scholarly journals Nonlinear Controller for the Set-Point Regulation of a Buck Converter System

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5760
Author(s):  
Eduardo Campos-Mercado ◽  
Edwin Fernando Mendoza-Santos ◽  
Jorge Antonio Torres-Muñoz ◽  
Edwin Román-Hernández ◽  
Víctor Iván Moreno-Oliva ◽  
...  
Keyword(s):  
Embedded System ◽  
Pid Controller ◽  
Variable Parameter ◽  
Input Voltage ◽  
Buck Converter ◽  
Control Input ◽  
Nonlinear Controller ◽  
Integral Control ◽  
Operation Conditions ◽  
Nonlinear Pid Controller

In this paper, we present a nonlinear PID controller based on saturation functions with variable parameters in order to regulate the output voltage of a buck converter in the presence of changes in the input voltage. The main feature of the proposed controller is to bound the control input with a variable parameter to avoid the windup effect generated by the combination of the integral control action and some operation conditions. The main advantages of the proposed nonlinear PID controller are its low computing cost and the simple tuning task to implement the control strategy in an embedded system. The acceptable behavior of the closed-loop system is presented through the simulation and experimental results.

scholarly journals Bat Algorithm Based an Adaptive PID Controller Design for Buck Converter Model

2020 ◽  
Vol 26 (7) ◽  
pp. 62-82
Author(s):  
Luay Thamir Rasheed
Keyword(s):  
Control System ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Tracking Error ◽  
Bat Algorithm ◽  
Input Voltage ◽  
Buck Converter ◽  
Control Action ◽  
On Line ◽  
Simulation Results

The aim of this paper is to design a PID controller based on an on-line tuning bat optimization algorithm for the step-down DC/DC buck converter system which is used in the battery operation of the mobile applications. In this paper, the bat optimization algorithm has been utilized to obtain the optimal parameters of the PID controller as a simple and fast on-line tuning technique to get the best control action for the system. The simulation results using (Matlab Package) show the robustness and the effectiveness of the proposed control system in terms of obtaining a suitable voltage control action as a smooth and unsaturated state of the buck converter input voltage of ( ) volt that will stabilize the buck converter system performance. The simulation results show also that the proposed control system when compared with the other controllers results has the capability of minimizing the rising time to (  sec) and the settling time to (  sec) in the transient response and minimizing the voltage tracking error of the system output to ( ) volt at the steady state response. Furthermore, the number of fitness evaluations is decreased.

An Adaptive Robust Controller for Hydraulic Robotic Manipulators with a Flow-Mapping Compensator

2021 ◽  
Author(s):  
Fu Zhang ◽  
Junhui Zhang ◽  
Bing Xu ◽  
Huaizhi Zong
Keyword(s):  
Input Voltage ◽  
Load Flow ◽  
Nonlinear Flow ◽  
Control Input ◽  
Nonlinear Controller ◽  
Flow Mapping ◽  
Robust Controller ◽  
Control Valves ◽  
Directional Control ◽  
Hydraulic Manipulator

Proportional directional control valves have flexible control functions for the control of various hydraulic manipulators. It is foreseeable that the application of proportional directional control valves will be further expanded. However, due to its own structure, its important parameter, flow gain, is complex, and it has a complex functional relationship with valve opening and temperature. The variable flow gain reduces the performance of a strictly derived nonlinear controller. Therefore, it is necessary to consider the nonlinearity of flow gain in the controller design. In order to solve the above problems, this paper proposes an adaptive robust controller for a hydraulic manipulator with a flow-mapping compensator, which takes into account the nonlinear flow gain and improves the performance of the nonlinear controller. First, we established an adaptive robust controller of the hydraulic manipulator to obtain the load flow of the control input valve. Then, the function of flow gain, input voltage, and temperature are calibrated offline using cubic polynomial, and the flow-mapping compensator is obtained. Finally, we calculate the input voltage based on the flow-mapping compensator and load flow. The flow-mapping compensator further reduces the uncertainty of the model and improves the robustness of the system. By using the proposed controller, the control accuracy of the hydraulic manipulator is significantly improved.

Nonlinear Controller: Voltage Controlled PFC-Based Fuzzy MDPSM Controller with Predictive Input Voltage

2020 ◽  
Vol 29 (13) ◽  
pp. 2050207
Author(s):  
R. Thangam ◽  
S. P. Joy Vasantha Rani
Keyword(s):  
Power Factor ◽  
Fuzzy Rule ◽  
Input Voltage ◽  
Buck Converter ◽  
Variable Load ◽  
Nonlinear Controller ◽  
Nonlinear Loads ◽  
Rule Based ◽  
Resistive Loads ◽  
Power Factor Improvement

In this paper, the design of a fuzzy rule-based MDPSM controlled buck converter is analyzed. Power factor improvement and harmonic minimization for the buck converter connected through the variable load with a fuzzy rule are discussed and simulated. The MDPSM controlled converter is supplied with 230[Formula: see text]V and reaches 15[Formula: see text]V as output. The converter output, always connected with nonlinear loads, causes less power factor with more harmonics and gives less power quality. Active PFC with a fuzzy-based voltage controlled power factor controller is designed to reduce total harmonics and to raise the power factor value equal to unity. The fuzzy-based MDPSM controller was designed using MATLAB Simulink. Controller output waveforms are examined and analyzed with other controller performances. The converter is rated with 2[Formula: see text]mA, 0.5[Formula: see text]mH and 212[Formula: see text][Formula: see text]F values with output power 48[Formula: see text]W. The converter is tested for different resistive loads and inductive loads.

A Nonlinear Controller for PMLSM Control System Based on Tracking Differentiator

2011 ◽  
Vol 383-390 ◽  
pp. 5796-5802 ◽  
Author(s):  
Guang Ya Yan ◽  
Wu Ai ◽  
Bing Chen
Keyword(s):  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
Low Noise ◽  
Nonlinear Controller ◽  
Tracking Differentiator ◽  
Nonlinear Pid Controller ◽  
Motor Control System ◽  
Noise Amplification ◽  
And Control

This paper introduces tracking differentiator based on the classical PID control in order to arrange the transitional process according to the set point and control system parameters, solving the contradictions between speed and overshoot of the manipulated variable in the traditional PID control. At the same time, tracking differentiator provide an effective method of calculating differential with low noise amplification. Nonlinear PID controller adopts a nonlinear combination of error, error differential and error integral to calculate the controlled variable in a very effective way. Applying this kind of nonlinear PID controller to the permanent magnet linear synchronous motor control system , the experimental results show that PMLSM can follow the speed command quickly in no overshoot, and has strong robustness, good static and dynamic characteristics.

scholarly journals Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3385
Author(s):  
Erickson Puchta ◽  
Priscilla Bassetto ◽  
Lucas Biuk ◽  
Marco Itaborahy Filho ◽  
Attilio Converti ◽  
...  
Keyword(s):  
Pid Controller ◽  
Optimization Algorithms ◽  
Optimization Methods ◽  
Buck Converter ◽  
Algebraic Solution ◽  
Nonlinear Load ◽  
Bee Colony ◽  
Arduous Task ◽  
Whale Optimization

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

scholarly journals Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm

2021 ◽  
Vol 4 (3) ◽  
pp. 50
Author(s):  
Preeti Warrier ◽  
Pritesh Shah
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Power Converters ◽  
Buck Converter ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Computational Time ◽  
Optimization Approach ◽  
Response Characteristics ◽  
Fractional Order Controller

The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.

COMPLEX DYNAMICS AND FAST-SLOW SCALE INSTABILITY IN CURRENT-MODE CONTROLLED BUCK CONVERTER WITH CONSTANT CURRENT LOAD

2013 ◽  
Vol 23 (04) ◽  
pp. 1350062 ◽  
Author(s):  
GUOHUA ZHOU ◽  
BOCHENG BAO ◽  
JIANPING XU
Keyword(s):  
Complex Dynamics ◽  
Period Doubling ◽  
Current Mode ◽  
Input Voltage ◽  
Second Order ◽  
Buck Converter ◽  
Constant Current ◽  
Current Load ◽  
Time Model ◽  
Conduction Mode

The complex dynamics and coexisting fast-slow scale instability in current-mode controlled buck converter with constant current load (CCL), operating in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM), are investigated in this paper. Via cycle-by-cycle computer simulation and experimental measurement of current-mode controlled buck converter with CCL, it is found that a unique fast-slow scale instability exists in the second-order switching converter. It is also found that a unique period-doubling accompanied by Neimark–Sacker bifurcation exists in this simple second-order converter, which is different from period-doubling or Neimark–Sacker bifurcations reported previously. Based on a nonlinear discrete-time model and the corresponding Jacobian, the effects of CCL and input voltage on the dynamics of current-mode controlled buck converter are investigated and verified theoretically. Fixed point analysis for slow-scale low-frequency oscillation is also given to verify the dynamics and the coexisting fast-slow scale instability.

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