Model-based control can provide high-accuracy performance over position-based or velocity-based control. Therefore, to employ model-based control in industrial robots, it is important to estimate the dynamic parameters as accurately as possible. However, traditional estimation methods, such as least squares (LS), are not sufficiently accurate, and the feasibility of dynamic parameters cannot be guaranteed. In this article, an iterative hybrid least square (IHLS) algorithm is proposed to estimate the base parameters of industrial robots by dividing the identification processes into two loops. The inner loop integrates a linear matrix inequality with the semidefinite programming technique to guarantee physical feasibility and reuses the torque deviations between the measured torque and predicted torque to estimate the base parameters of the robot, while the outer loop substitutes the Stribeck friction model for the Coulomb-viscous friction model to estimate the joint friction torque. Moreover, backpropagation neural network (BPNN) is introduced to further estimate the joint friction torque based on the Stribeck friction model. Experiments are conducted on two industrial robots, and four methods are compared in dynamic parameter identification. Experimental results show that the hybrid approach of the IHLS algorithm with the BPNN has the best performance among the four methods.

This paper proposes a novel micro-electromechanical system (MEMS) piezoresistive pressure sensor with a four-petal membrane combined with narrow beams and a center boss (PMNBCB) for low-pressure measurements. The stresses induced in the piezoresistors and deflection of the membrane were calculated using the finite element method (FEM). The functions of the relationship between the dimension variables and mechanical performance were determined based on the curve fitting method, which can provide an approach for geometry optimization of the sensor. In addition, the values in the equations were varied to determine the optimal dimensions for the proposed membrane. Then, to further improve the sensitivity of the sensor, a series of rectangular grooves was created at the position of the piezoresistors. The proposed diaphragm was compared to existing diaphragms, and a considerable increase in the sensitivity and a considerable decrease in nonlinearity error could be achieved by using the proposed sensor. The simulation results suggest that the sensor with the PMNBCB structure obtained a high sensitivity of 34.67 mV/kPa and a low nonlinearity error of 0.23% full-scale span (FSS) for the pressure range of 0–5 kPa. The proposed sensor structure is a suitable selection for MEMS piezoresistive pressure sensors.