The control of a permanent magnet synchronous motor (PMSM) without a position sensor based on a sliding-mode observer (SMO) algorithm has a serious jitter problem in the process of motor phase tracking. A second-order adaptive sliding-mode observer algorithm was proposed, and the ideas and principles of the second-order sliding-mode observer algorithm based on the super-twisting algorithm were elaborated. In particular, adaptive estimation with the introduction of back-electromotive force (EMF) was investigated, and the Lyapunov stability criterion was used to determine the convergence properties of the algorithm. The results showed that the second-order adaptive sliding-mode observer algorithm had better jitter suppression and a better phase tracking performance than the traditional sliding-mode observer algorithm. The experimental results showed that when the motor velocity was 800 r/min, the velocity error of the second-order adaptive sliding-mode observer algorithm was 0.57 r/min and the position error was 0.018 rad, with accuracy improvements of 93.63% and 58.34%, respectively. When the motor velocity was 1000 r/min, the velocity error of the second-order adaptive sliding-mode observer algorithm was 0.94 r/min and the position error was 0.022 rad, with accuracy improvements of 90.55% and 55.10%, respectively. The jitter of the system was suppressed well, the curve of back-EMF was smoother, and the robustness of the system was high. Therefore, the second-order adaptive sliding-mode observer algorithm is more suitable for the position-sensorless control of a PMSM.
The purpose of this study is improve calibration efficiency and obtain accurate diesel engine operating parameters, achieving improved diesel engine emissions and fuel efficiency. A PSO‐RBF (particle swarm optimization‐radial basis function) diesel engine performance prediction model combining an improved PSO (particle swarm optimization algorithm and an RBF neural network is proposed. A space‐filling experimental design method for diesel engine performance prediction is proposed based on the actual operating conditions of diesel engines. Training data are collected at the bench to build the RBF prediction model. An optimization PSO search method is proposed to improve the PSO optimization capability. An improved PSO algorithm is used to optimize the model and improve prediction accuracy. Then the BSFC (diesel brake‐specific fuel consumption), NOx ((Nitrogen Oxid), CO (Carbon Monoxide), and HC (Hydrocarbon) prediction models are constructed. Results show that the PSO‐RBF can find the global solution with good prediction accuracy and generalization ability during small amounts of data. The PSO‐RBF model fitting degrees of BSFC, NOx, CO, and HC are 0.9952, 0.9910, 0.9820, and 0.9870 respectively. Mean relative errors are 3.02%, 2.78%, 1.39%, and 2.01% respectively. Mean absolute percentage errors are 1.58%, 3.26%, 3.69%, and 2.96% respectively. The optimized model R2 (Model determination coefficient) is improved by 0.065, 0.102, 0.10, and 0.085, respectively.
The traditional single current sensor control strategy of a permanent magnet synchronous motor (PMSM) often adopts the DC bus method, which makes it difficult to eliminate the blind area of current reconstruction. Therefore, a current reconstruction method based on a sliding mode observer is proposed. Based on the current equation of the motor, the method takes the α-axis and β-axis currents as the observation objects and shares the same synovial surface, so that the α-axis current observation value and the β-axis current observation value converge to the actual current value at the same time and the unknown β-axis current information is obtained. The control system first tests the performance of the motor under different working conditions when the parameters are matched, and then tests the current reconstruction ability of the parameter mismatch. The results show that the current observer with a matched parameter can accurately and quickly reconstruct the β-axis current under various operating conditions, and the maximum current error does not exceed 4 mA. When the parameters are mismatched, high-performance control of the motor can still be achieved. The proposed method has excellent robustness.
This paper studies the operating uniformity of a high-pressure common-rail diesel engine based on crankshaft segment signals. Engine nonuniformity or unevenness refers to the crankshaft torque fluctuation caused by cylinder-to-cylinder differences, which are caused by misfiring or difference in fuel supply or air supply. The experiments were conducted on a diesel engine of the YN30CR model. Based on the relationships among the cylinder pressure, the instantaneous crankshaft angular speed, and the crankshaft segment signal, it is found that the crankshaft segment signal can be used to characterize the operating nonuniformity of the diesel engine. Based on the Fast Fourier Transformation (FFT) from the angular domain to the frequency domain in analyzing the fluctuation patterns of the crankshaft segment signals at different operating conditions, the nonuniformity information was extracted. In order to further reveal the relationship between the fluctuation phenomena of the crankshaft segment signal and the cause of the fluctuation, the torque acting on the crankshaft of the multi-cylinder diesel engine was studied to quantify the operating nonuniformity of the diesel engine. The results show that the crankshaft segment signal can reflect the uniformity of the engine torque very well in both phase and amplitude, and the degree of nonuniformity of the engine torque can be reflected by the amplitudes of the low-order non-dominant harmonics of the crankshaft segment signal below the firing frequency. The crankshaft segment signal is very consistent with the variation pattern of the engine torque.
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