High-precision wind power prediction is important for the planning, economics, and security maintenance of a power grid. Meteorological features and seasonal information are strongly related to wind power prediction. This paper proposes a hybrid method for ultrashort-term wind power prediction considering meteorological features (wind direction, wind speed, temperature, atmospheric pressure, and humidity) and seasonal information. The wind power data are decomposed into stationary subsequences using the ensemble empirical mode decomposition (EEMD). The principal component analysis (PCA) is used to reduce the redundant meteorological features and the algorithm complexity. With the stationary subsequences and extracted meteorological features data as inputs, the long short-term memory (LSTM) network is used to complete the wind power prediction. Finally, the seasonal autoregressive integrated moving average (SARIMA) is innovatively used to fit seasonal features (quarterly and monthly) of wind power and reconstruct the prediction results of LSTM. The proposed method is used to predict 15-minute wind power. In this study, three datasets were collected from a windfarm in Laizhou to validate the prediction performance of the proposed method. The experimental results showed that the prediction accuracy was significantly improved when meteorological features were considered and further improved with seasonal correction.

In order to estimate different road adhesion coefficients, a new method based on the second‐order linear extended state observer is proposed to improve the active safety and driving stability of vehicles. The vehicle single‐wheel driving dynamics model and single‐wheel braking dynamics model are built with analyzing the driving and braking processes of the vehicle. The controllers of ASR and ABS are designed by using sliding mode structure control, and the chattering phenomenon of sliding mode control is eliminated by the saturation function. The road adhesion coefficient can be estimated by the second‐order linear extended state observer in real time with the wheel speeds, and driving torque and braking torque are used as input variables. The simulation results show that the road adhesion coefficient estimation method based on the second‐order linear extended state observer can accurately identify the adhesion coefficient of different roads in the presence of external interference and has strong robustness. At the same time, it also provides a reference for the design of the stability control system of unmanned vehicles.

In this paper, the problem of adaptive control with disturbance attenuation is investigated for stochastic discrete-time nonlinear systems with Markovian jumping parameters and unknown system parameters. Using the proposed control law and the sufficient conditions, the closed-loop system achieved H∞ performance. As a practical example of the considered problem, an air-fuel ratio controller for a gas engine intended as an electric vehicle extender is designed based on the proposed adaptive disturbance attenuation control algorithm. The air-fuel ratio controller is validated via a numerical simulation under three working conditions. The results of the numerical simulation show that the air-fuel ratio can be regulated into a range around its desired value by the proposed adaptive disturbance attenuation controller and that the adaptive law can be tuned to a steady value. The control performance indices of the proposed adaptive disturbance attenuation controller are smaller than those of the open-loop controller, which means that the proposed adaptive disturbance attenuation controller achieves a greater control effect.

The problem of stochastic adaptive air-fuel ratio control by the dynamic model of biogas-fuelled engines is investigated in this paper. An adaptive law is employed to estimate the theoretical air-fuel ratio, which is undetermined due to the uncertainty of the methane concentration in the biogas. A stochastic adaptive air-fuel ratio controller in consideration of the stochasticity of the residual gas is designed based on the adaptive law, and the closed-loop system is proven to be mean-square stable. The proposed stochastic adaptive air-fuel ratio controller is validated through a numerical simulation when the theoretical air-fuel ratio is unknown constants and jump signals.

In this paper, the air-fuel ratio regulation problem of compressed natural gas (CNG) engines is considered by employing stochastic model predictive control (MPC) technology. A stochastic model predictive regulator based on a discrete-time dynamic model of CNG engines is proposed, taking into account the residual gas, and the closed-loop system is deduced to be stochastically stable. A numerical simulation is performed to demonstrate the effectiveness of the proposed control scheme under two working conditions. The simulation results show that the performance of the proposed stochastic model predictive regulator is better than that of the open-loop controller.

In this paper, the air-fuel ratio regulation problem of compressed natural gas (CNG) engines considering stochastic L₂ disturbance attenuation is researched. A state observer is designed to overcome the unmeasurability of the total air mass and total fuel mass in the cylinder, since the residual air and residual fuel that are included in the residual gas are unmeasured and the residual gas reflects stochasticity. With the proposed state observer, a stochastic robust air-fuel ratio regulator is proposed by using a CNG engine dynamic model to attenuate the uncertain cyclic fluctuation of the fresh air, and the augmented closed-loop system is mean-square stable. A validation of the proposed stochastic robust air-fuel ratio regulator is carried out by the numerical simulation of two working conditions. The accuracy control of the air-fuel ratio is realized by the proposed stochastic robust air-fuel ratio regulator, which in turn leads to an improvement in fuel economy and emission performance of the CNG engines.