Model‐based vs data‐driven adaptive control: An overview

Benosman, Mouhacine

doi:10.1002/acs.2862

Cited by 80 publications

(60 citation statements)

References 335 publications

(375 reference statements)

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“…If the cargo is fixed in space and control is reduced to eliminating errors in the dynamic positioning of the cargo, then the control efforts are minimal. So, it becomes possible to achieve invariance, since the disturbances associated with the movement of the vessel are known, and it is possible to compensate them [25].…”

Section: Materials and Methods Of Analysis Of The "Vessel-cranecargo"mentioning

confidence: 99%

Development of control model for loading operations on heavy lift vessels based on inverse algorithm

Solovey¹,

Ben²,

Dudchenko³

et al. 2020

EEJET

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The aim of the work is to develop a method for optimal control of handling operations with heavy lift cargo on sea vessels. Based on the review of scientific research in the field of loading heavy lift cargo, priority directions for improving the automated control systems for cargo handling operations on ships have been determined. Within a scientific hypothesis, it was proposed to synchronize solutions to the problem of ship propulsion control and automated control of heavy lift onboard cranes in order to improve the accuracy of loading processes. The paper analyzes the dynamic model of the "vessel-crane-cargo" system and the criteria of optimality in the problem of ship regulation-stabilization under minimization of loading time. An inverse loading algorithm has been developed, based on the principles of the loading control optimization with limiting the choice of motion by linear displacements and turns of the vessel. When executing the inverse algorithm, restrictions associated with the minimization of heeling moments in the "vessel-crane-cargo" system and restrictions associated with the maximum and minimum boom outreach are applied. The study determined the technical feasibility of achieving invariance in the cargo stabilization system with the inverse loading algorithm on heavy lift vessels. On the basis of the proposed method, simulation modeling of the ship loading process was carried out on simulators at the Kherson State Maritime Academy. The simulation modeling has shown that the use of the inverse algorithm will reduce the time of cargo operations by 50-70 percent and, as a result, reduce the risk of emergencies when loading the ship. It was also determined that the use of the inverse algorithm is appropriate for cargo of more than 100 tons

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Section: Materials and Methods Of Analysis Of The "Vessel-cranecargo"mentioning

confidence: 99%

Development of control model for loading operations on heavy lift vessels based on inverse algorithm

Solovey¹,

Ben²,

Dudchenko³

et al. 2020

EEJET

View full text Add to dashboard Cite

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“…A similar role in social networks is played by stubborn agents [Yildiz et al, 2013, Parsegov et al, 2017. The clustered structure of a network substantially simplifies its structure as a control system and enables relatively simple (low-dimensional) controllers, which can operate in uncertain conditions, as exemplified by modern methods of robust and stochastic model predictive control (MPC) [Mayne et al, 2000, Mesbah, 2018, Seron et al, 2018, adaptive control [Fradkov et al, 1999, Fradkov, 2007, data-driven and learning-based control [Benosman, 2018].…”

Section: Clusters and Control Of Networkmentioning

confidence: 99%

Evolution of clusters in large-scale dynamical networks

Proskurnikov

Granichin

2018

CAP

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Recent tremendous progress in electronics, complexity theory and network science provides new opportunities for intellectual control of complex large-scale systems operating in turbulent environment via networks of interconnected miniature devices, serving as actuators, sensors and data processors. Actual dynamics of the resulting control systems are too sophisticated to be examined controlled by traditional methods, which primarily deal with ordinary differential equations. However, their complexity can be dramatically reduced by fast processes, organizing the elementary units of the system (called agents) into relatively small number of clusters. The clusters emerge and deteriorate in response to changes in the environment, and the processes of their formation and destruction are very short in time. During the periods of the clusters’ existence, the system’s dynamics is essentially low-dimensional due to synchronization between the agents in each cluster. An enormously complicated system is thus reduced to a finite-dimensional model with time-varying structure of the state vector. The low-dimensionality of the reduced model allows to control it by using classical methods, e.g. model-predictive or adaptive control. This philosophy of complex systems control is illustrated on an experimental setup, called the “airplane with feathers”. The wings of this airplane are equipped with arrays of microsensors, microcomputers, and microactuators (“feathers”). The feathers self-organize into clusters by using a multi-agent consensus protocol; the aim of this coordination is to reduce the perturbing forces, affecting the airplane in a turbulent flow.

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“…In control science and technology, it is a central issue to ensure the normal operation of control systems despite various uncertainties [1]. Motivated by this important objective, numerous control methods have been developed, such as proportional-integral-derivative control [2], adaptive control [3] and disturbance rejection methods [4][5][6][7]. Among various disturbance rejection methods, active disturbance rejection control (ADRC) has drawn lots of attention from researches due to its simplicity in practical implementation and superior performance to handle uncertainties, which has been successfully applied to flight systems [8][9][10], motion control systems [11,12] and process control systems [13][14][15], just to name a few.…”

Section: Introductionmentioning

confidence: 99%

On active disturbance rejection control for lower-triangular nonlinear uncertain systems with mismatched uncertainties and unknown control coefficients

Liu¹,

Chen

Zhao

2021

Preprint

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The paper investigates the control problem for a class of lower-triangular nonlinear uncertain systems with mismatched uncertainties and unknown values of control coefficients. Based on the signs of control coefficients rather than the nominal values or the approximative mathematical expressions, a new active disturbance rejection control is proposed. The design procedure can be concluded by three steps: determining the equivalent integrators chain form, constructing the extended state observer to estimate the total disturbance, and designing a dynamical system to let the actual input track the ideal input. Then under a mild assumption for mismatched uncertainties and unknown control coefficients, the paper rigorously analyzes the bounds of tracking error, estimating error and the error between the actual and ideal inputs. The presented theoretical results reveal the strong robustness of the proposed method to mismatched uncertainties and uncertain control input coefficients. Moreover, the tuning law of observer parameter and the parameter of dynamical input design is theoretically shown.

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Model‐based vs data‐driven adaptive control: An overview

Cited by 80 publications

References 335 publications

Development of control model for loading operations on heavy lift vessels based on inverse algorithm

Development of control model for loading operations on heavy lift vessels based on inverse algorithm

Evolution of clusters in large-scale dynamical networks

On active disturbance rejection control for lower-triangular nonlinear uncertain systems with mismatched uncertainties and unknown control coefficients

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