Data-driven gradient-based point-to-point iterative learning control for nonlinear systems

Huo, Benyan; Freeman, Christopher; Liu, Yanghong

doi:10.1007/s11071-020-05941-8

Cited by 15 publications

(3 citation statements)

References 43 publications

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“…Second, the proposed method requires a feasible reference trajectory which might not be directly available in some applications. If the reference is only specified at a subset of the trial’s samples, the proposed method might be extended by well-established P2P-ILC concepts, see ( Freeman and Tan, 2013 ; Janssens et al, 2013 ; Huo et al, 2020 ). And in cases in which the motion task is formulated only via goal states and constraints, a prior planning step might be required.…”

Section: Discussionmentioning

confidence: 99%

Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear Dynamics

2022

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This work addresses the problem of reference tracking in autonomously learning robots with unknown, nonlinear dynamics. Existing solutions require model information or extensive parameter tuning, and have rarely been validated in real-world experiments. We propose a learning control scheme that learns to approximate the unknown dynamics by a Gaussian Process (GP), which is used to optimize and apply a feedforward control input on each trial. Unlike existing approaches, the proposed method neither requires knowledge of the system states and their dynamics nor knowledge of an effective feedback control structure. All algorithm parameters are chosen automatically, i.e. the learning method works plug and play. The proposed method is validated in extensive simulations and real-world experiments. In contrast to most existing work, we study learning dynamics for more than one motion task as well as the robustness of performance across a large range of learning parameters. The method’s plug and play applicability is demonstrated by experiments with a balancing robot, in which the proposed method rapidly learns to track the desired output. Due to its model-agnostic and plug and play properties, the proposed method is expected to have high potential for application to a large class of reference tracking problems in systems with unknown, nonlinear dynamics.

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Section: Discussionmentioning

confidence: 99%

Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear Dynamics

2022

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“…However, this condition of conventional ILC is too strict to meet the design requirements of certain practical engineering applications. For some applications, such as robotic arm in Reference 11 performs pick‐and‐place tracking task, the stroke rehabilitation system in Reference 12 artificially stimulates muscles at some specific time instants for functional task training. Only the system output is significant for a finite number of time instants during the trial, while the rest of time instants do not need to meet the tracking requirements.…”

Section: Introductionmentioning

confidence: 99%

Robust point‐to‐point iterative learning control for constrained systems: A minimum energy approach

Zhou

Tao

Chen

et al. 2022

Intl J Robust & Nonlinear

103

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Iterative learning control (ILC) is a high performance control scenario that is widely applied to systems that repeat a given task or operation defined over a finite duration, and has been introduced to point‐to‐point motion tasks in existing work. However, its design degree of freedom has not been fully utilized to optimize performance beyond tracking accuracy in constrained conditions. The framework of point‐to‐point ILC in this article is extended within discrete linear time‐invariant (LTI) system, so as to take the tracking time instants of desired positions as changing variables. Therefore, it is possible to achieve the objective of minimizing energy while maintaining the required tracking accuracy. The multiobjective optimization problem is divided into two sub‐problems, which are solved with an iterative algorithm composed of norm‐optimal ILC approach as well as the coordinate descend method. Furthermore, the impact of model uncertainty on algorithm performance is also considered, and the iterative algorithm is further extended to capture constrained systems. The algorithm is robust to the model uncertainty and has a certain robustness to output disturbances. Finally, the validity of the proposed algorithm is verified by a twin rotor aerodynamic system (TRAS) model.

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“…Different control methods have been applied to the control of FES system, such as PID and their modifications [23], adaptive fuzzy terminal sliding mode control method [24], reinforcement learning method [7], neural network based modeling and control method [25], iterative learning control method [26], multiple-model adaptive control method [16], etc. However, none of those methods took EMD into account.…”

Section: Introductionmentioning

confidence: 99%

Force Tracking Control of Functional Electrical Stimulation via Hybrid Active Disturbance Rejection Control

Huo

Wang

Qin³

et al. 2022

Electronics

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Stroke is a worldwide disease with a high incidence rate. After surviving a stroke, most patients are left with impaired upper or lower limb. Muscle force training is vital for stroke patients to recover limb function and improve their quality of life. This paper proposes a force tracking control method for upper limb based on functional electrical stimulation (FES), which is a promising rehabilitation approach. A modified Hammerstein model is proposed to describe the nonlinear dynamics of biceps brachii, which consists of a nonlinear mapping function, linear dynamics and time delay component to represent the biochemical process of muscle contraction. A quick model identification method is presented based on the least square algorithm. To deal with the variation of muscle dynamics, a hybrid active disturbance rejection control (ADRC) is proposed to estimate and compensate for the model uncertainty and unmeasured disturbances. The parameter tuning process is given. In the end, the performance of the proposed methods is verified via simulations and experiments. Compared with the Proportional integral derivative controller (PID) method, the proposed methods could suppress the model uncertainty and improve the tracking precision.

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Data-driven gradient-based point-to-point iterative learning control for nonlinear systems

Cited by 15 publications

References 43 publications

Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear Dynamics

Bridging Reinforcement Learning and Iterative Learning Control: Autonomous Motion Learning for Unknown, Nonlinear Dynamics

Robust point‐to‐point iterative learning control for constrained systems: A minimum energy approach

Force Tracking Control of Functional Electrical Stimulation via Hybrid Active Disturbance Rejection Control

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