Stochastic iterative learning control for discrete linear time-invariant system with batch-varying reference trajectories

Oh, Se-Kyu; Lee, Jong Min

doi:10.1016/j.jprocont.2015.09.008

Cited by 36 publications

(8 citation statements)

References 14 publications

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“…Remark 4. In the case when the system Markov parameters are unavailable, iterative learning identification ( 16) is embedded into the norm-optimal ILC (27), which is formed in the DDOILC scheme. In (28), the control law is equipped with an inversion matrix, which is prominently different from the existing strategies [19,21,22]; for avoiding matrix inversion and guaranteeing the convergence of the tracking error, the gain matrix of the control law is transigent and replaces the…”

Section: Data-driven Optimal Ilcmentioning

confidence: 99%

“…On the other side, the multi-phase batch process is a common process in which the dynamics of each batch can be described as a switched system whose dynamics switches among a finite number of subsystems with different phases (or time intervals) [23][24][25]. Although the achievement of ILC schemes for single-phase batch processes is remarkable (e.g., [26][27][28][29]), to date, there have been few investigations of the ILC mechanism for the multi-phase batch process. It was not until 2007 that a formulation of control was presented for the first time for multi-phase batch processes in [24].…”

Section: Introductionmentioning

confidence: 99%

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The Convergence of Data-Driven Optimal Iterative Learning Control for Linear Multi-Phase Batch Processes

2022

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For multi-phase batch processes with different dimensions whose dynamics can be described as a linear discrete-time-invariant system in each phase, a data-driven optimal ILC was explored using multi-operation input and output data that subordinate a tracking performance criterion. An iterative learning identification was constructed to estimate the system Markov parameters by minimizing the evaluation criterion that consists of the residual of the real outputs from the predicted outputs and two adjacent identifications. Meanwhile, the estimated Markov parameters matrix was embedded into the learning control process in the form of an interaction. By virtue of inner product theory, the monotonic descent of the estimation error was derived, which does not restrict the weighting factor at all. Furthermore, algebraic derivation demonstrates that the tracking is strictly monotonically convergent if the estimation error falls within an appropriate domain. Numerical simulations were carried out to illustrate the validity and the effectiveness of the proposed method.

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Section: Data-driven Optimal Ilcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Convergence of Data-Driven Optimal Iterative Learning Control for Linear Multi-Phase Batch Processes

2022

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“…ILC is an active control scheme which controls the systems in iteration domain, whereas conventional controllers like PID, LQR or MPC control the system in time domain [21,26].…”

Section: Iterative Learning Control Based Fractional Order Pid Contromentioning

confidence: 99%

Iterative Learning Control Based Fractional Order PID Controller for Magnetic Levitation System

Hanif

Shaikh

Ali

2019

Mehran Univ. res. j. eng. technol.

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Maglev (Magnetic Levitation) systems are an interesting class of systems since they work without any physical contact and are hence frictionless. Due to this attractive property, such systems have the potential for wide range of applications such as maglev trains. Maglev is non-linear due to magnetic field and unstable that suggest the need of stabilizing controller. An appropriate controller is required to levitate the object at desired position. FOPID (Fractional Order Proportional Integral Derivative) controller and ILC (Iterative learning Control) based FOPID controller are designed in this paper for the levitation of metallic ball with desired reference at minimum transient errors. Since maglev is unstable and ILC is used only for stable systems, FOPID controller is used to stabilize the plant. Non-linear interior point optimization method is used to obtain the parameters of FOPID controller. An ILC is used as a feedforward controller in order to improve the response iteratively. P, PD and PID-ILC control laws are used to update the new control input in ILC based FOPID controller. The overall control scheme is therefore a hybrid combination of ILC and FOPID. The effectiveness of proposed technique is analyzed based on performance indices via simulation. ISE (Integral Square Error) and IAE (Integral Absolute Error) is lesser in case of hybrid PID-ILC as compared to simple FOPID controller.

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“…At present, FTC has been extended to multi-phase or even optimal guaranteed cost fault-tolerant control [17]. Iterative learning control (ILC) has good robustness, especially for repetitive processes [18][19][20][21][22][23][24]. It includes single-phase chemical process [23] and multi-phase chemical process [24].…”

Section: Introductionmentioning

confidence: 99%

Genetic-Algorithm-Optimization-Based Predictive Functional Control for Chemical Industry Processes Against Partial Actuator Faults

2020

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Actuator faults, which are common in industrial processes, can make the controller fail to achieve the desired control objectives, which may lead to the degradation of control performance. In order to solve this problem, this paper proposes a predictive functional control based on genetic algorithm optimization. Firstly, an extended dimension discrete switched model is constructed, which consists of a state difference variable, tracking error and a new state variable including tracking error. In this model, the performance index function based on a genetic optimization algorithm is selected, and its parameters are adjusted and the controller is designed. Then, under the obtained control law, the switching signal is designed and the range of uncertainty caused by the actuator fault is given to realize the robustness of the system. At the same time, the corresponding robustly sufficient conditions are presented. The advantage of this design is to avoid the disadvantages of manually adjusting the performance parameters, and the system has good tracking performance. Finally, taking the typical injection molding process of chemical production process as an example, the speed and pressure parameters are controlled, and compared with the traditional control method, the effectiveness and feasibility of the proposed method are verified. INDEX TERMS Chemical industry processes, partial actuator faults, genetic-algorithm-optimization design, predictive functional control

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Stochastic iterative learning control for discrete linear time-invariant system with batch-varying reference trajectories

Cited by 36 publications

References 14 publications

The Convergence of Data-Driven Optimal Iterative Learning Control for Linear Multi-Phase Batch Processes

The Convergence of Data-Driven Optimal Iterative Learning Control for Linear Multi-Phase Batch Processes

Iterative Learning Control Based Fractional Order PID Controller for Magnetic Levitation System

Genetic-Algorithm-Optimization-Based Predictive Functional Control for Chemical Industry Processes Against Partial Actuator Faults

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