Self‐tuning regulator adaptive controller design for DC‐DC boost converter with a novel robust improved identification method

Abstract

The design of a self-tuning regulator adaptive controller is presented for Boost converter using Pulse Width Modulation, which has been improved in dynamic with a novel Robust Improved Exponential Regressive Least Square identification scheme. Regarding the negative influences of harmful disturbances on the converters, the Minimum Degree Pole Placement technique is designed using an adaptive mechanism with an online identification technique, which is simple in structure and can provide more accurate parametric estimation and better efficiency in challenging situations, particularly against noise. Based on the right half-plane zero structure of the Boost converter, the open-loop system is called a non-minimum phase system which creates some challenging constraints for the controller design. The primary objective of designing a feedback loop controller is to ensure the stability of the system and appropriate regulation within a pre-determined range of operating conditions. This controller considers the system as a black-box structure; thus, the mathematical model of the system is not needed. This benefit can decrease the computational burden, and provides faster dynamics along with ease of implementation. Finally, the strength of this control strategy is tested by experimental and simulation results in different conditions.

Fractional-Order PID Controller Design on Buck Converter with Antlion Optimizer Algorithm

“…As a result, to provide suitable performance for power converters under harmful disturbances, conventional methods do not contain suitable efficiency; hence, improved controllers are required to provide a robust operation against various disturbances. Thus, this novel GMPC High [19] STR High [20] STR High [21] Cascade Very High [22] ABSC Very High [23] FO-PID Low ×…”

“…Meanwhile, chattering phenomena, higher complexity, high cost of implementation, and undesirable results against noise are their main negative points. All the issues mentioned by these strategies are covered by , which proposed a Self-tuning adaptive strategy based on a robust identification procedure benefited from a simple scheme [19][20][21][22] . However, this robust adaptive structure has a high dependency on the identification process resulting in the lack of reliability in the control block.…”

Design of a novel robust adaptive cascade controller for DC‐DC buck‐boost converter optimized with neural network and fractional‐order PID strategies

“…This method presents good dynamic response and significant output regulation with a simple structure suitable for practical usage, but considering possible parameter-model uncertainties and harmful disturbances on converters, these schemes cannot guarantee good performance, particularly with parameter uncertainties and external disturbance conditions. In [10][11][12], some optimal LQR and adaptive controllers have been proposed for power converters to improve their dynamical performance in different conditions; however, lack of data-driven from error dynamics and high dependency on an exact mathematical model of the system make it weak against further disturbances such as load variations, high variance noises, and fluctuations in supply voltage [13][14][15].…”

“…Also, it can be applied in controlling very complex or ill-defined systems where a precise mathematical model is not reachable [18][19][20]. NN strategy has been used as an optimizer in various modern controllers such as neural network fuzzy logic schemes [21][22][23], predictive neural network controllers [24,25], and neural adaptive strategies [15,26]. Some main issues are reported for these works; (1) stable dynamics in non-actuated conditions, (2) dependency on a large amount of learning data leading to a substantial computational burden, and (3) short circuit problems.…”

Lyapunov-based PID adaptive controller design for Buck converter

“…Meanwhile, chattering phenomena, higher complexity, high cost of implementation, and undesirable results against noise are their main negative points. The issues mentioned by these strategies are covered by Mollaee and Hasan (2021); Mollaee et al (2022); Saadat et al (2022); Ghamari et al (2021), which proposed a Self-tuning adaptive strategy based on a robust identification procedure benefited from a simple scheme. However, these robust adaptive structures have a high dependency on the identification process resulting in a lack of reliability in the control block.…”