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A correction estimation method considering sensor measurement bias was proposed to accurately estimate the lateral velocity of the tractor in autonomous semi-trailer vehicles. Based on the three-degree-of-freedom vehicle dynamics model, the measurement bias estimation model was established by expanding the front wheel angle, yaw rate bias, and lateral acceleration bias into the state variables. Taking into account the impact of dynamic model mismatch, a feedback correction mechanism based on the kinematic model was designed to adjust the estimation results of lateral velocity and yaw rate through feedback. The proposed method was implemented using the square-root cubature Kalman filter algorithm, and the observability of the estimator was analyzed. This method was verified on the joint simulation platform of TruckSim and MATLAB/Simulink. The results show that the proposed method not only eliminates the influence of measurement bias but also exhibits good convergence speed. Its performance is significantly better than estimation methods that do not consider measurement bias or only consider measurement bias. [ABSTRACT FROM AUTHOR]

Wind power exhibits low controllability and is situated in dispersed geographical locations, presenting complex coupling and aggregation characteristics in both temporal and spatial dimensions. When large-scale wind power is integrated into the power grid, it will bring a significant technical challenge: the highly variable nature of wind power poses a threat to the safe and stable control of the power, frequency, and voltage in the power system. Simultaneously, the model predictive control (MPC) technology provides more opportunities for investigating control issues related to large-scale wind power integration in power systems. This paper provides a timely and systematic overview of the applications of MPC in the field of wind power for the first time, innovatively embedding MPC technology into multi-level (e.g., wind turbines, wind farms, wind power cluster, and power grids) and multi-objective (e.g., power, frequency, and voltage) control. Firstly, the basic concept and classification criteria of MPC are developed, and the available modeling methods in wind power are carefully compared. Secondly, the application scenarios of MPC in multi-level and multi-objective wind power control are summarized. Finally, how to use a variety of optimization algorithms to solve these models is discussed. Based on the broad review above, we summarize several key scientific issues related to predictive control and discuss the challenges and future development directions in detail. This paper details the role of MPC technology in multi-level and multi-objective control within the wind power sector, aiming to help engineers and scientists understand its substantial potential in wind power integration in power systems.

In order to mitigate the excessive amplitudes of pitch and heave motions, a real‐time predictive control method is proposed for the high‐speed multihull with strict input constraints of anti‐pitching appendages. The vertical control model of the high‐speed multihull has been established with T‐foil and flap served as anti‐pitching appendages, and the motion couplings between heave and pitch have been analyzed. The wave disturbance considered as a kind of colored noise can be whitened so as to obtain the control signal, and then adaptive extended Kalman filtering method can be used to get the online estimates of the heave velocity and pitch angular velocity. The proposed predictive control‐based anti‐pitching method mainly contains two reformed aspects of the predictive control. One is to take into account the error between the actual and predicted system values, such that the prediction model with linear error correction can be obtained resulting in the enhanced robustness. The other is to employ the Laguerre function to convert the control sequence of moving horizon optimization into the fewer‐dimensional Laguerre parameters, and then the computational burden of online optimization can be significantly reduced indicating the improved real‐time performance. The closed‐loop system stability has also been theoretically analyzed. The simulation and experiment have been carried out to verify the effectiveness of the proposed algorithm in this paper. [ABSTRACT FROM AUTHOR]

In order to reduce the impact of load power fluctuations on the power system and ensure the economic benefits of user-side energy storage operation, an optimization strategy of configuration and scheduling based on model predictive control for user-side energy storage is proposed in this study. Firstly, considering the cost and benefits of energy storage comprehensively, an energy storage configuration optimization model with the highest annualized net income as the goal is built to determine the parameters for configuring energy storage. Then, with the goal of maximizing the profit during the scheduling period, pre-month scheduling optimization model, day-ahead scheduling optimization model and intra-day scheduling optimization model are established. The goal of the pre-month scheduling optimization model is to determine the maximum monthly demand; part of the scheduling results in the day-ahead scheduling optimization model directly participate in the intra-day scheduling; the intra-day rolling optimization relies on the advantages of real-time feedback and closed-loop scheduling to smooth out power fluctuations caused by load forecast errors. Finally, the configuration and economic benefit of lithium iron phosphate batteries, lead-carbon batteries and sodium-sulfur batteries are analyzed and compared, and scheduling analysis is performed. The simulation results show that the proposed optimization method can cut peaks and fill valleys, ensure the economic benefits of users, and provide guidance for users to invest in energy storage. [ABSTRACT FROM AUTHOR]