Model-free adaptive PID control for nonlinear discrete-time systems

Zhang, Shuhua; Chi, Ronghu

doi:10.1177/0142331219896649

Cited by 14 publications

(7 citation statements)

References 23 publications

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“…Data-driven control is a modeling control method developed in recent years, which can build a nonmechanical model of the system based on the input and output information of the system [10]. Model-free PID control strategies is utilized by early researchers, but their control parameters count mainly on empirical methods of trial and error, leading to poor system anti-disturbance capability [12]. With the continuous advancement of control theory, methods such as ILC, SMC, adaptive control and impedance control have attracted great attention [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Impedance Iterative Learning Sliding Mode Control for Robot-Assisted Bathing with Non-repetitive Trajectory

Guo

zhang

et al. 2022

Preprint

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In this paper, an impedance iterative learning sliding mode control (IILSMC) scheme for robot-assisted bathing with unknown model parameters is investigated. Firstly, impedance control is not only applied to adjust the desired trajectory, but also implemented to ensure the active compliance control of the robot-assisted bathing. Furthermore, iterative learning control is designed to dynamically estimate the unknown model parameters. To release the identical initial condition of iterative learning control (ILC), the trajectory reconstruction method is proposed, such that the convergence of tracking errors can be guaranteed with random initial status. Moreover, adaptive sliding mode control (SMC) is proposed where non-parametric, external disturbances and the human-machine interaction (HMI) torque generated during the bathing process is suppressed by adaptive method. Based on the composite energy function (CEF) method, the convergence of the double closed-loop system in the time domain and iterative domain is proved. Finally, through co-simulation of MATLAB/Simulink and ADAMS, the effectiveness and superiority of the control strategy proposed in this paper is jointly verified.

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Section: Introductionmentioning

confidence: 99%

Impedance Iterative Learning Sliding Mode Control for Robot-Assisted Bathing with Non-repetitive Trajectory

Guo

zhang

et al. 2022

Preprint

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“…However, with its fixed parameter, the PID controller does not work well for nonlinear or delay systems (Segovia et al, 2004) . To solve the problem of the PID controller, adaptive methodologies were applied (Kayacan and Kaynak, 2009;Klopot et al, 2014;Latip et al, 2019;Mahmoodabadi et al, 2015;Zhang and Chi, 2020). And a PID neural network (PIDNN) was proposed, which automatically identifies the system parameters and adjusts them by the system changes (Cong and Liang, 2009;Kang et al, 2014;Madhuranthakam et al, 2010;Tan and Cauwenberghe, 1999).…”

Section: Introductionmentioning

confidence: 99%

Multi-dimensional Taylor network adaptive predictive control for single-input single-output nonlinear systems with input time-delay

Yan

Zhang

2021

Transactions of the Institute of Measurement and Control

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In this study, an adaptive predictive control approach based on the multi-dimensional Taylor network (MTN) is proposed for the real-time tracking control of single-input single-output nonlinear systems with input time-delay. Two MTNs are used to implement the accurate tracking control. First, to compensate for the influence of time-delay, MTN is taken as a predictor and the damped recursive least squares algorithm is used as its online learning algorithm. Second, a feed-forward MTN controller is developed on the basis of the proportional–integral–derivative controller, and the closed-loop errors between the reference input and the system output are directly chosen to be the MTN controller’s inputs. The back propagation algorithm is introduced for its learning which can update its weights online at stable learning rate by the errors caused by the system’s uncertain factors. Convergence and stability analysis are given to guarantee the performance of our proposed approach. Finally, two examples are given to verify the effectiveness of the proposed approach.

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“…In recent years, researchers have proposed some control methods for vehicle lateral stability. These control methods include robust control method (Liu and Dong, 2020), PID control method (Haroun and Li, 2020; Li et al, 2016; Zhang and Chi, 2020), sliding mode control method (Ding et al, 2017, 2018; Gu and Xu, 2020; Guo et al, 2020), finite-time control method (Feng et al, 2020; Sun et al, 2017a; Wang et al, 2020), and adaptive disturbance attenuation method (Sun and Wang, 2019; Sun et al, 2017b). However, these control methods are mainly studied in continuous-time.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear lateral motion stability control method for electric vehicle based on the combination of dual extended state observer and domination approach via sampled-data output feedback

Meng

Sun

2021

Transactions of the Institute of Measurement and Control

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This paper studies lateral motion stability control method for an electric vehicle (EV) with uncertain disturbances. Aiming to suppress the influence of uncertain disturbances, a novel method based on the combination of dual extended state observer (dESO) and domination approach is proposed. The proposed method enables the designed controller just to adopt a smaller scaling gain than the classic domination method, which is more practical for the controller in engineering. Firstly, a dESO is proposed to estimate the uncertain disturbances of the system. Also, a sampled-data output feedback domination approach is designed to dominate the disturbances by using a scaling gain. Then, the sampled-data output feedback control law is constructed based on the dESO and domination approach called dESOD. Furthermore, the stability analysis is presented to show that the proposed sampled-data controller can guarantee the closed-loop system globally asymptotically stable. Finally, the simulations and experiment results show the effectiveness of the proposed method.

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Model-free adaptive PID control for nonlinear discrete-time systems

Cited by 14 publications

References 23 publications

Impedance Iterative Learning Sliding Mode Control for Robot-Assisted Bathing with Non-repetitive Trajectory

Impedance Iterative Learning Sliding Mode Control for Robot-Assisted Bathing with Non-repetitive Trajectory

Multi-dimensional Taylor network adaptive predictive control for single-input single-output nonlinear systems with input time-delay

Nonlinear lateral motion stability control method for electric vehicle based on the combination of dual extended state observer and domination approach via sampled-data output feedback

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