Norm-Optimal Iterative Learning Control With Intermediate Point Weighting: Theory, Algorithms, and Experimental Evaluation

Owens, D.H.; Freeman, Chris T.; Dinh, Thanh Van

doi:10.1109/tcst.2012.2196281

Cited by 78 publications

(57 citation statements)

References 24 publications

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“…The approach then reduces to the intermediate point tracking problem [5] . In addition, G is right-invertible, then the convergence is geometric as…”

Section: Formal Solution Of the Intermediate Point Algorithm With Submentioning

confidence: 99%

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Generalized norm optimal iterative learning control with intermediate point and sub-interval tracking

Owens

Freeman

Chu

2015

Int. J. Autom. Comput.

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Norm optimal iterative learning control (NOILC) has recently been applied to iterative learning control (ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC s well-defined convergence properties. Design guidelines and supporting experimental results are included.

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“…The approach then reduces to the intermediate point tracking problem [5] . In addition, G is right-invertible, then the convergence is geometric as…”

Section: Formal Solution Of the Intermediate Point Algorithm With Submentioning

confidence: 99%

“…The formal solution as a boundary value problem can be expressed either as a causal feedforward solution when Q(t) = 0 [5] or more generally, as a causal feedback plus feedforward solution. The details are described below.…”

Section: A Causal Algorithmmentioning

confidence: 99%

Generalized norm optimal iterative learning control with intermediate point and sub-interval tracking

Owens

Freeman

Chu

2015

Int. J. Autom. Comput.

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“…It removes the unnecessary output constraints, and releases extra freedom in control design to address additional performance objectives. Recent research has been made on optimal problem [8], [9], frequency analysis [7] and constrained conditions [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Point-to-point iterative learning control with optimal tracking time allocation: A coordinate descent approach

Chen

Chu

Freeman

2017

2017 36th Chinese Control Conference (CCC)

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Iterative learning control (ILC) is a high performance control technique for systems operating in a repetitive manner. A novel design methodology is developed in this paper to incorporate optimal tracking time allocation within the point-topoint ILC framework for discrete time systems. This leads to significant performance improvements compared to fixed time points (e.g. energy reduction). An optimization problem is formulated based on the point-to-point tracking requirement and the via-point temporal constraints. A two stage design framework is proposed to solve this problem, yielding an algorithm based on norm optimal ILC and the coordinate descent method, which automatically minimizes control effort while maintaining high performance tracking. The proposed algorithm is implemented on a gantry robot experimental test platform, with results verifying its practical effectiveness in the presence of model uncertainty.

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“…The specified task is regarded as improving the tracking performance of systems. The objective of iterative learning control (ILC) [1][2][3][4][5][6] is to use the information from previous executions of the task and do repetitive work by tracking error in attempt to achieve the desired trajectory to minimal error, which has been successfully applied to the real systems, such as industrial robots, wafer scanner, chemical processes and many production machines.…”

Section: Introductionmentioning

confidence: 99%

One-Learning-Epoch Optimal LQDT with Input Constraint for the Repetitive Proper System with Unknown Disturbances

2016

2016 International Conference on Advances in Software, Control and Mechanical Engineering (ICSCME-16) April 12-13, 2016 Kyoto (

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Norm-Optimal Iterative Learning Control With Intermediate Point Weighting: Theory, Algorithms, and Experimental Evaluation

Cited by 78 publications

References 24 publications

Generalized norm optimal iterative learning control with intermediate point and sub-interval tracking

Generalized norm optimal iterative learning control with intermediate point and sub-interval tracking

Point-to-point iterative learning control with optimal tracking time allocation: A coordinate descent approach

One-Learning-Epoch Optimal LQDT with Input Constraint for the Repetitive Proper System with Unknown Disturbances

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