Adaptive iterative learning control of non-uniform trajectory tracking for strict feedback nonlinear time-varying systems with unknown control direction

Zhang, Chunli; Li, Junmin

doi:10.1016/j.apm.2014.10.070

Cited by 45 publications

(11 citation statements)

References 6 publications

(7 reference statements)

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“…Several data-based adaptive controller design methods have already been developed trying to achieve the expectation as described above for nonlinear systems, like virtual reference feedback tuning (VRFT) [5,21], iterative learning control (ILC) [3,[26][27][28], iterative feedback tuning (IFT) [9,19] , model-free adaptive control (MFAC) [10], real-time particle filter (RTPF) [13], and adaptive dynamic programming (ADP) [11,14,16] etc. Unfortunately, none of these methods can realize the expectation without any shortage related to the high autonomy.…”

Section: Introductionmentioning

confidence: 99%

“…The main idea of the ILC approach [26] is to update the desired control input according to the output measurements iteratively for each discrete time step using always the same initial conditions of the system dynamic behavior for each iteration, which is an imperative fundamental assumption of ILC [27,28]. A priori knowledge about the bounding functions of the uncertainties of the system dynamic behavior is required for the learning process [26], so ILC is not a complete model-free approach.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven stabilization of unknown nonlinear dynamical systems using a cognition-based framework

Nowak

Söffker

2016

Nonlinear Dyn

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In this paper, a cognitive stabilizer concept is introduced. The framework acts as an adaptive discrete control approach. The aim of the cognitive stabilizer is to stabilize a specific class of unknown nonlinear MIMO systems. The cognitive stabilizer is able to gain useful local knowledge of the system assumed as unknown. The approach is able to define autonomously suitable control inputs to stabilize the system. The system class to be considered is described by the following assumptions: unknown input/output behavior, fully controllable, stable zero dynamics, and measured state vector. The cognitive stabilizer is realized by its four main modules: (1) "perception and interpretation" using system identifier for the system local dynamic online identification and multi-step-ahead prediction;(2) "expert knowledge" relating to the quadratic stability criterion to guarantee the stability of the considered motion of the controlled system; (3) "planning" to generate a suitable control input sequence according to a certain cost function; (4) "execution" to generate the optimal control input in a corresponding feedback form. Each module can be realized using different methods. Two realizations will be stated in this paper. Using the cognitive stabilizer, the control goal can be achieved efficiently without an individual control design process for different kinds of unknown systems. Numerical examples (e.g., a chaotic nonlinear MIMO system-Lorenz system) demonstrate the successful application of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-driven stabilization of unknown nonlinear dynamical systems using a cognition-based framework

Nowak

Söffker

2016

Nonlinear Dyn

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“…In order to mitigate i.i.c., we use an initial‐state learning protocol to improve initial tracking performance. To regulate the control direction, inspired by the work of Zhang and Li, we apply a Nussbaum function with modifications in the convergence analysis. As we consider the general nonlinear systems that are not with parametric forms, we employ fuzzy systems to approximate to each nonlinear function, which results in a weighted parametric form and an approximation error.…”

Section: Introductionmentioning

confidence: 99%

Adaptive learning control for general nonlinear systems with nonuniform trial lengths, initial state deviation, and unknown control direction

Liu

Shen

Wang

2019

Intl J Robust & Nonlinear

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Summary This paper studies the iteration varying trail lengths problem for high‐order continuous‐time nonlinear systems, where the initial state may deviate from the desired value and the sign of input gain is unknown. First, to deal with the general nonlinear systems, a fuzzy approximation technique is applied for each dimension of the nonlinear function and the backstepping technique is then used for controller design and performance analysis. Moreover, to deal with the randomly varying trial length problem, we introduce a novel composite energy function for the asymptotic convergence analysis. Furthermore, the initial state deviation issue is resolved by introducing an initial state learning protocol such that the initial state tracking error converges to zero asymptotically. Last but not least, the unknown control direction is regulated by applying a Nussbaum function and the analysis in the presence of nonuniform trial lengths is strictly established. Based on these treatments, we prove that the tracking error converges to zero as iteration number increases and all signals are bounded. The effectiveness of the proposed framework is verified by numerical simulations.

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“…24 To solve the uncertainties of the system, a large number of researchers used adaptive iterative learning control (AILC) methods to estimate the uncertain parameters of the manipulator systems. [25][26][27][28] In recent years, AILC method became a popular technique in the control field, which generalized lots of algorithms. The following algorithms were effective to reduce the external disturbances, for instance, Nussbaum function, 29 saturation function, 30 and separation technology.…”

Section: Introductionmentioning

confidence: 99%

An adaptive iterative learning control approach based on disturbance estimation for manipulator system

Liu

Chai

Sun

et al. 2019

International Journal of Advanced Robotic Systems

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An adaptive iterative learning control approach based on disturbance estimation has been developed for trajectory tracking of manipulators with uncertain parameters and external disturbances. The external disturbances are estimated by the feedback iterative learning method, whereas the uncertain parameters are compensated by adaptive control. This approach which is based on the disturbance estimation technique provides a rapid convergence of trajectory tracking errors. According to the Lyapunov theory, the sufficient condition of the asymptotic stability has been developed for the 2-degrees of freedom (DOFs) manipulator system. The numerical results show that the adaptive iterative learning control approach based on disturbance estimation is feasible and effective for the 2-DOFs manipulator. A comparison of the adaptive iterative learning control method and the iterative learning control method is completed, which shows that the adaptive iterative learning control method performs a faster convergence of the disturbance to the steady state.

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Adaptive iterative learning control of non-uniform trajectory tracking for strict feedback nonlinear time-varying systems with unknown control direction

Cited by 45 publications

References 6 publications

Data-driven stabilization of unknown nonlinear dynamical systems using a cognition-based framework

Data-driven stabilization of unknown nonlinear dynamical systems using a cognition-based framework

Adaptive learning control for general nonlinear systems with nonuniform trial lengths, initial state deviation, and unknown control direction

An adaptive iterative learning control approach based on disturbance estimation for manipulator system

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