Networked control with guaranteed performance for IoT rehabilitation robot under nonvanishing uncertainties and input quantization^☆

Abstract

The Internet of Things (IoT) technology provides data acquisition, transmission, and analysis to control rehabilitation robots, encompassing sensor data from the robots as well as lidar signals for trajectory planning (desired trajectory). In IoT rehabilitation robot systems, managing nonvanishing uncertainties and input quantization is crucial for precise and reliable control performance. These challenges can cause instability and reduced effectiveness, particularly in adaptive networked control. This paper investigates networked control with guaranteed performance for IoT rehabilitation robots under nonvanishing uncertainties and input quantization. First, input quantization is managed via a quantization-aware control design, ensur stability and minimizing tracking errors, even with discrete control inputs, to avoid chattering. Second, the method handles nonvanishing uncertainties by adjusting control parameters via real-time neural network adaptation, maintaining consistent performance despite persistent disturbances. Third, the control scheme guarantees the desired tracking performance within a specified time, with all signals in the closed-loop system remaining uniformly bounded, offering a robust, reliable solution for IoT rehabilitation robot control. The simulation verifies the benefits and efficacy of the proposed control strategy.

Keywords

Networked control / IoT rehabilitation robot / Guaranteed performance / Nonvanishing uncertainties / Input quantization

References

[1] H. Zhang, D. Xu, A survey on rehabilitation robots and their control strategies, J. Rehabil. Res. Dev. 58 (2) (2021) 45-60.

[2] J. Smith, Y. Liu, Advances in neuroadaptive control for rehabilitation robots, IEEE Trans. Neural Syst. Rehabil. Eng. 28 (11) (2020) 2330-2342.

[3] S. Wang, G. Chen, Robust control techniques for nonlinear systems: a comprehensive review, Nonlinear Dyn. 98 (3) (2019) 1231-1254.

[4] H. Liu, Y. Zhao, Handling uncertainties in robotic systems: theory and applications, Control Eng. Pract. 118 (2022) 105260.

[5] F. Yang, Z. Li, Quantization effects in digital control systems: analysis and mitigation strategies, Automatica 92 (2018) 111-123.

[6] W. Chen, L. Zhang, Adaptive control of nonlinear systems with input quantization, Syst. Control Lett. 143 (2020) 104798.

[7] R. Xu, S. Yang, Performance guarantees in adaptive control: challenges and solutions, IEEE Control Syst. Mag. 41 (5) (2021) 78-92.

[8] R. Brown, J. Clarke, Recent developments in neuroadaptive control for robotics, J. Control Decis. 6 (2) (2019) 134-146.

[9] K. Zhao, Y. Song, Z. Shen, Neuroadaptive fault-tolerant control of nonlinear systems under output constraints and actuation faults, IEEE Trans. Neural Netw. Learn. Syst. 29 (2) (2016) 286-298.

[10] M. Zhang, X. Yang, Guaranteed performance control for robotic systems: an overview, J. Mech. Eng. Sci. 235 (3) (2021) 789-803.

[11] X. Gao, Y. Wang, Quantization error compensation in digital control systems, IEEE Trans. Circuits Syst. I, Regul. Pap. 67 (10) (2020) 3912-3923.

[12] L. Huang, X. Zhang, Precision and stability in rehabilitation robotics: an emerging frontier, J. Robot. Autom. Sci. 7 (1) (2019) 25-34.

[13] Z. Wei, S. Tan, Neuroadaptive tracking control of uncertain nonlinear systems with spatiotemporal constraints, Proc. Rom. Acad., Ser. A: Math. Phys. Tech. Sci. Inf. Sci. 24 (3) (2023).

[14] S. Tan, L. Sun, Y. Song, Prescribed performance control of Euler-Lagrange systems tracking targets with unknown trajectory, Neurocomputing 480 (2022) 212-219.

[15] R. Chen, Q. Wang, Neuroadaptive control for complex nonlinear systems: state of the art and future directions, IEEE Trans. Autom. Control 67 (4) (2022) 2367-2382.

[16] M. Zhao, J. Liu, Handling persistent disturbances in adaptive control systems, J. Process Control 99 (2021) 15-28.

[17] Z. Liu, S. Zheng, Quantization effects in rehabilitation robotics: an experimental study, Robot. Auton. Syst. 115 (2019) 123-136.

[18] X. Sun, J. Huang, Robust control methods for nonlinear systems with persistent dis-turbances, Syst. Theory Appl. 28 (2) (2020) 299-315.

[19] L. Wang, H. Liu, Neuroadaptive control for nonlinear systems: challenges and solu-tions, IEEE Access 9 (2021) 112345-112358.

[20] Y. Zhou, Q. Zhao, Adaptive control for systems with nonvanishing uncertainties: a survey, Control Theory Technol. 20 (1) (2022) 45-60.

[21] Y. Kim, J. Park, Advanced neuroadaptive control for rehabilitation robots: a com-parative study, J. Robot. Res. 38 (7) (2019) 1220-1237.

[22] Z. Yang, J. Liu, Challenges in input quantization for digital control systems, IEEE Trans. Control Syst. Technol. 29 (3) (2021) 1123-1135.

[23] Q. Zhang, W. Xu, Adaptive backstepping control for systems with quantized inputs: a review, Int. J. Adapt. Control Signal Process. 34 (6) (2020) 847-860.

[24] B. Wang, W. Sun, Quantization and control of nonlinear systems: techniques and applications, Math. Rev. 23 (4) (2019) 457-470.

[25] H. Chen, L. Zhao, Impact of quantization on the stability of control systems, J. Eng. Math. 133 (1) (2022) 85-98.

[26] Z. Liu, F. Wang, Y. Zhang, C.P. Chen, Fuzzy adaptive quantized control for a class of stochastic nonlinear uncertain systems, IEEE Trans. Cybern. 46 (2) (2015) 524-534.

[27] S. Lee, Q. Wu, Mitigating quantization effects in digital control systems: a novel approach, J. Comput. Appl. Math. 374 (2020) 112-126.

[28] X. Zhang, R. Wu, Hysteretic quantizers in control systems: theory and practice, J. Syst. Eng. Electron. 43 (5) (2021) 1042-1056.

[29] Y. Xu, J. Chen, Integrating input quantization and neuroadaptive control for en-hanced system performance, Control Eng. Pract. 86 (2019) 129-140.

[30] Z. Wen, X. Bie, S. Tan, Neuroadaptive fixed-time tracking control of full-state con-strained strict-feedback nonlinear systems with actuator faults, Eng. Lett. 32 (3)(2024).

[31] P. Krishnamurthy, F. Khorrami, M. Krstic, A dynamic high-gain design for prescribed-time regulation of nonlinear systems, Automatica 115 (2020) 108860.

[32] K. Zhao, F.L. Lewis, L. Zhao, Unifying performance specifications in tracking control of MIMO nonlinear systems with actuation faults, Automatica 155 (2023) 111102.

[33] H. Ye, Y. Song, Prescribed-time tracking control of MIMO nonlinear systems with nonvanishing uncertainties, IEEE Trans. Autom. Control 68 (6) (2022) 3664-3671.

[34] R. Gao, H. Wang, L. Zhao, K. Zhao, Exponential tracking control of MIMO nonlin-ear systems with actuator failures and nonparametric uncertainties, Int. J. Robust Nonlinear Control 32 (3) (2022) 1604-1617.

[35] B. Huang, F. Zhang, M. Song, P. Huang, Event-based predefined-time deployment control for space triangular tethered satellite system with input quantization, IEEE Trans. Aerosp. Electron. Syst. 59 (5) (2023) 4936-4946.

[36] Z. Zhang, C. Wen, L. Xing, Y. Song, Adaptive output feedback control of nonlin-ear systems with mismatched uncertainties under input/output quantization, IEEE Trans. Autom. Control 67 (9) (2022) 4801-4808.

[37] K. Zhao, Y. Song, T. Ma, L. He, Prescribed performance control of uncertain Euler-Lagrange systems subject to full-state constraints, IEEE Trans. Neural Netw. Learn. Syst. 29 (8) (2018) 3478-3489.

[38] J. Zhou, C. Wen, G. Yang, Adaptive backstepping stabilization of nonlinear uncer-tain systems with quantized input signal, IEEE Trans. Autom. Control 59 (2) (2013) 460-464.

[39] K. Zhao, F.L. Lewis, L. Zhao, Unifying performance specifications in tracking control of MIMO nonlinear systems with actuation faults, Automatica 155 (2023) 111102.