Adaptive Ramp Technique for Controlling Chaos and Subharmonic Oscillations in DC–DC Power Converters

Morcillo, José D.; Burbano, Daniel; Angulo, Fabiola

doi:10.1109/tpel.2015.2487269

Cited by 41 publications

(21 citation statements)

References 33 publications

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“…This establishes the following relationship between the steady‐state duty cycle D and

y_{avg} false[\infty false]

y_{avg} false[\infty false] = - boldC {false(A_{1} D + A_{0} \overset{D}{true} false)}^{- 1} false(B_{1} D + B_{0} \overset{D}{true} false) boldw .

As commented in El Aroudi and El Aroudi et al,() the steady‐state value D can be obtained from the previous equation once all the matrices and the parameters of the system are specified and there is no need to obtain it using numerical root‐finding algorithms such as in previous studies. () Once D is obtained, the steady‐state state vector x (0) and x ( D T ) are straightforward from and , respectively.…”

Section: Review Of Discrete‐time Modeling Of Switching Converters Undmentioning

confidence: 99%

“…The ramp voltage can be used either for modulation or for slope compensation. For instance, in conventional VMC, the ramp is used for modulation 7,19,36,38 while in CMC and in ripple-based VMC, it is used for compensation. 4,26…”

Section: The Continuous-time Switching Condition Dictated By the Pwmmentioning

confidence: 99%

“…As commented in El Aroudi and El Aroudi et al, 5,6 the steady-state value D can be obtained from the previous equation once all the matrices and the parameters of the system are specified and there is no need to obtain it using numerical root-finding algorithms such as in previous studies. 4,19,36,38,39 Once D is obtained, the steady-state state vector x(0) and x(DT) are straightforward from (16a) and (16b), respectively.…”

Section: The Fundamental Steady-state Periodic Response Of the Systemmentioning

confidence: 99%

“…system stability requirements while maximizing the bandwidth of the system thus and optimizing the fast controllers design. Unlike many existing models in the literature dealing with accurate prediction of switching converters based on nonaveraging procedures, 4,19,[36][37][38][39] here, we break the feedback loop and we focus on analyzing the open-loop gain and the effect of the system parameters on relative stability. As a consequence, the integral loop widely used in the output feedback to get zero static error is separated from the rest of dynamics hence avoiding many singularity problems appearing in the expressions of the system trajectories and their steady-state values.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Nonaveraged control‐oriented modeling and relative stability analysis of DC‐DC switching converters

Al‐Turki

Aroudi

Mandal

et al. 2017

Circuit Theory & Apps

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Summary This paper presents a unified and exact nonaveraged approach to derive a frequency‐domain control‐oriented model for accurate prediction of the fast timescale dynamics and performances of switching converters with fixed frequency naturally sampled pulse width modulation and integrating feedback loop. Because the approach avoids averaging and approximations related to this process, a very good accuracy of the derived model is obtained. The main difference between the presented approach and the existing methodology for accurately predicting the behavior of switching converters is that, here, we break the feedback loop and we focus on analyzing the open‐loop gain and the effect of the system parameters on relative stability. This results in an approach much similar to control systems techniques rather than nonlinear dynamical system approaches. Consequently, the relative stability is tackled easily in the frequency domain. In particular, by treating the modulator as a gain depending on the operating point, the new model is formulated in such a way that standard control‐oriented tools such as Bode diagrams and root‐loci can be easily used. Therefore, the proposed approach gives some important issues like gain and phase margins that are highly useful in controller design. It is noticed that the crossover frequency, gain, and phase margins predicted by using the averaged model may deviate significantly from the actual values given by the proposed approach. The paper points out the sources of discrepancies and the theoretical results are validated by simulations using a circuit‐level switched model.

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“…This establishes the following relationship between the steady‐state duty cycle D and

y_{avg} false[\infty false]

y_{avg} false[\infty false] = - boldC {false(A_{1} D + A_{0} \overset{D}{true} false)}^{- 1} false(B_{1} D + B_{0} \overset{D}{true} false) boldw .

Section: Review Of Discrete‐time Modeling Of Switching Converters Undmentioning

confidence: 99%

Section: The Continuous-time Switching Condition Dictated By the Pwmmentioning

confidence: 99%

Section: The Fundamental Steady-state Periodic Response Of the Systemmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Nonaveraged control‐oriented modeling and relative stability analysis of DC‐DC switching converters

Al‐Turki

Aroudi

Mandal

et al. 2017

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

“…In recent years, the scientific community has paid increasing attention to the chaotic systems and their applications in various areas of science and engineering. Such systems have been investigated in such fields as parameter identifications [14], optimizations [45], electronic circuits [46], electric motor drives [47,48], power electronics [49], communications [50], robotics [51], and many others. Feng et al [52] introduced two means of modifying the inertia weight of a PSO using chaos.…”

Section: Chaos-enhanced Particle Swarm Optimization With Adaptive Parmentioning

confidence: 99%

A Chaos-Enhanced Particle Swarm Optimization with Adaptive Parameters and Its Application in Maximum Power Point Tracking

Hong

Beltran

Paglinawan

2016

Mathematical Problems in Engineering

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This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type 1 constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system.

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Nonlinear analysis of interleaved boost converter with a novel valley V ² control

Sun

Qiu

Liu

et al. 2021

Int Trans Electr Energ Syst

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In this paper, a novel valley V 2 control scheme for interleaved boost converter is presented to improve the transient response performance of system and realize the current sensorless control, and its stability is analyzed by a nonlinear analysis technique. In this proposed methodology, the current sensor is saved by the proposed control signals sharing algorithm, and the nonlinear dynamics is investigated by employing a nonlinear analysis method based on the Monodromy matrix. Meanwhile, the information of the converter parameters' influence on the system stability can be obtained by calculating the Monodromy matrix of switching event. It is worth mentioning that the location of eigenvalues of this matrix can be utilized to analyze the relationship between converter parameters and stability margin. Furthermore, by adopting the knowledge gained from this analysis, an auto-tuning resonant parametric perturbation is proposed to ensure an excellent performance of converter with an extended range of stable operation. Finally, the simulation and experimental results are presented to verify the effectiveness and theoretical analysis of the proposed method.

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Adaptive Ramp Technique for Controlling Chaos and Subharmonic Oscillations in DC–DC Power Converters

Cited by 41 publications

References 33 publications

Nonaveraged control‐oriented modeling and relative stability analysis of DC‐DC switching converters

Nonaveraged control‐oriented modeling and relative stability analysis of DC‐DC switching converters

A Chaos-Enhanced Particle Swarm Optimization with Adaptive Parameters and Its Application in Maximum Power Point Tracking

Nonlinear analysis of interleaved boost converter with a novel valley V ² control

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Adaptive Ramp Technique for Controlling Chaos and Subharmonic Oscillations in DC–DC Power Converters

Cited by 41 publications

References 33 publications

Nonaveraged control‐oriented modeling and relative stability analysis of DC‐DC switching converters

Nonaveraged control‐oriented modeling and relative stability analysis of DC‐DC switching converters

A Chaos-Enhanced Particle Swarm Optimization with Adaptive Parameters and Its Application in Maximum Power Point Tracking

Nonlinear analysis of interleaved boost converter with a novel valley V 2 control

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Nonlinear analysis of interleaved boost converter with a novel valley V ² control