Development and Optimization of PID Control for FES Knee Exercise in Hemiplegic Rehabilitation

Boudville, R.; Hussain, Z.; Yahaya, Saiful Zaimy; Rahman, Mohamad Faizal Abd; Ahmad, K. A.; Husin, Nurul Izza

doi:10.1109/icsenst.2018.8603628

Cited by 9 publications

(8 citation statements)

References 15 publications

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“…error e(t) P I The PID controller has been widely used in FES control studies as a benchmark comparison to other control algorithms (Boudville et al, 2018;Qiu et al, 2014;Wannawas et al, 2022). Here, we use heuristics to hand tune the PID parameters one at a time, particularly focusing on the response to a single pulse.…”

Section: Pid Controllermentioning

confidence: 99%

“…The PID feedback controller was chosen as a comparator for consistency with other papers on advanced FES control. Although PID controllers have generally been shown to under perform in comparison to other more intelligent and adaptive control techniques (Boudville et al, 2018;Qiu et al, 2014;Wannawas, Shafti, & Faisal, 2022), it is common practice to compare developed FES control algorithms to the basic PID controller as a benchmark. There are numerous other control algorithms that have been applied to FES control, making the choice of controller to use in comparisons difficult and somewhat arbitrary, so we choose to use a PID controller for comparison, as is standard.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs

Wilson

2023

Neural Computation

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Prior applications of the cerebellar adaptive filter model have included a range of tasks within simulated and robotic systems. However, this has been limited to systems driven by continuous signals. Here, the adaptive filter model of the cerebellum is applied to the control of a system driven by spiking inputs by considering the problem of controlling muscle force. The performance of the standard adaptive filter algorithm is compared with the algorithm with a modified learning rule that minimizes inputs and a simple proportional-integral-derivative (PID) controller. Control performance is evaluated in terms of the number of spikes, the accuracy of spike input locations, and the accuracy of muscle force output. Results show that the cerebellar adaptive filter model can be applied without change to the control of systems driven by spiking inputs. The cerebellar algorithm results in good agreement between input spikes and force outputs and significantly improves on a PID controller. Input minimization can be used to reduce the number of spike inputs, but at the expense of a decrease in accuracy of spike input location and force output. This work extends the applications of the cerebellar algorithm and demonstrates the potential of the adaptive filter model to be used to improve functional electrical stimulation muscle control.

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Section: Pid Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs

Wilson

2023

Neural Computation

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“…An accurate simulation model incorporating the non-linear effects of muscles is also necessary to guarantee the performance of the controller [8]. Several feedback controllers, such as proportional-integral-derivative (PID) [9], fuzzy logic [10], neural network [11] and adaptive controllers [3], have been proposed. However, the proposed feedback control system demonstrates a decline in control performance in the presence of non-linearities.…”

Section: Introductionmentioning

confidence: 99%

Pole placement tuning of proportional integral derivative feedback controller for knee extension model

Arof,

Noorsal,

Yahaya

et al. 2024

IJEECS

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Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle non-linear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization.

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“…Therefore, applying a pre-specified stimulation pattern in openloop FES systems cannot obtain the proper performance. To solve the problem, researches have proposed many control strategies of single-joint movement (eg, ankle, or knee joints) (3)(4)(5)(6)(7)(8)(9) and multi-joint motion (eg, cycling, standing, or walking) (10)(11)(12)(13). The proposed methods included adaptive feedback control (14), fuzzy control (5,15), PID control (4,16), neural control (17), sliding mode control (5,6,18), gain scheduling (8), nonlinear model predictive control (9,19,20), and so on.…”

Section: Introductionmentioning

confidence: 99%

Control of Functional Electrical Stimulation Systems Using Simultaneous Pulse Width, Amplitude, and Frequency Modulations

Nekoukar¹

2021

Neuromodulation: Technology at the Neural Interface

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Development and Optimization of PID Control for FES Knee Exercise in Hemiplegic Rehabilitation

Cited by 9 publications

References 15 publications

Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs

Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs

Pole placement tuning of proportional integral derivative feedback controller for knee extension model

Control of Functional Electrical Stimulation Systems Using Simultaneous Pulse Width, Amplitude, and Frequency Modulations

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