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International Conference on Unmanned Aircraft Systems

作者： Juliano A. de Bonfim Gripp Ulisses P. Sampaio Instituto Tecnologico de Aeronautica

ISBN: (纸本)9781479923779

Recent advancements in processing capabilities have enabled the possibility of use of new computationally costly control methods like the model predictive control (MPC) on real-time applications. On this paper, a framework is proposed for the design of a supervising MPC to be implemented over a simpler, more reliable internal loop PID Autopilot without making any modifications to its structure. The proposed system was able to add new capabilities to the internal loop by making little corrections to its control demands, ensuring the compliance with restrictions. At the end, the system was able to ensure safe landing over many demanding conditions by effectively expanding the internal autopilots states and by enforcing altitude restrictions.

关键词： Automatic pilots Autoland model predictive control predictive control cage set real-time application program distributed model predictive control nonlinear model predictive control Unmanned Aerial Vehicle drone honey bees Internal control Methods Maximum Allowable Concentration

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On distributed model predictive control for Systems with Decoupled Dynamics

On Distributed Model Predictive Control for Systems with Dec...

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27th Chinese control Conference

作者： Wei Shanbi Chai Yi Ding Baocang Chongqing Univ Coll Automat Chongqing 400030 Peoples R China

ISBN: (纸本)9787900719706

This paper studies distributed model predictive control for the system which is composed of several subsystems with decoupled dynamics. Couplings between different subsystems are manifested by the cost function and constraints. The couplings are described through a connected graph. In this connected graph, each code denotes a subsystem. The cost function and constraints of a code are the functions of its state and the states of its neighbors. The centralize MPC controller for the over system is broken into distinct distributed MPC controllers of smaller size. Each MPC controller is associated to a different node. The distributed MPC and centralize MPC are compared through a numerical example.

关键词： distributed model predictive control Systems with decoupled dynamics Mixed integer linear programming

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Efficient parallel coordinate descent algorithm for convex optimization problems with separable constraints: Application to distributed MPC

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JOURNAL OF PROCESS control 2013年第3期23卷 243-253页

作者： Necoara, Ion Clipici, Dragos Univ Politehn Bucuresti Automat Control & Syst Engn Dept Bucharest 060042 Romania

In this paper we propose a parallel coordinate descent algorithm for solving smooth convex optimization problems with separable constraints that may arise, e.g. in distributed model predictive control (MPC) for linear network systems. Our algorithm is based on block coordinate descent updates in parallel and has a very simple iteration. We prove (sub)linear rate of convergence for the new algorithm under standard assumptions for smooth convex optimization. Further, our algorithm uses local information and thus is suitable for distributed implementations. Moreover, it has low iteration complexity, which makes it appropriate for embedded control. An MPC scheme based on this new parallel algorithm is derived, for which every subsystem in the network can compute feasible and stabilizing control inputs using distributed and cheap computations. For ensuring stability of the MPC scheme, we use a terminal cost formulation derived from a distributed synthesis. Preliminary numerical tests show better performance for our optimization algorithm than other existing methods. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Coordinate descent optimization Parallel algorithm (Sub)linear convergence rate distributed model predictive control Embedded control

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model predictive control for a Sustainable Transport Modal Split at Intermodal Container Hubs

Model Predictive Control for a Sustainable Transport Modal S...

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10th IEEE International Conference on Networking, Sensing and control (ICNSC)

作者： Nabais, Joao Lemos Negenborn, Rudy R. Botto, Miguel Ayala Polytech Inst Setubal IDMEC Dept Informat & Syst Engn Setubal Sch Technol Setubal Portugal Delft Univ Technol Marine & Transport Engn Transport Engn & Logist Delft Netherlands Univ Tecn Lisboa Inst Super Tecn IDMEC Dept Mech Engn Lisbon Portugal

ISBN: (纸本)9781467352000;9781467351980

The increase of international commerce and the expected container vessels capacity with 18, 000 TEU (twenty-foot equivalent unit) will put a considerable pressure on container hubs. High flow peaks will appear at gateway hubs in the transport network compromising the cargo transportation towards the hinterland and decreasing the network transport capacity. Moreover, authorities are forcing transport operators to operate in more sustainable ways. For container hubs this is translated into making a preferable choice for barge and train modalities before opting for truck modality. In this work we present a framework based on model predictive control (MPC) to address the so-called transport modal split problem for the outgoing cargo at container hubs. We use two features (destination and due time) to categorize the cargo present at a container hub and develop a dynamic model to make predictions of cargo volume over time. The controller decision takes into account transporting cargo towards the final destination while opting for sustainable transport modalities. The approach is able to assign cargo in advance to the existing connections at the hub in order to overcome predicted cargo peaks in the future. The framework can also be used to choose between different connection schedules. Giving decision freedom to container hubs is a step towards a synchromodal and more flexible transport network. These statements are illustrated with two simulation examples.

关键词： HUB Transport networks CONTAINERS cargo Freight transport Modal split International trade Sustainable transport transport capacity BARGES destination distributed model predictive control nonlinear model predictive control hinterland model predictive control

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model predictive control of Underwater Gliders Based on a One-layer Recurrent Neural Network

Model Predictive Control of Underwater Gliders Based on a On...

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6th International Conference on Advanced Computational Intelligence (ICACI)

作者： Shan, Yuan Yan, Zheng Wang, Jun Dalian Univ Technol Sch Control Sci & Engn Dalian Liaoning Peoples R China Chinese Univ Hong Kong Dept Mech & Aeronaut Engn Shatin Hong Kong Peoples R China

ISBN: (纸本)9781467363433

In this paper, a motion control problem for underwater gilders in longitudinal plane is considered. A recurrent neural network based model predictive control approach is developed. The model predictive control of underwater gliders is formulated as a time-varying constrained quadratic programming problem, which is solved by using a rcurrent neural network called the simplified dual network in real-time. Simulation results are further presented to show the effectiveness and performance of the proposed model predictive control approach.

关键词： recurrent neural nets model predictive control predictive control distributed model predictive control nonlinear model predictive control Gliders Underwater Motor control

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An Improved MPPT Technique for High Gain DC-DC Converter using model predictive control for Photovoltaic Applications

An Improved MPPT Technique for High Gain DC-DC Converter usi...

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Annual IEEE Applied Power Electronics Conference and Exposition

作者： Mohammad B. Shadmand Mostafa Mosa Robert S. Balog Haitham Abu Rub Renewable Energy & Advanced Power Electronics Research Laboratory Dept of Electrical & Computer Engineering Texas A&M University Dept of Electrical & Computer Engineering Texas A&M University at Qatar

ISBN: (纸本)9781479923250

This paper presents an enhanced Maximum Power Point Tracking (MPPT) of Photovoltaic (PV) systems by means of model predictive control (MPC) techniques. The PV array can feed power to the load through a DC/DC converter boosting the output voltage. Due to stochastic behavior of solar energy, MPPT control technique of PV arrays is required to operate at maximum power point. Extracting the maximum power from PV systems has been widely investigated within the literature. The main contribution of this paper is enhancement of the Incremental Conductance (INC) method through a fixed step predictive control under measured fast solar radiation variation. The proposed predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the selected high gain multilevel DC-DC converter. Comparing the developed technique to the conventional INC method shows significant improvement in PV system performance. Experimental validation is presented using the dSpace CP 1103 to implement the proposed MPC-MPPT.

关键词： maximum power point trackers Maximum power predictive control DC converter photovoltaic array control ring high gain DC to DC converters Electric loads model predictive control Polydnaviridae random behavior Power Points System performance distributed model predictive control

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model predictive control for PMSM with flux-current nonlinear maps

Model predictive control for PMSM with flux-current nonlinea...

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International Symposium on Power Electronics, Electrical Drives, Automation and Motion

作者： Gianluca Angelone Andrea Del Pizzo Ivan Spina Francesco Vasca Department of Engineering University of Sannio Department of Electrical Engineering and Information Technology "Federico II" University

ISBN: (纸本)9781479947485

Typical operating conditions of permanent magnet synchronous motors correspond to nonlinear relationships between fluxes and currents. The determination of equivalent motor parameters is a non trivial task and can be critical for the design of motor controllers, which is typically based on a mathematical model. In this paper an experimental method to determine the flux-current nonlinear maps and a model based approach to implement a predictive controller capable to deal with such nonlinearities is presented. Numerical results show that the optimization technique is able to provide the maximum torque per ampere tracking.

关键词： Permanent magnet synchronous machines nonlinearity peak torque parametric motor predictive control model predictive control maps Operating conditions distributed model predictive control nonlinear model predictive control controller design Ampere

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A Dual Decomposition Algorithm for Separable Nonconvex Optimization Using the Penalty Function Framework

A Dual Decomposition Algorithm for Separable Nonconvex Optim...

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52nd IEEE Annual Conference on Decision and control (CDC)

作者： Quoc Tran Dinh Necoara, Ion Diehl, Moritz Katholieke Univ Leuven Dept Elect Engn ESAT SCD Leuven Belgium Katholieke Univ Leuven Optimizat Engn Ctr OPTEC Leuven Belgium Univ Politehn Bucuresti Automat Control & Syst Engn Dept Bucharest Romania Ecole Polytech Fed Lausanne Lab Informat & Inference Syst LIONS Lausanne Switzerland

ISBN: (纸本)9781467357173

We propose a dual decomposition method for solving separable nonconvex optimization problems that arise e.g. in distributed model predictive control over networks. We first derive a new sequential convex programming (SCP) scheme based on penalty function approach to handle nonconvexity. Then, we combine this SCP scheme with a dual decomposition algorithm to obtain a two-level decomposition algorithm. The global convergence of this algorithm is analyzed under standard assumptions. Some preliminary numerical results are also given to illustrate the theoretical results.

关键词： distributed model predictive control penalty function algorithms decomposition Separable Dual SOUTH CELESTIAL POLE Single cell protein

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Stability of NMPC with cyclic horizons

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IFAC Proceedings Volumes 2013年第23期46卷 809-814页

作者： Markus Kögel Rolf Findeisen Institute for Automation Engineering Otto-von-Guericke-University Magdeburg Germany

In this paper we present stability conditions for nonlinear model predictive control with cyclically varying horizons. Starting from a maximum horizon length, the horizon is reduced by one at each sampling time until a minimum horizon length is reached, at which the horizon is increased to the maximum length. The approach allows to utilize shapes and structures in the terminal constraints, which can otherwise not be handled. Examples are terminal box-constraints, where the terminal set cannot be rendered invariant, or quadratic terminal regions and penalties of diagonal structure. Such constraints are for example of advantage for distributed predictive control problems. To underline the applicability, the approach is used to control a four tank system.

关键词： nonlinear model predictive control distributed model predictive control stability cyclically varying horizons

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Set-point tracking using distributed MPC

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IFAC Proceedings Volumes 2013年第32期46卷 57-62页

作者： Markus Kögel Rolf Findeisen Institute for Automation Engineering Otto-von-Guericke-University Magdeburg Germany

We consider the output tracking of a set-point using cooperative distributed model predictive control. We propose a framework to avoid the loss of feasibility, guarantee stability, constraint satisfaction as well as convergence to admissible set-points. To enable a distributed implementation of the model predictive control law we utilize a cyclic varying horizon length. A simulation example illustrates the approach and its applicability for interconnected systems.

关键词： model predictive control distributed model predictive control Set-point tracking

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