The friction interference in the pneumatic rotary actuator is the primary factor affecting the position accuracy of a pneumatic rotary actuator servo system. The paper proposes an evolutionary algorithm-based friction-forward compensation control architecture for improving position accuracy. Firstly, the basic equations of the valve-controlled actuator are derived and linearized in the middle position, and the transfer function of the system is further obtained. Then, the evolutionary algorithm-based friction feedforward compensation control architecture is structured, including that the evolutionary algorithm is used to optimize the controller coefficients and identify the friction parameters. Finally, the contrast experiments of four control strategies (the traditional PD control, the PD control with friction feedforward compensation without evolutionary algorithm tuning, the PD control with friction feedforward compensation based on the differential evolution algorithm, and the PD control with friction feedforward compensation based on the genetic algorithm) are carried out on the experimental platform. The experimental results reveal that the evolutionary algorithm-based friction feedforward compensation greatly improves the position tracking accuracy and positioning accuracy, and that the differential evolution-based case achieves better accuracy. Also, the system with the friction feedforward compensation still maintains high accuracy and strong stability in the case of load.

In order to accurately control the rotation position of a pneumatic rotary actuator, the flow state of the gas and the motion state of the pneumatic rotary actuator in the pneumatic rotary actuator position servo system are analyzed in this paper. The mathematical model of the system and the experiment platform are established after that. An Adaptive Differential Evolution (ADE) algorithm which adaptively ameliorates the scaling factor and crossover probability in the process of individual evolution is proposed and applied to the parameter optimization of PD controller. The experimental platform is used to compare the controller with Differential Evolution (DE) algorithm and NCD-PID controller. Finally, the characteristics of the system are tested by increasing the inertial load. The experimental results illustrate that system using ADE-PD control strategy has greater position precision and faster response than using DE-PD and NCD-PID strategies, and shows great robustness.

In order to accurately reflect the characteristics and motion states of a pneumatic rotary actuator position servo system, an accurate non-linear model of the valve-controlled actuator system is proposed, and its parameter identification and experimental verification are carried out. Firstly, in the modeling of this system, the mass flow rate of the gas flowing through each port of the proportional directional control valve is derived by taking into account the clearance between the valve spool and the sleeve, the heat transfer formula is used to the derivation of the energy equation, and the Stribeck model is applied to the friction model of the pneumatic rotary actuator. Then, the flow coefficient, the heat transfer coefficient and the friction parameters are identified by the model and pneumatic test circuits. After the verification experiment of the mass flow rate equations, the charging and discharging experiment reveals that the model can clearly show the effect of clearances on gas pressure changes and describe the effect of heat transfer on gas temperature changes. Finally, the results of model verification indicate that the simulation curves of rotation angle and two-chamber pressures are consistent with their experimental values, and the non-linear model shows high accuracy.

In this paper, a hybrid computational framework that combines the state-of-the art machine learning algorithm (i.e., deep neural network) and nonlinear finite element analysis for efficient and accurate fatigue life prediction of rotary shouldered threaded connections is presented. Specifically, a large set of simulation data from nonlinear FEA, along with a small set of experimental data from full-scale fatigue tests, constitutes the dataset required for training and testing of a fast-loop predictive model that could cover most commonly used rotary shouldered connections. Feature engineering was first performed to explore the compressed feature space to be used to represent the data. An ensemble deep learning algorithm was then developed to learn the underlying pattern, and hyperparameter tuning techniques were employed to select the learning model that provides the best mapping, between the features and the fatigue strength of the connections. The resulting fatigue life predictions were found to agree favorably well with the experimental results from full-scale bending fatigue tests and field operational data. This newly developed hybrid modeling framework paves a new way to realtime predicting the remaining useful life of rotary shouldered threaded connections for prognostic health management of the drilling equipment.