The Research On State Estimation Method For Power Inspection Robot

State estimation method is one of the key technologies for power inspection robot,which is widely used in robot integrated navigation,target tracking,signal processing and fault diagnosis.At the same time,the state estimation lays a solid foundation for the stable work of the power inspection robot.However,the accuracy of state estimation will be seriously affected when there are nonlinear coupling problems and uncertainty of noise statistical characteristics.In order to reduce the adverse effects of nonlinear coupling and uncertain parameters or deviations on the system state estimation,the method of state estimation is deeply studied in this paper.The main research is as follows.(1)For nonlinear coupled system state estimation with linear state driving,the traditional nonlinear Kalman filtering algorithm has some shortcomings,such as large computation and insufficient estimation accuracy.In order to reduce the calculation amount of system estimation,improve the precision of state estimation and improve the working efficiency of power inspection robot.A two-stage state estimation algorithm based on model prediction is proposed by model parameter decoupling method.A new two-stage subsystem is established by model decoupling and reconstruction.The simulation results show that the two-stage state estimation algorithm based on model prediction can effectively improve the accuracy of nonlinear system state estimation without increasing the extra calculation amount.(2)The intelligent optimization algorithm is widely used in the process of state estimation.Aiming at the problem that the Beetle Antennae Search algorithm tends to fall into local optimum in the fusion process of the Beetle Antennae Search algorithm and state estimation algorithm,an improved Beetle Antennae Search algorithm based on inertia weight and attenuation factor is proposed.The inertia weights of normal distribution,versoria distribution and random distribution are introduced to improve the search strategy and control the proportion of global search and local search,so that the algorithm can avoid local optimization.At the same time,the randomness is introduced in the updating process of Beetle step,which can better help Beetle escape from the local optimal.Finally,the simulation experiments on the standard test function show that the algorithm has obvious performance improvement in the processing of high dimensional and multi-peak problems.(3)In order to solve the problem that the performance of state estimation is degraded due to the unknown statistical characteristics for noise in the process of system state estimation,a nonlinear system state estimation method based on improved Beetle Antennae Search algorithm is proposed.Firstly,based on the minimum innovation error covariance,an improved Beetle Antennae Search algorithm is used to search noise parameters.Then the search noise is substituted into the Kalman filtering process.Finally,more accurate state estimation results are obtained through the prediction updating step of Kalman filter.Simulation results show that the proposed algorithm can effectively improve the estimation accuracy of the system in the case of unknown statistical characteristics of noise during the motion of the inspection robot.