Adaptive fuzzy dynamic surface control for pneumatic muscle systems with full-state constraints and disturbances

Abstract

In the era of intelligent revolution, pneumatic artificial muscle (PAM) actuators have gained significance in robotics, particularly for tasks demanding high safety and flexibility. Despite their inherent flexibility, PAMs encounter challenges in practical applications because of their complex material properties, including hysteresis, nonlinearity, and low response frequencies, which hinder precise modeling and motion control, limiting their widespread adoption. This study focuses on fuzzy logic dynamic surface control (DSC) for PAMs, addressing full-state constraints and unknown disturbances. We propose an improved neural DSC method, combining enhanced DSC techniques with fuzzy logic system approximation and parameter minimization for PAM systems. The introduction of a novel barrier Lyapunov function during system design effectively resolves full-state constraint issues. A key feature of this control approach is its single online estimation parameter update while maintaining stability characteristics akin to the conventional backstepping method. Importantly, it ensures constraint adherence even in the presence of disturbances. Lyapunov stability analysis confirms signal boundedness within the closed-loop system. Experimental results validate the algorithm’s effectiveness in enhancing control precision and response speed.

Graphical abstract

Keywords

adaptive fuzzy control / tracking control / PAM system / state constraints / input saturation

References

[1] Weihmann T. Leg force interference in polypedal locomotion. Science Advances, 2018, 4(9): eaat3721

[2] Asano Y, Okada K, Inaba M. Design principles of a human mimetic humanoid: Humanoid platform to study human intelligence and internal body system. Science Robotics, 2017, 2(13): eaaq0899

[3] ArelekattiV MWinterA G. Design of a fully passive prosthetic knee mechanism for transfemoral amputees in India. In: 2015 IEEE International Conference on Rehabilitation Robotics (ICORR). IEEE, 2015, 350–356

[4] Dabiri Y, Najarian S, Eslami M R, Zahedi S, Moser D. A powered prosthetic knee joint inspired from musculoskeletal system. Biomedical Engineering, 2013, 33(2): 118–124

[5] CeriC N. Design analysis of the four-bar Jaipur-Stanford prosthetic knee for developing countries. Thesis for the Bachelor Degree. Cambridge: Massachusetts Institute of Technology, 2013

[6] Al-Maliky F T, Chiad J S. Study and evaluation of four bar polycentric knee used in the prosthetic limb for transfemoral amputee during the gait cycle. Materials Today: Proceedings, 2021, 42: 2706–2712

[7] OmirbayevSIssa IKuangaliyevZTurganbayevANiyetkaliyev A. The use of four-bar mechanisms in robotic exoskeletons. In: the 12th International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2022, 149–156

[8] HanSUmS KimS. Mechanical design of robot lower body based on four-bar linkage structure for energy efficient bipedal walking . In: IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE, 2016, 402–407

[9] Sun Y, Tang H, Tang Y, Zheng J, Dong D, Chen X, Liu F, Bai L, Ge W, Xin L, Pu H, Peng Y, Luo J. Review of recent progress in robotic knee prosthesis related techniques: Structure, actuation and control. Journal of Bionics Engineering, 2021, 18(4): 764–785

[10] Mirvakili S M, Leroy A, Sim D, Wang E N. Solar‐driven soft robots. Advanced Science, 2021, 8(8): 2004235

[11] Duan H, Xun S, Bao Y, Zhang G. Computer intelligent algorithm in the recovery of the elbow joint sports injury model. Journal of Healthcare Engineering, 2022, 2022: 5044952

[12] Davidson Z S, Shahsavan H, Aghakhani A, Guo Y, Hines L, Xia Y, Yang S, Sitti M. Monolithic shape-programmable dielectric liquid crystal elastomer actuators. Science Advances, 2019, 5(11): eaay0855

[13] Schaffner M, Faber J A, Pianegonda L, Rühs P A, Coulter F, Studart A R. 3D printing of robotic soft actuators with programmable bioinspired architectures. Nature Communications, 2018, 9(1): 878

[14] Robinson R M, Kothera C S, Sanner R M, Wereley N M. Nonlinear control of robotic manipulators driven by pneumatic artificial muscles. IEEE/ASME Transactions on Mechatronics, 2016, 21(1): 55–68

[15] Huang J, Tu X, He J. Design and evaluation of the RUPERT wearable upper extremity exoskeleton robot for clinical and in-home therapies. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 46(7): 926–935

[16] Vo C P, Ahn K K. An adaptive finite-time force-sensorless tracking control scheme for pneumatic muscle actuators by an optimal force estimation. IEEE Robotics and Automation Letters, 2021, 7(2): 1542–1549

[17] Wu J, Huang J, Wang Y, Xing K. Nonlinear disturbance observer-based dynamic surface control for trajectory tracking of pneumatic muscle system. IEEE Transactions on Control Systems Technology, 2013, 22(2): 440–455

[18] Merola A, Colacino D, Cosentino C, Amato F. Model-based tracking control design, implementation of embedded digital controller and testing of a biomechatronic device for robotic rehabilitation. Mechatronics, 2018, 52: 70–77

[19] Reynolds D, Repperger D, Phillips C, Bandry G. Modeling the dynamic characteristics of pneumatic muscle. Advanced Engineering, 2003, 31: 310–317

[20] Chen C T, Lien W Y, Chen C T, Twu M J, Wu Y C J I A. Dynamic modeling and motion control of a cable-driven robotic exoskeleton with pneumatic artificial muscle actuators. IEEE Access, 2020, 8: 149796–149807

[21] Chen C T, Lien W Y, Chen C T, Wu Y C. Implementation of an upper-limb exoskeleton robot driven by pneumatic muscle actuators for rehabilitation. Actuators, 2020, 9(4): 106

[22] Guan X, He Z, Zhang M, Xia H. Filtering-error constrained angle tracking adaptive learning fuzzy control for pneumatic artificial muscle systems under nonzero initial errors. IEEE Access, 2022, 10: 41828–41838

[23] Ba D X, Dinh T Q, Ahn K K. An integrated intelligent nonlinear control method for a pneumatic artificial muscle. IEEE/ASME Transactions on Mechatronics, 2016, 21(4): 1835–1845

[24] Xing K, Huang J, Wang Y, Wu J, Xu Q, He J. Tracking control of pneumatic artificial muscle actuators based on sliding mode and non-linear disturbance observer. IET Control Theory & Applications, 2010, 4(10): 2058–2070

[25] Nikkhah A, Bradley C, Ahmadian A S. Design, dynamic modeling, control and implementation of hydraulic artificial muscles in an antagonistic pair configuration. Mechanism and Machine Theory, 2020, 153: 104007

[26] Wang T, Chen X, Qin W. A novel adaptive control for reaching movements of an anthropomorphic arm driven by pneumatic artificial muscles. Applied Soft Computing, 2019, 83: 105623

[27] Zhao L, Cheng H, Xia Y, Liu B. Angle tracking adaptive backstepping control for a mechanism of pneumatic muscle actuators via an AESO. IEEE Transactions on Industrial Electronics, 2018, 66(6): 4566–4576

[28] Yang T, Chen Y, Sun N, Liu L, Qin Y, Fang Y. Learning-based error-constrained motion control for pneumatic artificial muscle-actuated exoskeleton robots with hardware experiments. IEEE Transactions on Automation Science and Engineering, 2022, 19(4): 3700–3711

[29] Sun N, Liang D, Wu Y, Chen Y, Qin Y, Fang Y. Adaptive control for pneumatic artificial muscle systems with parametric uncertainties and unidirectional input constraints. IEEE Transactions on Industrial Informatics, 2020, 16(2): 969–979

[30] Qian K, Li Z, Chakrabarty S, Zhang Z, Xie S Q. Robust iterative learning control for pneumatic muscle with uncertainties and state constraints. IEEE Transactions on Industrial Electronics, 2023, 70(2): 1802–1810

[31] Wang D, Huang J. Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Transactions on Neural Networks, 2005, 16(1): 195–202

[32] Song S, Zhang B, Song X, Zhang Z. Neuro-fuzzy-based adaptive dynamic surface control for fractional-order nonlinear strict-feedback systems with input constraint. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(6): 3575–3586

[33] Wu Y C, Chen F W, Liao T T, Chen C T. Force reflection in a pneumatic artificial muscle actuated haptic system. Mechatronics, 2019, 61: 37–48

[34] Guo D, Liu J, Zheng S, Jiang P. Design, analysis and experiment of robust adaptive repetitive angle tracking control for a one-DOF manipulator driven by pneumatic artificial muscles. Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science, 2024, 238(4): 1029–1043

[35] Liu G, Sun N, Liang D, Chen Y, Yang T, Fang Y. Neural network-based adaptive command filtering control for pneumatic artificial muscle robots with input uncertainties. Control Engineering Practice, 2022, 118: 104960

[36] Ai Q, Ke D, Zuo J, Meng W, Liu Q, Zhang Z, Xie S Q. High-order model-free adaptive iterative learning control of pneumatic artificial muscle with enhanced convergence. IEEE Transactions on Industrial Electronics, 2020, 67(11): 9548–9559

[37] Bneakey J W, Marquette S H. Beyond the four-bar knee. Journal of Prosthetics and Orthotics, 1998, 10(3): 77–80

[38] Nuchkrua T, Leephakpreeda T. Fuzzy self-tuning PID control of hydrogen-driven pneumatic artificial muscle actuator. Journal of Bionics Engineering, 2013, 10(3): 329–340

RIGHTS & PERMISSIONS

Higher Education Press