Self-Tuning Controller for Scalar Systems: a Preselected-Time Control Approach

A Single-Input Single-Output (SISO) linear system can represent various physical systems. One of the challenges in controlling practical systems is that the settling point and settling time cannot be prescribed together. In this paper, a novel Self-Tuning Control (STC) algorithm is presented, which ensures system stability while allowing the simultaneous determination of the settling time and settling point. Four possible scenarios will be defined to investigate the effectiveness of the proposed control method. Three different practices will be described in the simulation section for each scenario. Linear Quadratic Regulation (LQR) will be employed for comparison purposes. MATLAB software will be used to test and simulate these practices. The simulations will demonstrate that the STC is an optimal solution for SISO linear systems, comparable to LQR, with the significant advantage of guaranteeing the attainment of the settling point at the predefined settling time.

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Publication Details

Journal
Journal of Automation Mobile Robotics & Intelligent Systems
Published
2025-09-09
DOI
https://doi.org/10.14313/jamris-2025-022
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Self-Tuning Controller for Scalar Systems: a Preselected-Time Control Approach

Ali Soltani Sharif Abadi
Journal of Automation Mobile Robotics & Intelligent Systems
Advanced Control Systems Optimization
article

Self-Tuning Controller for Scalar Systems: a Preselected-Time Control Approach

Ali Soltani Sharif Abadi
article en

Abstract

A Single-Input Single-Output (SISO) linear system can represent various physical systems. One of the challenges in controlling practical systems is that the settling point and settling time cannot be prescribed together. In this paper, a novel Self-Tuning Control (STC) algorithm is presented, which ensures system stability while allowing the simultaneous determination of the settling time and settling point. Four possible scenarios will be defined to investigate the effectiveness of the proposed control method. Three different practices will be described in the simulation section for each scenario. Linear Quadratic Regulation (LQR) will be employed for comparison purposes. MATLAB software will be used to test and simulate these practices. The simulations will demonstrate that the STC is an optimal solution for SISO linear systems, comparable to LQR, with the significant advantage of guaranteeing the attainment of the settling point at the predefined settling time.

Journal of Automation Mobile Robotics & Intelligent Systems
Warsaw University of Technology (PL)
Openalex Percentile: Top 70%
Advanced Control Systems Optimization
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