Design of Robust PID Controller Using PSO-Based Automated QFT for Nonminimum Phase Boost Converter

Kobaku, Tarakanath; Poola, Rajesh; Agarwal, Vivek

doi:10.1109/tcsii.2022.3182045

Cited by 14 publications

(3 citation statements)

References 16 publications

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“…Since then, several algorithms have been studied/proposed, among which Particle Swarm Optimization (PSO) and Differential Evolution (DE) schemes have received systematic attention from the community. For instance, within the class of swarm-inspired algorithm, [23] used PSO for PID optimization in hypersonic vehicle control, [24] used Quantitative Feedback Theory (QFT) and PSO for a DC-DC converter, [25] used Kriging surrogates and PSO for fractional order PID in a production-inventory control system, [26] used PSO in magnetic-levitation control, [27] approached the electro-hydraulic servo control system, [28] combined GA and PSO for Gaussian adaptive PID control of a DC-DC converter, [29] modified the inertia weight of PSO as a piecewise nonlinear function to consider the effects of PID parameters on control response, [30] used a fractional order PSO in which the velocity term implements a non-integer order equation to smooth the transition and exploration of the search space.…”

Section: Related Workmentioning

confidence: 99%

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

Parque,

Khalifa

2023

IEEE Access

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Proportional-Integral-Derivative (PID) is a simple and intuitive feedback-based control mechanism being useful to track set points and to reject disturbances. A key question in gradient-free optimization is to ascertain whether the class of optimization algorithms based on the difference of vectors generalize reasonably well to tackle a large class of PID control problems. For generalization and practical purposes, it would be desirable to render algorithms being able to tune PID controllers over a diverse and large set of control problems/tasks with minimal human intervention (self-adaptation features), and under tight computational budgets. In this paper, aiming to fill the above-mentioned gap, we propose and investigate the effectiveness of a new class of algorithm based on the difference of vectors and self-adaptation mechanisms for PID tuning. As such, we introduce a new class of Differential Evolution with successbased Particle Adaptations (DEPA), which unifies the principles of difference of vectors, particle schemes and trial/parameter adaptation through archive (memory) mechanisms. Our computational simulations using a large/relevant set of 25 control problem instances (tracking of linear, nonlinear, continuous, and discontinuous trajectories in motor position control, motor velocity control, magnetic levitation, inverted pendulum, crane stabilization), and the comparisons with a large set of closely related optimization algorithms, and their extended adaptive variants (23 optimization algorithms in total) has shown the outperforming benefits of the proposed approach in convergence performance under tight function evaluation budgets (1000 function evaluations). Also, the experiments on a real-world inverted pendulum device show the potential for transferability of the learned gains to unseen situations during training. Furthermore, we evaluated the algorithmic extension and the generalization towards diverse fitness landscapes in the CEC 2017 benchmark suite, showing the attractive/outperforming performance overall problem instances. In particular, the proposed framework performs better in 358 control instances compared to other algorithms for PID control tasks, and the algorithmic extension for general optimization landscapes performs better than the related algorithms in 182, 215 and 235 problem instances of CEC 2017 benchmark suite for 10, 30 and 50 dimensions, respectively. Our obtained results have the potential to further advance towards developing efficient and self-adaptive optimization algorithms based on the difference of vectors, which may find use in a wider set of optimization and control problems.

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Section: Related Workmentioning

confidence: 99%

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

Parque,

Khalifa

2023

IEEE Access

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“…Zhang et al [12] designed a hybrid control strategy for particle swarm sliding-mode fuzzy PID control to weaken the chattering of the sliding-mode control using particle swarm optimization. To solve the problem of tuning PID parameters, Kobaku et al [13] proposed a design method that uses quantitative feedback theory in conjunction with particle swarm optimization to perform automatic loop shaping. Mahmoodabadi and Nejadkourki [14] proposed an optimal fuzzy adaptive robust proportional-integral-derivative controller for a quarter-car model with an active suspension system based on integral sliding surfaces defined by control errors.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Adaptive Steady-State-Integral-Derivative Controller Using Recursive Least Squares With Performance Conditions

Lee¹,

2023

IEEE Access

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This paper presents a data-driven adaptive steady state-integral-derivative (SS-ID) control algorithm that uses gradient descent and recursive least squares (RLS) with a forgetting factor. A simplified first-order differential equation of the control system was designed and its parameters were estimated in real-time using the RLS algorithm. The steady-state control input for target-state tracking was derived based on the estimated parameters and steady-state performance conditions. The gradient of the integrated control error to the gain was estimated based on the least-squares method, using the saved past error and gain data in a finite sliding window to determine the control input. The integral gain was adapted based on the gradient descent method, using the estimated gradient, integrated error, and adaptation rate. Simplified control error dynamics were designed, and their parameter was estimated using the RLS algorithm. The derivative control gain can be adapted in real time using the estimated parameters from the simplified control error dynamics and time constant-based performance conditions. The proposed controller was designed in the MATLAB/Simulink environment. A performance evaluation was conducted under various scenarios using a DC motor simulation model and an actual test platform equipped with an optical encoder.

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“…In Sable et al, 3 Li et al, 11 and Maksimovic and Zane, 12 leading‐edge carrier signal and, in Yousefzadeh et al 4 and Hariharan et al, 14 the OFF‐time sampling instant control are employed to get minimum‐phase response. In Hariharan et al, 14 Kobaku et al, 15 Pandey et al, 16 and Paduvalli et al, 17 various control techniques such as constant OFF‐time control, PSO based automated QFT, and sliding mode control with observer are employed. Further, in Poorali and Adib, 18 Zhang et al, 19 Liu and Zhang, 20 Hung and Tseng, 21 the topological structure of the conventional boost converter is modified to mitigate its non‐minimum phase nature.…”

Section: Introductionmentioning

confidence: 99%

Improved dynamic performance with discrete sampling in digital ACM controlled DC‐DC boost converter with capacitor ESR inclusion

Balapanuru

Lokhande

Aware

2022

Circuit Theory & Apps

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A classical symmetrical carrier-based digital Average Current Mode (ACM) control has two sampling choices, peak point and valley point, to control the average inductor current. If it is employed to regulate the DC-DC boost converter, there will be a difference in the dynamic performance at peak and valley point sampling instants because of discontinuous output voltage ripple due to Equivalent Series Resistance (ESR) of the output capacitor. Therefore, this paper investigates the effect of sampling instant on the dynamic performance of the PWM DC-DC boost converter in consideration of capacitor ESR. The small-signal transfer functions of a boost converter for both sampling positions are prominent to analyze each sampling impact on its dynamics. They are derived by using discrete-time modeling. Further, the choices of sampling instant are investigated related to the RHP zero elimination, voltage stability, and peak-to-peak ripple current of the DC-DC boost converter. The boost converter has improved stability performance by synchronizing the ADC sampling at starting position (valley point) of the symmetrical carrier signal, as evident by the analytical and hardware results of both sampling techniques working from 25% to 100% load working conditions.

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Design of Robust PID Controller Using PSO-Based Automated QFT for Nonminimum Phase Boost Converter

Cited by 14 publications

References 16 publications

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

PID Tuning Using Differential Evolution With Success-Based Particle Adaptations

Data-Driven Adaptive Steady-State-Integral-Derivative Controller Using Recursive Least Squares With Performance Conditions

Improved dynamic performance with discrete sampling in digital ACM controlled DC‐DC boost converter with capacitor ESR inclusion

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