A Robust Adaptive Iterative Learning Control for Trajectory Tracking of Permanent-Magnet Spherical Actuator

Zhang, Liang; Chen, Weihai; Liu, Jingmeng; Chen, Wen

doi:10.1109/tie.2015.2464186

Cited by 84 publications

(43 citation statements)

References 18 publications

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“…In particular, most control methodologies improve tracking performance by gradually tuning the input along the time axis, whereas ILC concerns more about the iteration‐axis‐based adjustment of control. Indeed, ILC features structure simplicity, model independent design, and high tracking precision among other advantages, which renders it widely applicable under various design conditions …”

Section: Introductionmentioning

confidence: 99%

Robust learning control for nonlinear systems with nonparametric uncertainties and nonuniform trial lengths

Shen

2018

Intl J Robust & Nonlinear

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This paper proposes robust iterative learning control schemes for continuous-time nonlinear systems with various nonparametric uncertainties under nonuniform trial length circumstances. The nonuniform trial length is described by a random variable, which causes a random data missing problem while designing and analyzing algorithms for the precise tracking problem. Three common types of nonparametric uncertainties are taken into account: norm-bounded uncertainty, variation-norm-bounded uncertainty, and norm-bounded uncertainty with unknown coefficients. A novel composite energy function is introduced with the help of a newly defined virtual tracking error for the asymptotical convergence of the proposed schemes. Extensions to multiple-input-multiple-output cases are also elaborated. Illustrative simulations are provided to verify the theoretical results. KEYWORDS composite energy function, nonparametric nonlinear systems, nonuniform trial length, robust iterative learning control INTRODUCTIONIterative learning control (ILC) is a distinct type of intelligent control for repetitive systems, those can complete certain tracking tasks in a finite time interval repeatedly. For these systems, ILC utilizes the tracking information including input and output signals from previous iterations/trials/cycles and the desired tracking trajectory to generate a new input signal for the current iteration/trial/cycle. [1][2][3][4] The essential character of ILC differing from other control methodologies lies in the improvement direction. In particular, most control methodologies improve tracking performance by gradually tuning the input along the time axis, whereas ILC concerns more about the iteration-axis-based adjustment of control. Indeed, ILC features structure simplicity, model independent design, and high tracking precision among other advantages, which renders it widely applicable under various design conditions. 5-10 It is the system repetitiveness that offers the well-tracking performance of ILC. That is, the operation conditions for different iterations should retain the same so that ILC can learn the iteration-invariant characteristics and then improve the tracking performance along the iteration axis. For example, ILC usually requires the system to be terminated within the same trial length. However, in some practical applications such as biomedical auxiliary systems, we have to end the learning trial early if the derivation of the actual output from the desired trajectory is clearly large to maintain safety. Recent progress on functional electrical stimulation for upper limb movement and gait assistance 11-13 and humanoid and biped walking robots 14 has shown this demand. Thus, it is of great importance to investigate how to design ILC schemes for systems under randomly nonuniform trial length circumstances. 1302

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Section: Introductionmentioning

confidence: 99%

Robust learning control for nonlinear systems with nonparametric uncertainties and nonuniform trial lengths

Shen

2018

Intl J Robust & Nonlinear

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“…For fair comparison, we still choose = 0.25 min. Moreover, assume that h = 1.2 min in (11) and that Γ(t) takes the form of cubic polynomial, where the coefficients can be calculated by solving (i) and (ii) below (11). Fig.…”

Section: Illustrative Examplementioning

confidence: 99%

“…Summarizing them, one trend is to focus on ILC algorithm design for more general problem settings, e.g., iteration-varying trial lengths [2], disturbances with high-order internal model [3], stochastic factors [4], as well as high-dimensional or even infinite-dimensional process model [1,5]. Another trend is to apply the well-established ILC theory to all kinds of industrial or engineering processes, e.g., multi-phase batch processes [6], robotic fish [7], urban road networks [8], marine vibrator [9], wind turbines [10], permanent-magnet spherical actuator [11], and high-speed train [12].…”

Section: Introductionmentioning

confidence: 99%

Improved D‐Type Anticipatory Iterative Learning Control for a Class of Inhomogeneous Heat Equations

Huang

2016

Asian Journal of Control

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This paper aims at providing a practical iterative learning control (ILC) scheme for a wide class of heat transfer systems in the sense that it avoids high-gain learning of ILC, thus a potential non-monotonic convergence issue, and the risk of violating the hardware limitation of input profile in implementation. Meanwhile, the ILC scheme guarantees the identical initial condition of heat process. As a result, the output tracking precision may be improved while not reducing the anticipatory step size as in [1]. All the benefits of the proposed ILC scheme are achieved by applying a heuristic selection algorithm for the anticipatory step size and rectifying the output reference simultaneously.

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“…9,10 Typical applications of ILC can be found in various robots 11,12 and industrial devices. 13,14 In classic ILC, it requires that every execution (trial, iteration, pass) must be completed in a fixed time duration. However, in many practical control systems, this requirement may not hold due to the limitations of control objects, system constraints, or safety problems.…”

Section: Introductionmentioning

confidence: 99%

Sampled‐data iterative learning control for continuous‐time nonlinear systems with iteration‐varying lengths

Wang

Shen

2018

Intl J Robust & Nonlinear

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In this work, sampled-data iterative learning control (ILC) method is extended to a class of continuous-time nonlinear systems with iteration-varying trial lengths.In order to propose a unified ILC algorithm, the tracking errors will be redefined when the trial length is shorter or longer than the desired one. Based on the modified tracking errors, 2 sampled-data ILC schemes are proposed to handle the randomly varying trial lengths. Sufficient conditions are derived rigorously to guarantee the convergence of the nonlinear system at each sampling instant.To verify the effectiveness of the proposed ILC laws, simulations for a nonlinear system are performed. The simulation results show that if the sampling period is set to be small enough, the convergence of the learning algorithms can be achieved as the iteration number increases. KEYWORDSinitial state condition, iteration learning control, iteration-varying lengths, iteratively moving average operator, relative degree, sampled-data Int J Robust Nonlinear Control. 2018;28:3073-3091.wileyonlinelibrary.com/journal/rnc

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A Robust Adaptive Iterative Learning Control for Trajectory Tracking of Permanent-Magnet Spherical Actuator

Cited by 84 publications

References 18 publications

Robust learning control for nonlinear systems with nonparametric uncertainties and nonuniform trial lengths

Robust learning control for nonlinear systems with nonparametric uncertainties and nonuniform trial lengths

Improved D‐Type Anticipatory Iterative Learning Control for a Class of Inhomogeneous Heat Equations

Sampled‐data iterative learning control for continuous‐time nonlinear systems with iteration‐varying lengths

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