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Prof. Dr. Constantin Florin Caruntu
Gheorghe Asachi Technical University of Iasi, Romania

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Research Keywords & Expertise

0 Automotive Control
0 cyber-physical system
0 Distributed control systems
0 Networked control system
0 Vehicle platooning and autonomous vehicles

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Vehicle platooning and autonomous vehicles
Networked control system
cyber-physical system

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Journal article
Published: 11 June 2021 in Sensors
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Following the current technological development and informational advancement, more and more physical systems have become interconnected and linked via communication networks. The objective of this work is the development of a Coalitional Distributed Model Predictive Control (C- DMPC) strategy suitable for controlling cyber-physical, multi-agent systems. The motivation behind this endeavour is to design a novel algorithm with a flexible control architecture by combining the advantages of classical DMPC with Coalitional MPC. The simulation results were achieved using a test scenario composed of four dynamically coupled sub-systems, connected through an unidirectional communication topology. The obtained results illustrate that, when the feasibility of the local optimization problem is lost, forming a coalition between neighbouring agents solves this shortcoming and maintains the functionality of the entire system. These findings successfully prove the efficiency and performance of the proposed coalitional DMPC method.

ACS Style

Anca Maxim; Constantin-Florin Caruntu. A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application. Sensors 2021, 21, 4041 .

AMA Style

Anca Maxim, Constantin-Florin Caruntu. A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application. Sensors. 2021; 21 (12):4041.

Chicago/Turabian Style

Anca Maxim; Constantin-Florin Caruntu. 2021. "A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application." Sensors 21, no. 12: 4041.

Journal article
Published: 09 June 2021 in Sensors
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The evolution of communication networks offers new possibilities for development in the automotive industry. Smart vehicles will benefit from the possibility of connecting with the infrastructure and from an extensive exchange of data between them. Furthermore, new control strategies can be developed that benefit the advantages of these communication networks. In this endeavour, the main purposes considered by the automotive industry and researchers from academia are defined by: (i) ensuring people’s safety; (ii) reducing the overall costs, and (iii) improving the traffic by maximising the fluidity. In this paper, a cyber-physical framework (CPF) to control the access of vehicles in roundabout intersections composed of two levels is proposed. Both levels correspond to the cyber part of the CPF, while the physical part is composed of the vehicles crossing the roundabout. The first level, i.e., the edge-computing layer, is based on an analytical solution that uses multivariable optimisation to minimise the waiting times of the vehicles entering a roundabout intersection and to ensure a safe crossing. The second level, i.e., the cloud-computing layer, stores information about the waiting times and trajectories of all the vehicles that cross the roundabout and uses them for long-term analysis and prediction. The simulated results show the efficacy of the proposed method, which can be easily implemented on an embedded device for real-time operation.

ACS Style

Ovidiu Pauca; Anca Maxim; Constantin-Florin Caruntu. Multivariable Optimisation for Waiting-Time Minimisation at Roundabout Intersections in a Cyber-Physical Framework. Sensors 2021, 21, 3968 .

AMA Style

Ovidiu Pauca, Anca Maxim, Constantin-Florin Caruntu. Multivariable Optimisation for Waiting-Time Minimisation at Roundabout Intersections in a Cyber-Physical Framework. Sensors. 2021; 21 (12):3968.

Chicago/Turabian Style

Ovidiu Pauca; Anca Maxim; Constantin-Florin Caruntu. 2021. "Multivariable Optimisation for Waiting-Time Minimisation at Roundabout Intersections in a Cyber-Physical Framework." Sensors 21, no. 12: 3968.

Journal article
Published: 13 April 2021 in Sensors
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Anthropomorphic hands that mimic the smoothness of human hand motions should be controlled by artificial units of high biological plausibility. Adaptability is among the characteristics of such control units, which provides the anthropomorphic hand with the ability to learn motions. This paper presents a simple structure of an adaptive spiking neural network implemented in analogue hardware that can be trained using Hebbian learning mechanisms to rotate the metacarpophalangeal joint of a robotic finger towards targeted angle intervals. Being bioinspired, the spiking neural network drives actuators made of shape memory alloy and receives feedback from neuromorphic sensors that convert the joint rotation angle and compression force into the spiking frequency. The adaptive SNN activates independent neural paths that correspond to angle intervals and learns in which of these intervals the rotation the finger rotation is stopped by an external force. Learning occurs when angle-specific neural paths are stimulated concurrently with the supraliminar stimulus that activates all the neurons that inhibit the SNN output stopping the finger. The results showed that after learning, the finger stopped in the angle interval in which the angle-specific neural path was active, without the activation of the supraliminar stimulus. The proposed concept can be used to implement control units for anthropomorphic robots that are able to learn motions unsupervised, based on principles of high biological plausibility.

ACS Style

Mircea Hulea; George Uleru; Constantin Caruntu. Adaptive SNN for Anthropomorphic Finger Control. Sensors 2021, 21, 2730 .

AMA Style

Mircea Hulea, George Uleru, Constantin Caruntu. Adaptive SNN for Anthropomorphic Finger Control. Sensors. 2021; 21 (8):2730.

Chicago/Turabian Style

Mircea Hulea; George Uleru; Constantin Caruntu. 2021. "Adaptive SNN for Anthropomorphic Finger Control." Sensors 21, no. 8: 2730.

Journal article
Published: 01 January 2020 in IFAC-PapersOnLine
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The will to apply bio-inspired techniques to coordinate and control autonomous X vehicles (AXVs) has increased tremendously during the last decade due to their advantages in the face of complexity in today’s demanding applications. Thus, several bio-inspired approaches for multiple-entities optimization have been proposed in the literature for various limited applications, e.g., drone coordination, mobile robot formation maintenance. In all these strategies, the entities must plan their path and control their movements while coordinating their behavior w.r.t. the other members, and they must avoid collisions, so the task could be very difficult in the unstructured environments present in future manufacturing plants and goods transportation. Future applications of these bio-inspired techniques for coordination and control of AXVs include large warehouses, manufacturing, logistics, last-mile delivery, etc. The AXVs could be grouped to carry larger goods or they can act as swarm members when they do not have a common goal, but they must interact while they move to complete the allocated tasks and intersect their paths with the paths of other entities. As such, this paper illustrates the concept of applying such bio-inspired coordination and control techniques for the development of future manufacturing and goods transportation, a discussion being carried out regarding the advantages and disadvantages of several techniques for their use in specific applications.

ACS Style

Constantin F. Caruntu; Carlos M. Pascal; Anca Maxim; Ovidiu Pauca. Bio-inspired Coordination and Control of Autonomous Vehicles in Future Manufacturing and Goods Transportation. IFAC-PapersOnLine 2020, 53, 10861 -10866.

AMA Style

Constantin F. Caruntu, Carlos M. Pascal, Anca Maxim, Ovidiu Pauca. Bio-inspired Coordination and Control of Autonomous Vehicles in Future Manufacturing and Goods Transportation. IFAC-PapersOnLine. 2020; 53 (2):10861-10866.

Chicago/Turabian Style

Constantin F. Caruntu; Carlos M. Pascal; Anca Maxim; Ovidiu Pauca. 2020. "Bio-inspired Coordination and Control of Autonomous Vehicles in Future Manufacturing and Goods Transportation." IFAC-PapersOnLine 53, no. 2: 10861-10866.

Journal article
Published: 29 October 2019 in Processes
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This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost balance of wide area control systems in general. The problem of cost effective optimization of system output is taken into account in a multi-objective predictive control formulation and applied on a windmill park case study. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions. Long-term economic objectives are included and realistic simulations of a windmill park are performed. The results indicate the global optimal criterium is no longer feasible when long-term economic objectives are introduced. Instead, local sub-optimal solutions are likely to enable long-term energy efficiency in terms of balanced production of energy and costs for distribution and maintenance of a windmill park.

ACS Style

Clara M. Ionescu; Constantin F. Caruntu; Ricardo Cajo; Mihaela Ghita; Guillaume Crevecoeur; Cosmin Copot. Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study. Processes 2019, 7, 778 .

AMA Style

Clara M. Ionescu, Constantin F. Caruntu, Ricardo Cajo, Mihaela Ghita, Guillaume Crevecoeur, Cosmin Copot. Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study. Processes. 2019; 7 (11):778.

Chicago/Turabian Style

Clara M. Ionescu; Constantin F. Caruntu; Ricardo Cajo; Mihaela Ghita; Guillaume Crevecoeur; Cosmin Copot. 2019. "Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study." Processes 7, no. 11: 778.

Conference paper
Published: 01 October 2019 in 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC)
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ACS Style

Constantin F. Caruntu; Lavinia Ferariu; Carlos Pascal; Nicolae Cleju; Ciprian R. Comsa. Connected cooperative control for multiple-lane automated vehicle flocking on highway scenarios. 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC) 2019, 1 .

AMA Style

Constantin F. Caruntu, Lavinia Ferariu, Carlos Pascal, Nicolae Cleju, Ciprian R. Comsa. Connected cooperative control for multiple-lane automated vehicle flocking on highway scenarios. 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC). 2019; ():1.

Chicago/Turabian Style

Constantin F. Caruntu; Lavinia Ferariu; Carlos Pascal; Nicolae Cleju; Ciprian R. Comsa. 2019. "Connected cooperative control for multiple-lane automated vehicle flocking on highway scenarios." 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC) , no. : 1.

Conference paper
Published: 01 October 2019 in 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC)
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The control of autonomous vehicles is a topic of great interest nowadays due to their inherent advantages. They can increase the safety, lower the fuel consumption and thus reduce the travel costs, improve the driving comfort and reduce the traffic congestion. Still the control of vehicle dynamics is an open subject, many researchers and companies wanting to improve the existing strategies or to develop novel ones. The developed solutions should help the driver and ensure a safer driving at the same time. Thus, this paper proposes a solution to control the lateral and longitudinal dynamics of a vehicle. The first step is the modeling of both dynamics by constructing a nonlinear model based on the laws of physics that considers the lateral and longitudinal motions of a vehicle. To design a linear model predictive control (MPC) strategy, the nonlinear lateral model is linearized. The aim is to design two control algorithms based on the linear model of the vehicle dynamics, one for the lateral dynamics and one for the longitudinal dynamics and apply them for the full nonlinear model of the system. Both controllers were tested together by simulating an overpassing. This is a maneuver in which the lateral position and the longitudinal velocity must be controlled. The MPC algorithm for the lateral dynamics uses a predefined reference trajectory and the states of the system to compute the command for the vehicle such that the vehicle follows the trajectory and satisfies the imposed constraints. The controller for the longitudinal velocity of the vehicle computes the command such that the velocity of the vehicle follows its reference. The results illustrate that the MPC algorithms manage to control the vehicle and to satisfy the defined requirements.

ACS Style

Ovidiu Pauca; Constantin Florin Caruntu; Corneliu Lazar. Predictive Control for the Lateral and Longitudinal Dynamics in Automated Vehicles. 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC) 2019, 797 -802.

AMA Style

Ovidiu Pauca, Constantin Florin Caruntu, Corneliu Lazar. Predictive Control for the Lateral and Longitudinal Dynamics in Automated Vehicles. 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC). 2019; ():797-802.

Chicago/Turabian Style

Ovidiu Pauca; Constantin Florin Caruntu; Corneliu Lazar. 2019. "Predictive Control for the Lateral and Longitudinal Dynamics in Automated Vehicles." 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC) , no. : 797-802.

Conference paper
Published: 01 October 2019 in 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC)
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In this paper, a comparative analysis of two distributed model predictive control (DMPC) strategies tested in simulation on the same test bench in a multiple-agent framework is proposed. Firstly, some details about the non-cooperative DMPC with state-space ‘velocity-form’ formulation called DMPC ss are given. After that, the non-cooperative DMPC with input-output model denoted DMPC io is briefly presented. Moreover, the distributed strategies are evaluated with respect to the centralized and decentralized model predictive control (MPC) strategies with ‘velocity-form’ formulation, denoted C-MPC ss and DC-MPC ss , respectively. The simulation results obtained on the octuple tank process, defined as two quadruple tank processes coupled in series, show that the two non-cooperative algorithms perform similarly with the centralized and decentralized strategies when tuned properly.

ACS Style

Anca Maxim; Constantin Florin Caruntu; Corneliu Lazar; Robin De Keyser; Clara M. Ionescu. Comparative Analysis of Distributed Model Predictive Control Strategies. 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC) 2019, 468 -473.

AMA Style

Anca Maxim, Constantin Florin Caruntu, Corneliu Lazar, Robin De Keyser, Clara M. Ionescu. Comparative Analysis of Distributed Model Predictive Control Strategies. 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC). 2019; ():468-473.

Chicago/Turabian Style

Anca Maxim; Constantin Florin Caruntu; Corneliu Lazar; Robin De Keyser; Clara M. Ionescu. 2019. "Comparative Analysis of Distributed Model Predictive Control Strategies." 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC) , no. : 468-473.

Journal article
Published: 01 August 2019 in International Journal of Humanoid Robotics
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This paper details an intelligent motion planning and control approach for a one-degree of freedom joint of a robotic arm that can be used to implement anthropomorphic robotic hands. This intelligent control method is based on bio-inspired electronic neural networks and contractile artificial muscles implemented with shape memory alloy (SMA) actuators. The spiking neural network (SNN) includes several excitatory neurons that naturally determine the contraction force of the actuators, and unevenly distributed inhibitory neurons that regulate the excitatory activity. To validate the proposed concept, the experiments highlight the motion planning and control of a single-joint robotic arm. The results show that the electronic neural network is able to intelligently activate motion and hold with high precision the mobile link to the target positions even if the arm is slightly loaded. These results are encouraging for the development of improved biologically plausible neural structures that are able to control simultaneously multiple muscles.

ACS Style

Mircea Hulea; Adrian Burlacu; Constantin Florin Caruntu. Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks. International Journal of Humanoid Robotics 2019, 16, 1 .

AMA Style

Mircea Hulea, Adrian Burlacu, Constantin Florin Caruntu. Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks. International Journal of Humanoid Robotics. 2019; 16 (4):1.

Chicago/Turabian Style

Mircea Hulea; Adrian Burlacu; Constantin Florin Caruntu. 2019. "Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks." International Journal of Humanoid Robotics 16, no. 4: 1.

Conference paper
Published: 01 July 2019 in 2019 International Symposium on Signals, Circuits and Systems (ISSCS)
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Vehicle platooning is one solution to cope with issues such as increased risk of accidents, fuel consumption, and pollution, vehicle components wear, increased number of traffic jams, driving stress and discomfort for the passengers, increased journey duration, large vehicle-to-vehicle gaps. Advanced platooning approaches involve the need for reliable, short latency vehicle-to-vehicle communication (V2V). This work presents results obtained by V2V communication experiment with a low cost, customizable platform that supports different VANET standards for 5.9 GHz and 700 MHz frequency bands. 1

ACS Style

Adrian Abunei; Ciprian R. Comsa; Constantin Florin Caruntu; Ion Bogdan. Redundancy Based V2V Communication Platform for Vehicle Platooning. 2019 International Symposium on Signals, Circuits and Systems (ISSCS) 2019, 1 -4.

AMA Style

Adrian Abunei, Ciprian R. Comsa, Constantin Florin Caruntu, Ion Bogdan. Redundancy Based V2V Communication Platform for Vehicle Platooning. 2019 International Symposium on Signals, Circuits and Systems (ISSCS). 2019; ():1-4.

Chicago/Turabian Style

Adrian Abunei; Ciprian R. Comsa; Constantin Florin Caruntu; Ion Bogdan. 2019. "Redundancy Based V2V Communication Platform for Vehicle Platooning." 2019 International Symposium on Signals, Circuits and Systems (ISSCS) , no. : 1-4.

Conference paper
Published: 01 July 2019 in 2019 IEEE 15th International Conference on Control and Automation (ICCA)
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Mobile robot formation control is one of the most important problems in multi-robot systems. A very challenging sub-problem is mobile robot platooning, which means that the mobile robots should follow each other and should maintain a safe distance between them. In order to avoid collisions in the platoon, the controllers have to be designed to ensure string stability, i.e., the spacing errors should not get amplified as they propagate upstream from robot to robot. This paper investigates two different decentralized model predictive control strategies for platoon guidance using only longitudinal changes for the mobile robots. Moreover, the simple solution for mobile robot platooning assumes that there is no communication between them and each mobile robot measures only the distance between itself and the one in front of it. Furthermore, several comparisons are made with classical proportional string stable controllers and a performance analysis is provided.

ACS Style

Constantin Florin Caruntu; Cosmin Copot; Corneliu Lazar; Robin De Keyser. Decentralized Predictive Formation Control for Mobile Robots without Communication. 2019 IEEE 15th International Conference on Control and Automation (ICCA) 2019, 555 -560.

AMA Style

Constantin Florin Caruntu, Cosmin Copot, Corneliu Lazar, Robin De Keyser. Decentralized Predictive Formation Control for Mobile Robots without Communication. 2019 IEEE 15th International Conference on Control and Automation (ICCA). 2019; ():555-560.

Chicago/Turabian Style

Constantin Florin Caruntu; Cosmin Copot; Corneliu Lazar; Robin De Keyser. 2019. "Decentralized Predictive Formation Control for Mobile Robots without Communication." 2019 IEEE 15th International Conference on Control and Automation (ICCA) , no. : 555-560.

Conference paper
Published: 01 May 2019 in 2019 International Conference on Robotics and Automation (ICRA)
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ACS Style

Mircea Hulea; Adrian Burlacu; Constantin Florin Caruntu. Robotic Joint Control System based on Analogue Spiking Neural Networks and SMA Actuators. 2019 International Conference on Robotics and Automation (ICRA) 2019, 1 .

AMA Style

Mircea Hulea, Adrian Burlacu, Constantin Florin Caruntu. Robotic Joint Control System based on Analogue Spiking Neural Networks and SMA Actuators. 2019 International Conference on Robotics and Automation (ICRA). 2019; ():1.

Chicago/Turabian Style

Mircea Hulea; Adrian Burlacu; Constantin Florin Caruntu. 2019. "Robotic Joint Control System based on Analogue Spiking Neural Networks and SMA Actuators." 2019 International Conference on Robotics and Automation (ICRA) , no. : 1.

Conference paper
Published: 01 May 2019 in 2019 20th International Carpathian Control Conference (ICCC)
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Nowadays, the networked control systems (NCSs) are used in different industries, including the manufacturing plants, automotive systems, power production and distribution, tele-robotics and tele-medicine. The NCSs bring a lot of advantages when dealing with large plants, but they also come with disadvantages issued by the network-enhanced complexities, one of them being caused by data-packet dropouts. The disadvantages usually degrade the control systems performances and can even lead to instability. Thus, in this paper, the problem of compensating the negative effects induced by the data-packet dropouts is tackled. For this, a predictive controller with inherited input-to-state stability and robustness is designed; the controller is based on the so-called flexible control Lyapunov functions and makes use of a newly developed method to model the network-induced data-packet dropouts effect as a disturbance. The flexibility of the control Lyapunov function is constrained by means of a new less conservative solution. The method's efficiency was tested in simulation on an electric power-assisted steering system controlled through controller area network and the results are promising.

ACS Style

Constantin Florin Caruntu. A Less Conservative Condition for Flexible Control Lyapunov Functions used in Networked Predictive Control Systems. 2019 20th International Carpathian Control Conference (ICCC) 2019, 1 -6.

AMA Style

Constantin Florin Caruntu. A Less Conservative Condition for Flexible Control Lyapunov Functions used in Networked Predictive Control Systems. 2019 20th International Carpathian Control Conference (ICCC). 2019; ():1-6.

Chicago/Turabian Style

Constantin Florin Caruntu. 2019. "A Less Conservative Condition for Flexible Control Lyapunov Functions used in Networked Predictive Control Systems." 2019 20th International Carpathian Control Conference (ICCC) , no. : 1-6.

Conference paper
Published: 01 May 2019 in 2019 22nd International Conference on Control Systems and Computer Science (CSCS)
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This paper proposes a coalitional model predictive control algorithm with feasibility guarantees for systems with bounded additive uncertainties. This formulation, suitable for sub-systems coupled through the inputs, assumes the coupling variables as disturbances and ensures a robust consensus with minimum information exchange. The simulation results show that the coalitional method has similar behaviour to the fully centralized algorithm and improved performance with respect to the decentralized and the iterative min-max distributed model predictive controllers.

ACS Style

Anca Maxim; Jose M. Maestre; Constantin Florin Caruntu; Corneliu Lazar. Min-Max Coalitional Model Predictive Control Algorithm. 2019 22nd International Conference on Control Systems and Computer Science (CSCS) 2019, 24 -29.

AMA Style

Anca Maxim, Jose M. Maestre, Constantin Florin Caruntu, Corneliu Lazar. Min-Max Coalitional Model Predictive Control Algorithm. 2019 22nd International Conference on Control Systems and Computer Science (CSCS). 2019; ():24-29.

Chicago/Turabian Style

Anca Maxim; Jose M. Maestre; Constantin Florin Caruntu; Corneliu Lazar. 2019. "Min-Max Coalitional Model Predictive Control Algorithm." 2019 22nd International Conference on Control Systems and Computer Science (CSCS) , no. : 24-29.

Conference paper
Published: 01 April 2019 in 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT)
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Nowadays, the wind power industry development and the increasing size of wind farms pose a couple of technical challenges. The most fundamental problem is caused by the dynamics of the air turbulence created by wind turbines (i.e., turbines mix wind into chaotic vortices, termed wakes). The power production of a wind turbine that operates in the wake of another is reduced up to 40%, the turbine having also a higher unsteady loading and a shorter mean time between failures. But, considering the wind turbines inter-dependencies and making them cooperative by exchanging information, the resulting wind farm performances would be much greater than those obtained while controlling individual wind turbines and neglecting their interactions. Moreover, the particular feature of wind farms, i.e., the interconnected structure of the involved wind turbines (seen as sub-systems) through the wakes effect, make them suitable as distributed model predictive control (DMPC) applications. Thus, this paper makes a literature review regarding the recent results on DMPC for the cooperative wind farm control problem, starting with the wind turbines and wind farms modeling and control, then presenting the DMPC solutions and concluding with the challenges and opportunities for future research directions.

ACS Style

Constantin Florin Caruntu. Distributed predictive control for wind farms efficiency maximization: challenges and opportunities. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) 2019, 452 -457.

AMA Style

Constantin Florin Caruntu. Distributed predictive control for wind farms efficiency maximization: challenges and opportunities. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). 2019; ():452-457.

Chicago/Turabian Style

Constantin Florin Caruntu. 2019. "Distributed predictive control for wind farms efficiency maximization: challenges and opportunities." 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) , no. : 452-457.

Journal article
Published: 29 November 2018 in International Journal of Computers Communications & Control
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This paper presents a strategy for computing model predictive control of linear Gaussian noise systems with probability constraints. As usual, constraints are taken on the system state and control input. The novelty relies on setting bounds on the underlying cumulative probability distribution, and showing that the model predictive control can be computed in an efficient manner through these novel bounds— an application confirms this assertion. Indeed real-time experiments were carried out to control a direct current (DC) motor. The corresponding data show the effectiveness and usefulness of the approach.

ACS Style

Constantin Florin Caruntu; Cristian C. Velandia-Cardenas; Xinghua Liu; Alessandro Vargas. Model Predictive Control of Stochastic Linear Systems with Probability Constraints. International Journal of Computers Communications & Control 2018, 13, 927 -937.

AMA Style

Constantin Florin Caruntu, Cristian C. Velandia-Cardenas, Xinghua Liu, Alessandro Vargas. Model Predictive Control of Stochastic Linear Systems with Probability Constraints. International Journal of Computers Communications & Control. 2018; 13 (6):927-937.

Chicago/Turabian Style

Constantin Florin Caruntu; Cristian C. Velandia-Cardenas; Xinghua Liu; Alessandro Vargas. 2018. "Model Predictive Control of Stochastic Linear Systems with Probability Constraints." International Journal of Computers Communications & Control 13, no. 6: 927-937.

Journal article
Published: 27 November 2018 in Journal of the Franklin Institute
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This paper presents conditions to assure the exponential stability in probability for autonomous switching linear systems. The switching signal acting on the autonomous system produces intervals that follow independent, identically distributed stochastic processes—the stability then follows by verifying simple-to-check linear matrix inequalities.

ACS Style

Alessandro N. Vargas; Constantin F. Caruntu; João Y. Ishihara. Stability of switching linear systems with switching signals driven by stochastic processes. Journal of the Franklin Institute 2018, 356, 31 -41.

AMA Style

Alessandro N. Vargas, Constantin F. Caruntu, João Y. Ishihara. Stability of switching linear systems with switching signals driven by stochastic processes. Journal of the Franklin Institute. 2018; 356 (1):31-41.

Chicago/Turabian Style

Alessandro N. Vargas; Constantin F. Caruntu; João Y. Ishihara. 2018. "Stability of switching linear systems with switching signals driven by stochastic processes." Journal of the Franklin Institute 356, no. 1: 31-41.

Conference paper
Published: 01 October 2018 in 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC)
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Vehicle platooning became an interesting topic in the last years, many researchers and practitioners from the academia and industry trying to develop new theories and design appropriate control methods and communication methodologies in order to bring this concept as fast as possible on the roads. Since vehicles drive on multi-lane roads and highways, the subsequent paradigm was to treat vehicles as swarms, i.e., groups of vehicles that travel closely together on different lanes and are electronically connected. A step forward towards this new concept would be the design of multiple-lane platoons. As such, this paper proposes a multi-agent distributed model predictive control strategy for the longitudinal coordination of the vehicles in individual platoons and a classical PI control algorithm for the lateral control of each vehicle in the platoon w.r.t. its neighbors. The simulation results obtained in Matlab/Simulink and the performance analysis prove that the concept is viable.

ACS Style

Constantin Florin Caruntu; Anca Maxim; Razvan C. Rafaila. Multiple-Lane Vehicle Platooning based on a Multi-Agent Distributed Model Predictive Control Strategy. 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC) 2018, 759 -764.

AMA Style

Constantin Florin Caruntu, Anca Maxim, Razvan C. Rafaila. Multiple-Lane Vehicle Platooning based on a Multi-Agent Distributed Model Predictive Control Strategy. 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC). 2018; ():759-764.

Chicago/Turabian Style

Constantin Florin Caruntu; Anca Maxim; Razvan C. Rafaila. 2018. "Multiple-Lane Vehicle Platooning based on a Multi-Agent Distributed Model Predictive Control Strategy." 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC) , no. : 759-764.

Conference paper
Published: 01 August 2018 in 2018 IEEE Conference on Control Technology and Applications (CCTA)
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This paper proposes a robust coalitional distributed model predictive control algorithm suitable for input coupled sub-systems. The core idea is based on a distributed iterative implementation with minimum information exchange, in which the coupling information is regarded as bounded additive uncertainty. If the disturbance received from the neighbours is too large for the local optimization problem, then a coalitional strategy is adopted. The closed-loop stability is guaranteed via terminal constraint. The performance of the coalitional strategy is compared with an iterative min-max distributed model predictive controller and a centralized model predictive controller.

ACS Style

Anca Maxim; J. M. Maestre; Constantin Florin Caruntu; C. Lazar. Robust Coalitional Distributed Model Predictive Control Algorithm with Stability via Terminal Constraint. 2018 IEEE Conference on Control Technology and Applications (CCTA) 2018, 964 -969.

AMA Style

Anca Maxim, J. M. Maestre, Constantin Florin Caruntu, C. Lazar. Robust Coalitional Distributed Model Predictive Control Algorithm with Stability via Terminal Constraint. 2018 IEEE Conference on Control Technology and Applications (CCTA). 2018; ():964-969.

Chicago/Turabian Style

Anca Maxim; J. M. Maestre; Constantin Florin Caruntu; C. Lazar. 2018. "Robust Coalitional Distributed Model Predictive Control Algorithm with Stability via Terminal Constraint." 2018 IEEE Conference on Control Technology and Applications (CCTA) , no. : 964-969.

Journal article
Published: 19 July 2018 in IFAC-PapersOnLine
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Intersections are the bottlenecks of the urban arterial causing the reduction of its capacity. In order to maximize the efficiency of existing transportation systems capacity with urban congestion reduction, intelligent transportation systems are employed. In this paper, such a solution is presented using the cooperative adaptive cruise control (CACC) of vehicle platoons waiting at a red traffic signal which enables thus to begin accelerating in a coordinated manner once the traffic signal turns green. This coordinated start could allow more vehicles to pass through an intersection on a green cycle than manual driving and thus increasing the urban arterial capacity. To design and evaluate the CACC system, a novel simulator for connected vehicle applications is developed. The simulation results show that the proposed CACC approach offers significant mobility.

ACS Style

C. Lazar; A. Tiganasu; C.F. Caruntu. Arterial Intersection Improvement by Using Vehicle Platooning and Coordinated Start. IFAC-PapersOnLine 2018, 51, 136 -141.

AMA Style

C. Lazar, A. Tiganasu, C.F. Caruntu. Arterial Intersection Improvement by Using Vehicle Platooning and Coordinated Start. IFAC-PapersOnLine. 2018; 51 (9):136-141.

Chicago/Turabian Style

C. Lazar; A. Tiganasu; C.F. Caruntu. 2018. "Arterial Intersection Improvement by Using Vehicle Platooning and Coordinated Start." IFAC-PapersOnLine 51, no. 9: 136-141.