in MTdc system. Specifically, we contribute two main points to the relevant literature, with the purpose of distinguishing our study from existing ones. First, the proposed method is based on minimal information exchange by only considering neighboring terminals. Second, the adaptive control is achieved by setting a weighted fitness function to adaptively tune the weights with the effective integration of the trust-region and particle swarm optimization. A four-terminal HVdc system built within the IEEE 30-bus ac system is used as the study case to validate the robustness and efficiency of the proposed method. In the case study regarding the multiobjective fitness function, the proposed approach benefits in suppressing the voltage deviation, providing frequency support and establishing an automatically updated power equilibrium leveraging by the adaptive parameters.
Accurate estimations of cell state-of-charge (SoC) for multi-cell series-connected battery pack are remaining challenge due to the inconsistency characteristic inhabited in battery pack and the uncertain operating conditions in electric vehicles. This paper tries to add three contributions. (1) A data-driven filtering process is proposed to select one represented cell to typify the voltage behavior of battery pack. (2) An improved battery model considering model and parameter uncertainties is developed. (3) An adaptive SoC estimator has been developed, in which the SoC of each cell in battery pack can be accurately predicted. The SoC of battery pack can be located with the SoC values of each cell. It significantly improves the safety operation of battery. The result indicates that the estimation errors of voltage and SoC for all the LiPB cells are less than 3% even if given big erroneous initial state of estimator.
Path-following control is a critical technology for autonomous vehicles. However, time-varying parameters, parametric uncertainties, external disturbances, and complicated environments significantly challenge autonomous driving. We propose an iterative robust gain-scheduled control (RGSC) with a finite time horizon based on linear matrix inequality (LMI) approach to address this issue. Firstly, a refined polytopic linear parameter varying (LPV) model is designed to consider inevitable time-varying parameters. Then, using a set of inequalities and constraints derived from Lyapunov asymptotic stability and the minimization of the worst-case objective function, a novel iterative RGSC technique is proposed to address the over-conservatism. Further, an expanded 3D phase plane is applied to define envelope surfaces, elucidating the connection of stable vehicle operation boundaries. Lane change maneuver is performed in TruckMaker/ Xpack4-RapidECU joint HIL platform. Compared with the infinite time horizon method, the tracking accuracy of our finite controller is significantly improved by 18.15%, 16.68%,14.32%, and 35.65% in cornering stiffness, mass, road conditions, and measurement noise, respectively. Simulation results reveal that our method maintains enhanced control accuracy, robustness, and less conservatism despite minor stability deterioration. An experimental test is carried out on an autonomous bus. The results indicate that our finite RGSC method demonstrates efficient computational characteristics and impressive tracking performance and holds the potential for seamless integration into autonomous vehicle systems. The suggested technique provides crucial insight into better trade-offs among robustness-oriented, less-conservatism-oriented, and stability-oriented control for practical application
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