A Novel Design Technique for Brushless DC Motor in Wireless Medical Applications

The regulation and stabilization of a system's output to achieve desired performance and ensure system reliability require the use of different controllers, but selecting appropriate control parameters presents a challenge in ensuring robustness and stability. Proportional–integral–derivative (PID) controllers are popularly used due to their simplicity and effectiveness in addressing these challenges. In this paper, a simple, effective, and efficient, novel graphical technique is proposed to design PID and its variants (PI/PD) controllers, which addresses the challenges in selecting appropriate control parameters. The method involves creating three equispaced vectors for controller parameters false(Kp,Ki,Kdfalse)$(K_{\text{p}}, K_{\text{i}}, K_{\text{d}})$, and obtaining a 3D (2D in PI/PD) Cartesian grid of these vectors. All nodes in the grid provide several possible controllers, and integral time squared error (ITSE) is calculated for each controller from the closed‐loop step response of the system. The obtained ITSE is plotted in a 4D (PID) or 3D (PI/PD) graph, and controller parameters corresponding to the minimum value of ITSE are identified. Furthermore, the proposed graphical technique aids in choosing the lower and upper bounds (LB and UB) if the controller is designed using optimization techniques. The better selection of LB and UB reduces the search space resulting in lesser execution time and fewer iterations. To validate the proposed graphical technique, we designed various controllers for widely‐used brush‐less DC and switch reluctance motors in electric vehicles. Additionally, by choosing the LB and UB with the proposed technique, controllers are also designed using three optimization techniques: particle swarm optimization, black widow optimization algorithm, and honey badger algorithm. The obtained controllers using the graphical technique outperformed the optimization techniques in terms of time and frequency domain specifications, and the proposed selection of lower and upper bounds resulted in improved performance in terms of iterations and execution time.

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“…A comparative analysis is done for these approaches. The transfer functions of the BLDC motor [41] and SR motor [42] are shown below

\begin{equation} G_{\text{BLDC}}(s)=\frac{9.306}{5.997e^{-8}s^2 + 0.0019s + 1} \end{equation}

\begin{equation} G_{\text{SRM}}(s)=\frac{0.1136}{s^2 + 2.034s + 0.1242} \end{equation}

…”

Section: Embodied Examplesmentioning

confidence: 99%

“…A comparative analysis is done for these approaches. The transfer functions of the BLDC motor [41] and SR motor [42] are shown below…”

Section: Embodied Examplesmentioning

confidence: 99%

A graphical technique of controller design and selection of lower and upper bounds in controller design using optimization techniques

Ahamad

Chhetri

Saklani

et al. 2023

IET Control Theory & Appl

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“…The main pitfalls of these strategies are peak overshooting and long settling time. PID controls have recently been found in many systems, including the power grid, computers and control system (Uniyal and Sikander, 2018; Goyal et al , 2019; Bhatt et al , 2019; Agarwal et al , 2018). A comprehensive literature review of LFC can be found (Shayeghi et al , 2008; Panda et al , 2013; Kumar and Sikander, 2021).…”

Section: Introductionmentioning

confidence: 99%

Parameter identification for load frequency control using fuzzy FOPID in power system

Kumar

Sikander

2021

COMPEL

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Purpose This paper aims to suggest the parameter identification of load frequency controller in power system. Design/methodology/approach The suggested control approach is established using fuzzy logic to design a fractional order load frequency controller. A new suitable control law is developed using fuzzy logic, and based on this developed control law, the unknown parameters of the fractional order proportional integral derivative (FOPID) controller are derived using an optimization technique, which is being used by minimizing the integral square error. In addition, to confirm the effectiveness of the proposed control design approach, numerous simulation tests were carried out on an actual single-area power system. Findings The obtained results reveal the superiority of the suggested controller as compared to the recently developed controllers with regard to time response specifications and quantifiable indicators. Additionally, the potential of the suggested controller is also observed by improving the load disturbance rejections under plant parametric uncertainty. Originality/value To the best of the authors’ knowledge, the work is not published anywhere else.

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Smart Infusion Pump Control: The Control System Perspective

Alamelu

Mythili

2021

Health Information Science

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A Novel Design Technique for Brushless DC Motor in Wireless Medical Applications

Cited by 12 publications

References 16 publications

A graphical technique of controller design and selection of lower and upper bounds in controller design using optimization techniques

A graphical technique of controller design and selection of lower and upper bounds in controller design using optimization techniques

Parameter identification for load frequency control using fuzzy FOPID in power system

Smart Infusion Pump Control: The Control System Perspective

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