Comparison of SMC and PID Controllers for Pneumatically Powered Knee Orthosis

Ashmi, M.; Anila, M.; Sivanandan, K. S.

doi:10.1007/s40313-021-00775-0

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…Integrating a PID together with other industrial automation control such as Programmable Logic Control (PLC), SCADA and a few others is a proven workable solution [11][12]. Nonlinear controllers for servo pneumatic systems usually adapt sliding mode controllers (SMC) [13][14][15], as well as a few other controllers such as model reference adaptive controller (MRAC) [16], fuzzy logic controllers (FLC) [17] and neural network (NN) controller [18]. Nonlinear controllers are commonly complex and are less applied in the industrial field.…”

Section: Introductionmentioning

confidence: 99%

Triple Nonlinear Hyperbolic Pid With Static Friction Compensation for Precise Positioning of a Servo Pneumatic Actuator

Kamaludin¹,

Abdullah²,

Salim³

et al. 2023

IIUMEJ

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Accurate and precise positioning control is critical in designing a positioning servo pneumatic system. The internal friction force of the pneumatic is one of the disturbances that make it challenging to achieve accurate and precise positioning. Dynamic friction identification and modelling are usually very complex and computationally exhaustive. In addition, pneumatic actuators are nonlinear systems, and applying linear control to the system is a mismatch. This study proposes an enhanced triple nonlinear hyperbolic PID controller with static friction (T-NPID+FSS) feedback module. T-NPID is integrated with nonlinear hyperbolic functions at each PID gain, hence the name. The reference in designing the T-NPID is the Popov stability criterion. Meanwhile, static friction (comparatively more straightforward than dynamic friction) is identified by measuring the actuator's internal friction at various velocities and applying it to the static friction model. T-NPID+FSS is compared to a classical PID, a PID with static friction (PID+FSS), and T-NPID without the friction module. With the comparisons, the performance gains of each module are clear. While most previous research focuses on the sinusoidal wave tracking performance (measuring the maximum tracking error, MTE, and root mean square error, RMSE), the analysis in this research focuses on obtaining precise positioning; steady-state analysis is the primary measurement. However, transient response and integral of absolute error (IAE) analysis are also observed to ensure no significant drawback in the controller's performance. T-NPID+FSS achieved the best precise positioning control, with 88.46% improvement over PID, 71.15% over PID+FSS, and 59.46% over T-NPID. The final controller is also on par with T-NPID for transient responses compared to the base PID. Although the FSS model caters to friction compensation, optimizing the FSS parameter by applying artificial intelligence, such as Neural Networks (NN) and Genetic Algorithm (GA), will increase the friction modeling‘s accuracy, and improve the compensation. ABSTRAK: Kawalan kedudukan yang tepat dan jitu adalah kitikal dalam mereka bentuk sistem pneumatik servo penentududukan. Daya geseran dalaman pneumatik adalah salah satu gangguan yang menyukarkan untuk mencapai kedudukan yang tepat dan jitu. Penentuan daya geseran dinamik dan pemodelannya selalunya kompleks dan pengiraan menyeluruh yang sukar. Selain itu, pneumatik ialah sistem tak linear, menggunakan kawalan linear pada sistem adalah tidak padan. Kajian ini mencadangkan PID hiperbolik tiga fungsi tak linear yang dipertingkatkan dengan modul suapan-balik geseran statik (T-NPID+FSS). T-NPID diintegrasikan dengan tiga fungsi hiperbolik tidak linear pada setiap pendarab PID, member pada nama. T-NPID direka bentuk dengan kriteria kestabilan Popov. Manakala geseran statik (secara perbandingan lebih mudah daripada geseran dinamik) dikenal pasti dengan mengukur geseran dalaman penggerak pada pelbagai halaju dan menerapkannya pada model geseran statik. T-NPID+FSS dibandingkan dengan PID klasik, PID dengan geseran statik (PID+ FSS) dan T-NPID tanpa modul geseran. Dengan perbandingan, prestasi peningkatan setiap modul adalah jelas. Walaupun kebanyakan penyelidikan terdahulu memfokuskan pada prestasi penjejakan gelombang sinusoidal (mengukur ralat penjejakan maksimum, MTE dan ralat purata kuasa dua akar, RMSE), analisis kajian ini memberi tumpuan kepada mendapatkan kedudukan yang tepat; oleh itu, analisis keadaan akhir ialah ukuran utama. Walau bagaimanapun, tindak balas sementara dan analisis kamiran ralat mutlak (IAE) juga diperhatikan untuk memastikan tiada kelemahan ketara dalam prestasi pengawal. T-NPID+FSS mencapai kawalan penentududukan tepat terbaik, dengan peningkatan 88.46% berbanding PID, 71.15% berbanding PID+FSS dan 59.26% berbanding T-NPID. Pengawal yang dicadangkan juga setanding dengan T-NPID untuk respons sementara berbanding PID asas. Walaupun model FSS telah ditunjukkan untuk memenuhi pampasan geseran, mengoptimumkan parameter FSS dengan menggunakan kecerdasan buatan (artificial intelligence, AI) seperti Neural Networks, NN dan Genetic Algorithms, GA akan meningkatkan ketepatan dan pampasan pemodelan geseran.

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