Triple nonlinear hyperbolic PID with static friction compensation for precise positioning of a servo pneumatic actuator.

Abstract

Accurate and precise positioning control is critical in designing a positioning servo pneumatic system. The internal friction force of the pneumatic is one of the disturbances that make it challenging to achieve accurate and precise positioning. Dynamic friction identification and modelling are usually very complex and computationally exhaustive. In addition, pneumatic actuators are nonlinear systems, and applying linear control to the system is a mismatch. This study proposes an enhanced triple nonlinear hyperbolic PID controller with static friction (T-NPID+FSS) feedback module. T-NPID is integrated with nonlinear hyperbolic functions at each PID gain, hence the name. The reference in designing the T-NPID is the Popov stability criterion. Meanwhile, static friction (comparatively more straightforward than dynamic friction) is identified by measuring the actuator's internal friction at various velocities and applying it to the static friction model. T-NPID+FSS is compared to a classical PID, a PID with static friction (PID+FSS), and T-NPID without the friction module. With the comparisons, the performance gains of each module are clear. While most previous research focuses on the sinusoidal wave tracking performance (measuring the maximum tracking error, MTE, and root mean square error, RMSE), the analysis in this research focuses on obtaining precise positioning; steady-state analysis is the primary measurement. However, transient response and integral of absolute error (IAE) analysis are also observed to ensure no significant drawback in the controller's performance. T-NPID+FSS achieved the best precise positioning control, with 88.46% improvement over PID, 71.15% over PID+FSS, and 59.46% over T-NPID. The final controller is also on par with T-NPID for transient responses compared to the base PID. Although the FSS model caters to friction compensation, optimizing the FSS parameter by applying artificial intelligence, such as Neural Networks (NN) and Genetic Algorithm (GA), will increase the friction modeling‘s accuracy, and improve the compensation.