New technique for voltage tracking control of a boost converter based on the PSO algorithm and LTspice

Farhang, Peyman; Drimus, Alin; Mátéfi‐Tempfli, Stefan

doi:10.1109/rtucon.2015.7343157

Cited by 5 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The total diode conduction loss is represented by P DL_D = P RF + P VF (22) where P RF is the power loss in the diode forward resistance and is given as (23) where R F is the diode forward resistance…”

Section: Volume 7 2019mentioning

confidence: 99%

“…Unfortunately, these techniques are complicated by the issues of technique dissatisfaction, the use of trial-anderror, the need for a few calculation operations and a complex mathematical version [20]. Other optimization methods, namely, the genetic algorithm GA-PI optimization [21], PSO-PID optimization [22], and BSA-PI optimization, have been used to improve the behavior of the PI voltage controller (VC) and to find the parameters of the PI controller [23]. The LSA is an optimization method that has the benefit of steady robustness, a global convergence capability, and ease of implementation [24], [25].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

dSPACE Controller-Based Enhanced Piezoelectric Energy Harvesting System Using PI-Lightning Search Algorithm

et al. 2019

View full text Add to dashboard Cite

This paper presents a new lightning search algorithm (LSA) to enhance the piezoelectric energy harvesting system converter (PEHSC) using the dSPACE DS1104 controller board as the proportionalintegral voltage controller (PIVC). To extract the energy from the vibration is challenging and difficult due to the uncertain behavior of vibration. Since the piezoelectric vibration transducer generates low AC voltage output with fluctuations and harmonics, it is difficult to control this low-level signal of various magnitudes. Therefore, the behavior of the converter is governed by its controller. The traditional PIVC process for improved parameter values of proportional gain (Kp) and integral gain (Ki) is commonly implemented via trial and error, which does not lead to an acceptable response in several conditions. Hence, this paper offers a method for finding the optimal Kp and Ki values for PIVC that eliminates the time-consuming conventional trial-and-error process. This method is applied to PEHSC development by producing values of Kp and Ki performed in the PIVC depending on the estimated outcomes of the objective function defined via LSA. The mean absolute error (MAE) is used as the objective function for reducing the output error of the PEHSC. The LSA optimizes the Kp and Ki values that give the minimum MAE, and the effect on the PEHSC is in terms of the rising and settling times. The development process and efficiency of the PIVC are demonstrated and examined via simulations using the MATLAB tools. The LSA-based PIVC (LSA-PI) is compared with the particle swarm optimization (PSO)-based PIVC (PSO-PI) and the backtracking search algorithm (BSA)based PIVC (BSA-PI). The performance of the LSA-PI-based PIVC is then validated through hardware implementation using the dSPACE DS1104 control board. The simulation results are compared with the hardware results of PEHSC to validate the overall efficiency of the system. Finally, the results are regulated at an output of 7 V DC from an input range of 150 mV∼250 mV AC at 30 Hz through a closed-loop using the LSA-PIVC.

show abstract

Section: Volume 7 2019mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

dSPACE Controller-Based Enhanced Piezoelectric Energy Harvesting System Using PI-Lightning Search Algorithm

et al. 2019

View full text Add to dashboard Cite

show abstract

“…This interface allows data to transfer bidirectionally between COA in MATLAB and the simulated circuit in LTspice. Based on this new technique, the bidirectional interfaces performed in a closed loop so that the optimization process will be continued automatically and without the need to open the LTspice file manually [14]. The following steps are considered in design procedure to create the mentioned interaction: 1) Open the simulated model in LTspice by using MATLAB; 2) Set the given initial parameters by COA in LTspice model directly from MATLAB; 3) Access to LTspice by MATLAB and simulate the circuit modeled in LTspice; 4) Read the output results from LTspice [15,16] and calculate the fitness function in MATLAB for COA; 5) Perform COA to set new values of controller parameters in LTspice; 6) Repeat the step 3 in order to obtain the optimal parameters.…”

Section: Chaos Optimization Algorithmmentioning

confidence: 99%

A new approach to control the modified LinVerter for high frequency applications

Farhang

Mátéfi‐Tempfli

2016

2016 IEEE Applied Power Electronics Conference and Exposition (APEC)

Self Cite

View full text Add to dashboard Cite

In this paper not only a modified multi-device LinVerter is proposed but also a novel approach to control this topology is introduced. This topology is able to improve the conventional LinVerter performance using parallel power devices and designing a sequential switching scheme. In addition, from control perspective, a novel control approach based on bidirectional interface between LTspice and MATLAB is created. In this case, the circuit is modeled in LTspice environment and a Chaos Optimization Algorithm (COA) is coded in MATLAB in order to find out the optimal solution in control process. In fact, this new approach combines the advantages of LTspice for simulation of different circuit configurations using actual component obtained from manufactures' models with advanced intelligent techniques capabilities from COA in MATLAB. First, the performance of proposed multi-device LinVerter along with new control technique is evaluated in different conditions, and then effectiveness and robustness of the whole suggested designs are presented.

show abstract

Robust PID Controllers Optimized by PSO Algorithm for Power Converters

Borin

Mattos

Osório

et al. 2019

2019 IEEE 15th Brazilian Power Electronics Conference and 5th IEEE Southern Power Electronics Conference (COBEP/SPEC)

View full text Add to dashboard Cite

This paper provides an automated design to provide robust PIDs with fixed control gains, suitable to be applied in power converters whose parameters belong to real intervals. Differently from conventional PIDs which use only a nominal model to obtain the fixed control gains and, a posteriori, verify robustness, the proposed approach ensures, a priori (i.e. during the design stage), robust performance for a set of plant parameters. To illustratethe proposed procedure, two conventional PID controllers are given, to achieve phase margin and crossover frequency for a nominal model of a buck converter. An objective function based on frequency domain specifications is proposed. A particle swarm optimization algorithm is then used to find PIDs, in a large search space that include stable and unstable controllers, allowing to optimize this function for all cases of combinations of plant parameters. A case study for the buck converter illustrates the improvements of performance with the proposed method when compared to the conventional PID controllers. Additionally, the design is used in a more challenging application, for a buck-boost converter suitable for small satellites application, becoming a simple alternative for benchmarks for robust control of power converters.

show abstract

New technique for voltage tracking control of a boost converter based on the PSO algorithm and LTspice

Cited by 5 publications

References 12 publications

dSPACE Controller-Based Enhanced Piezoelectric Energy Harvesting System Using PI-Lightning Search Algorithm

dSPACE Controller-Based Enhanced Piezoelectric Energy Harvesting System Using PI-Lightning Search Algorithm

A new approach to control the modified LinVerter for high frequency applications

Robust PID Controllers Optimized by PSO Algorithm for Power Converters

Contact Info

Product

Resources

About