Parameter Optimization Of Six-Axis Accelerometer Based On Genetic Algorithm

With the trend of forestry mechanization becoming more and more obvious,the anti-rolling of forestry vehicles operating in mountains and hills has become an important research content.The six-axis accelerometer not only can obtain the chassis position of the vehicle for the TTR anti-rollover algorithm,but also widely used in the field of robot dexterous hands.Parallel six-axis accelerometer have many structural parameters,it is difficult to obtain ideal optimization values and affect sensor performance,especially for small-range parallel six-dimensional accelerometer,due to structural errors in the process of materialization of theoretical models and processing and assembly during machining.The influence of the error on the performance of the sensor in the actual measurement process can not be ignored.Based on this,the genetic algorithm is used to optimize the single parameter value to the domain value to meet the performance requirements of the sensor value during the actualization and physicalization of the sensor,the impact of structural errors and assembly errors on sensor performance be reduced.Firstly,the mathematical model of the six-axis accelerometer is established.Based on this model,the acceleration Jacobian matrix is derived by using the spiral theory,and the measurement principle of the sensor is analyzed.The static and dynamic characteristics of the sensor were studied,and the performance evaluation system of the sensor was established.Especially,the acceleration isotropic and sensitivity characteristics were studied.The objective function of acceleration isotropic index and sensitivity isotropic index is solved by calculating the singular value of Jacobian matrix.According to the condition that the isotropic index is optimal at the same time,the relationship between the dimensionless size parameters of the sensor is obtained,and the initial range of parameter optimization is selected.The genetic algorithm is used to optimize the parameters in the process of sensor materialization.The algorithm parameters and operators are initialized and selected,and the relationship between the algorithm fitness function and the sensor isotropic index is established.The optimal parameter range of the sensor performance index is obtained by multiple runs.According to the optimization result,the parameter values in the optimized parameter range are selected,and the sensor entity based on the isotropic simulation experiment is designed.The isotropic verification experiment is applied to the sensor finite element model.The experimental results show the structural design.Rationality proves the effectiveness of algorithm optimization.In order to improve the signal-to-noise ratio of the measuring circuit,the structure of the sensor elastic connecting rod is redesigned.The finite element simulation results show that the sensor structure can greatly improve the signal-to-noise ratio of the measuring circuit.Finally,the dynamic characteristics of the sensor are studied,and the natural frequency and mode shape of the sensor are obtained,and the operating frequency band of the sensor is determined.Through the research of the above content,the mathematical model of sensor parameter optimization is established,and the optimized parameter range is obtained.The parameter value in this range is selected to design an accelerometer with good isotropicity,which greatly improves the measurement accuracy of the sensor.