PID-GM: PID Control with Gain Mapping
By: Bo Zhu, Wei Yu, Hugh H. T. Liu
Potential Business Impact:
Makes machines work better, even with mistakes.
Proportional-Integral-Differential (PID) control is widely used in industrial control systems. However, up to now there are at least two open problems related with PID control. One is to have a comprehensive understanding of its robustness with respect to model uncertainties and disturbances. The other is to build intuitive, explicit and mathematically provable guidelines for PID gain tuning. In this paper, we introduce a simple nonlinear mapping to determine PID gains from three auxiliary parameters. By the mapping, PID control is shown to be equivalent to a new PD control (serving as a nominal control) plus an uncertainty and disturbance compensator (to recover the nominal performance). Then PID control can be understood, designed and tuned in a Two-Degree-of-Freedom (2-DoF) control framework. We discuss some basic properties of the mapping, including the existence, uniqueness and invertibility. Taking as an example the PID control applied to a general uncertain second-order plant, we prove by the singular perturbation theory that the closed-loop steady-state and transient performance depends explicitly on one auxiliary parameter which can be viewed as the virtual singular perturbation parameter (SPP) of PID control. All the three PID gains are monotonically decreasing functions of the SPP, indicating that the smaller the SPP is, the higher the PID gains are, and the better the robustness of PID control is. Simulation and experimental examples are provided to demonstrate the properties of the mapping as well as the effectiveness of the mapping based PID gain turning.
Similar Papers
Online Optimal Parameter Compensation method of High-dimensional PID Controller for Robust stability
Systems and Control
Makes machines with many parts work smoothly.
Homogeneous Proportional-Integral-Derivative Controller in Mobile Robotic Manipulators
Robotics
Makes robots move and grab things better.
Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory
Artificial Intelligence
Makes machines learn to fix themselves better.