Auto-Regression Model-Based Off-Line PID Controller Tuning: An Adaptive Strategy for DC Motor Control

Abstract

Brushed (B) and Brushless (BL) DC motors constitute the cornerstone of mechatronic systems regardless their sizes (including miniaturized), in which both position and speed control tasks require the application of sophisticated algorithms. This manuscript addresses the initial step using time series analysis to forecast Back EMF values, thereby enabling the elaboration of real-time adaptive fine-tuning strategies for PID controllers in such a control system design problem. An Auto-Regressive Moving Average (ARMA) model is developed to estimate the DC motor parameter, which evolves in time due to the system’s imperfection (i.e., unpredictable duty cycle) and influences the closed-loop performance. The methodology is executed offline; thus, it highlights the applicability of collected BDC motor measurements in time series analysis. The proposed method updates the PID controller gains based on the Simulink ™ controller tuning toolbox. The contribution of this approach is shown in a comparative study that indicates an opportunity to use time series analysis to forecast DC motor parameters, to re-tune PID controller gains, and to obtain similar performance under the same perturbation conditions. The research demonstrates the practical applicability of the proposed method for fine-tuning/re-tuning controllers in real-time. The results show the inclusion of the time series analysis to recalculate controller gains as an alternative for adaptive control.

Position and Speed Tracking of DC Motor Based on Experimental Analysis in LabVIEW

“…The Figure 3a shows that tracking error is quite negligible using MATLAB simulation based on PID controller. The controller input based on a PID controller can be defined as in Equation ( 8) [24,25].…”

“…Figure 2 shows the cascaded control strategy based on a PID system to reflect the 𝜃 to 𝜃 in a closed-loop scheme. The controller input based on a PID controller can be defined as in Equation ( 8) [24,25].…”

Development of DC Motor Speed Control Using PID Based on Arduino and Matlab For Laboratory Trainer

“…Ziegler-Nichols method was also reported to provide PID constants with fast rise time [17]. Ziegler-Nichols method offers more straightforward computation than analytical tuning models, such as an auto-regression model [18] or a fuzzy-based tuning algorithm [19]. However, the transient response from PID control chosen using Ziegler-Nichols has the disadvantage that the maximum overshoot is still considerably significant, more than 20%.…”