Design and Analysis of Data-Driven Learning Control: An Optimization-Based Approach

Meng, Deyuan; Zhang, Jingyao

doi:10.1109/tnnls.2021.3070920

Cited by 18 publications

(12 citation statements)

References 47 publications

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“…Therefore, DDC theory began to be researched. There have had many DDC methods in the existing literature, such as PID control, model free adaptive control (MFAC) [8], iterative learning control (ILC) [9][10][11][12], iterative feedback tuning (IFT) [13] and so on. MFAC and ILC are proposed to solve the control problem of nonlinear systems by directly applying the I/O data of the controlled systems.…”

Section: Introductionmentioning

confidence: 99%

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

Zhou

Che

et al. 2022

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This paper mainly studies the data-based security fault tolerant iterative learning control (SFTILC) problem of nonlinear networked control systems (NCSs) under sensor failures and denial-of-service (DoS) attacks. Firstly, the radial basis function neural network (RBFNN) is used to approximate the sensor failure function and a DoS attack compensation mechanism is proposed in the iterative domain to lessen the impact of DoS attacks. Then, using the dynamic linearization technology, the nonlinear system considering failures and network attacks is transformed into a linear data model. Further, based on the designed linearization model, a new data-based SFTILC algorithm is designed to ensure the satisfactory tracking performance of the system. This process only uses the input and output data of the system, and the stability of the system is proved by using the compression mapping principle. Finally, a digital simulation is used to demonstrate the effectiveness of the proposed SFTILC algorithm.

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Section: Introductionmentioning

confidence: 99%

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

Zhou

Che

et al. 2022

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“…The L2 level learning process relies on the CLCS linearity, allowing for the application of one variant of ILC which is agnostic to the CLCS dynamics, called the experiment-driven model-free ILC (EDMFILC) [ 1 , 2 , 3 ]. This technique belongs to the popular data-driven ILC approaches [ 37 , 38 , 39 , 40 , 41 , 42 ] as part of data-driven research [ 43 , 44 , 45 , 46 , 47 ]. Here, the convergence analysis selects a conservative learning gain, based on equivalent CLCS models resulting from the actual reference model and from identified models based on the reusable input–output data.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking within a Hierarchical Primitive-Based Learning Approach

Rădac

2022

Entropy

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A hierarchical learning control framework (HLF) has been validated on two affordable control laboratories: an active temperature control system (ATCS) and an electrical rheostatic braking system (EBS). The proposed HLF is data-driven and model-free, while being applicable on general control tracking tasks which are omnipresent. At the lowermost level, L1, virtual state-feedback control is learned from input–output data, using a recently proposed virtual state-feedback reference tuning (VSFRT) principle. L1 ensures a linear reference model tracking (or matching) and thus, indirect closed-loop control system (CLCS) linearization. On top of L1, an experiment-driven model-free iterative learning control (EDMFILC) is then applied for learning reference input–controlled outputs pairs, coined as primitives. The primitives’ signals at the L2 level encode the CLCS dynamics, which are not explicitly used in the learning phase. Data reusability is applied to derive monotonic and safely guaranteed learning convergence. The learning primitives in the L2 level are finally used in the uppermost and final L3 level, where a decomposition/recomposition operation enables prediction of the optimal reference input assuring optimal tracking of a previously unseen trajectory, without relearning by repetitions, as it was in level L2. Hence, the HLF enables control systems to generalize their tracking behavior to new scenarios by extrapolating their current knowledge base. The proposed HLF framework endows the CLCSs with learning, memorization and generalization features which are specific to intelligent organisms. This may be considered as an advancement towards intelligent, generalizable and adaptive control systems.

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“…To make use of the repetitive characteristic of batch process, scholars have proposed using optimization-based ILC algorithm to enhance the control performance of batch process. [3][4][5][6][7][8] Because of the superior capability of solving multivariate constrained problems, model-based optimal ILC methods have been extensively researched. Lee et al 9 presented the quadratic-criterion-based iterative learning control (Q-ILC) strategy to reject to various external disturbances in batch process.…”

Section: Introductionmentioning

confidence: 99%

“…Different from continuous process, the control issue of the batch process is sophisticated and difficult due to its always time‐varying, highly nonlinear, and repetitive. To make use of the repetitive characteristic of batch process, scholars have proposed using optimization‐based ILC algorithm to enhance the control performance of batch process 3‐8 …”

Section: Introductionmentioning

confidence: 99%

A local dynamic extreme learning machine based iterative learning control of nonlinear batch process

Zhou

Jia

2021

Optim Control Appl Methods

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This article deals with the optimal control issue of nonlinear batch process. First, in order to derive high efficiency and accuracy process model, a novel hierarchical searching mechanism local dynamic nonlinear model is constructed which is composed of just‐in‐time learning and extreme learning machine (JITL‐ELM). Then, based on the local dynamic JITL‐ELM model, an optimal quadratic‐criterion‐based iterative learning control (Q‐ILC) algorithm is presented, where the control input trajectory can be obtained by solving a quadratic programming problem. Moreover, on the basis of inverse model system, the initial batch control input trajectory of the Q‐ILC algorithm can be obtained by the use of JITL method. As a result, not only the issue of model‐plant mismatch and real‐time disturbance can be solved, but also obtain faster system convergence rate and smaller tracking error. Besides, the convergence properties of control input and tracking error are analyzed. Finally, a typical batch process is presented to demonstrate the feasibility and superiority.

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Design and Analysis of Data-Driven Learning Control: An Optimization-Based Approach

Cited by 18 publications

References 47 publications

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

Trajectory Tracking within a Hierarchical Primitive-Based Learning Approach

A local dynamic extreme learning machine based iterative learning control of nonlinear batch process

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