Robust self-tuning regressive adaptive controller design for a DC–DC BUCK converter

Khavari

2022

IET Power Electronics

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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.

Section: Rls Estimatormentioning

confidence: 99%

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

Mollaee

Khavari

2022

IET Power Electronics

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“…Furthermore, in this new criterion the estimation regarding covariance matrices is being updated under actuation. Finally, the estimation procedure is enhanced by this factor that tries to update the pattern of covariance matrix under the occurrence of Equation () ,which can hinder this issue [42].

\begin{eqnarray} \def\eqcellsep{&}\begin{array}{l}\phi (t)^{T} P(t) \phi (t) &gt;2(-\lambda +1) \end{array} \end{eqnarray}

A novel method is used here to handle this issue which develops the covariance matrix that is “fixed matrix rejection method”.…”

Section: Rls Identification Processmentioning

confidence: 99%

Generalised model predictive controller design for A DC–DC non‐inverting buck–boost converter optimised with a novel identification technique

Khavari

Molaee

et al. 2022

IET Power Electronics

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An on-line generalised model predictive control (GMPC) strategy is designed and optimised with a novel identification procedure in the presence of different disturbances. The principle of MPC is utilising a discrete-time model of a system to reach the control variables with a prediction over these values, which is followed by computing a cost function for the control aims. Non-inverting buck-boost converter is a non-minimum phase system based on its boost mode, which makes a challenging condition for designing a stable controller. The proposed control technique described in this paper removes the requirement for a system mathematical model adopting a black-box identification method which can decrease the computational burden. Numerous harmful disturbances can affect a DC-DC converter; thus, the GMPC scheme is used along with a novel improved exponential regressive least identification algorithm as an adaptive strategy for the controller to optimise the gains of the controller in an on-line way resulting in better disturbance rejection. A PID controller with particle swarm optimisation algorithm is designed for this converter to be compared with the GMPC controller. Finally, the efficiency of the GMPC is verified in various performances with experimental and simulation results.

“…All the issues mentioned by these strategies are covered by refs. [38,39], which proposed a novel robust identification method with a simple structure. Nonetheless, the main drawback concerned by this technique is its high dependency on parametric estimation.…”

Section: Introductionmentioning

confidence: 99%

“…RLS identification technique is a well-known estimator owing to its main benefits, namely: easy implementation, high efficiency in real-time operations, dynamical adaptability, and low memory capacity[38]. Equation(25) shows the cost function of the RLS…”

mentioning

confidence: 99%

Adaptive backstepping controller design on Buck converter with a novel improved identification method

Saadat

IET Control Theory & Appl

Mollaee

2022

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An adaptive backstepping method is presented by this paper for a DC-DC Buck converter utilising a strategy for system identification with pulse width modulation in the presence of parametric uncertainties, load variations, and high variance noises. In this control structure, the system is assumed as a black-box block that can decrease the computational burden providing faster dynamics. An adaptive mechanism is adopted for the BSM using the Lyapunov definition, providing robust dynamics for the controller against various disturbances. In addition, a novel improved exponential recursive least-squares identification algorithm is proposed, which shows higher robustness in parametric estimations and can decrease the negative impact of disrupting factors on the estimator. Moreover, a particle swarm optimisation algorithm-based PID controller is designed to be compared with the proposed controller. Finally, the merits of the presented controller are validated for various working conditions through simulations and experiments. It can be seen that the adaptive backstepping method with the improved identification technique provides much better results with faster dynamics.