System response modeling of HMCVT for tractors and the comparative research on system identification methods - Cheng, Zhun; Lu, Zhixiong (Computers and electronics in agriculture, 2022)
•A novel system identification method based on the prior model was proposed.•The general form of tractor’s HMCVT system response model was deduced and verified.•The FI-SA proposed was proved to have the fastest identification speed.•The new method proposed has low dependence on sampling frequency and time consumed.•5. The tractor’s HMCVT system test bench was built and used for system response testing. The HMCVT (Hydro-mechanical Continuously Variable Transmission) is widely applied in agricultural and engineering machinery such as tractors. Getting a correct system response model is the key to HMCVT control strategy implementation and controller design. The study decomposes and analyzes the HMCVT system and builds response models. The study proposes to decompose the HMCVT system into three dynamic response stages including the electronic proportional pressure relief valve response, the variable plunger displacement response and the pump-motor system speed ratio response, and builds the response models of three stages respectively and then obtains the general form of transfer function of HMCVT system using a tandem method. The study proposes a system identification method based on the prior-model-heuristic-intelligent-optimization algorithm, and makes a comparative analysis between the classical system identification method (the area method) and the three heuristic intelligent optimization algorithms (the FI-SA, the I-SA and the I-AFSA) in terms of system identification result. The study compares the results of identification methods through a step response test of a model of HMCVT for tractors, and verifies the correctness of the transfer function model of HMCVT system proposed. Research results show that the general form of HMCVT system response model should be a 4-order denominator 1-order numerator transfer function model (the mean identification precision of 15 groups of measurement tests is about 0.9849). The area method is affected by the sampling frequency in the response measurement and requires a complete response process measured. The system identification based on heuristic intelligent optimization algorithm has low dependence on sampling frequency and time consumed and high precision. According to the results of the calculation example in the study, the coefficient of determination about the identified model is 1 under the measurement frequency of 4 Hz and the measurement duration of 4 s by using the FI-SA, the I-SA, and the I-AFSA. The precision is improved by 4.93 % compared with using the area method. According to the actual test results, the coefficients of determination about the FI-SA and the area method are 0.9939 and 0.9834, respectively. Comparing the three heuristic intelligent optimization algorithms, the FI-SA proposed has the fastest identification speed. The results of 20 groups measurement tests show that the average iteration number of FI-SA are 43.58 % and 17.95 % lower than those of I-SA and I-AFSA, and the average time consumption of FI-SA is 43.36 % and 84.76 % lower than that of I-SA and I-AFSA.