Model Predictive Control Research Papers (original) (raw)

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• • by Cecilia García
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  • Engineering, Model Predictive Control, Process Control, Mathematical Sciences
• • by Bhau Anarase
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  • Mechanical Engineering, Chemical Engineering, Model Predictive Control, State Estimation
• • by Bhau Anarase
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  • Mechanical Engineering, Applied Mathematics, Optimal Control, Model Predictive Control
• • by Bhau Anarase
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  • Mechanical Engineering, Control Systems Engineering, Applied Mathematics, Model Predictive Control
• • by Bhau Anarase
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  • Engineering, Model Predictive Control, Process Control, Mathematical Sciences
• • by Vivek Dua
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  • Engineering, Computational Complexity, Model Predictive Control, Mathematical Sciences
• • by Sherif Zaid
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  • Engineering, Signal Processing, Model Predictive Control, Power System
• • by Panagiotis Patrinos
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  • Applied Mathematics, Supply Chain Management, Globalization, Systems Engineering
• • by Antonio Ferramosca
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  • Model Predictive Control, Robust stability, Constraint Satisfaction, Nonlinear Model
• • by Anthony Tzes
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  • Mechanical Engineering, Model Predictive Control, Manufacturing Engineering, Electrical And Electronic Engineering
• • by Eduardo Gallestey
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  • Mechanical Engineering, Applied Mathematics, Convection, System Identification
• • by Daniel E Rivera
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  • Engineering, Control Systems Engineering, Supply Chain Management, Operations Research
• • by Moritz Diehl
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  • Applied Mathematics, Model Predictive Control, Automatic Control, Dynamic Optimization

This paper presents an integral predictive and nonlinear robust control strategy to solve the path following problem for a quadrotor helicopter. The dynamic motion equations are obtained by the Lagrange–Euler formalism. The proposed... more

This paper presents an integral predictive and nonlinear robust control strategy to solve the path following problem for a quadrotor helicopter. The dynamic motion equations are obtained by the Lagrange–Euler formalism. The proposed control structure is a hierarchical scheme consisting of a model predictive controller (mpc) to track the reference trajectory together with a nonlinear H∞H∞ controller to stabilize the rotational movements. In both controllers the integral of the position error is considered, allowing the achievement of a null steady-state error when sustained disturbances are acting on the system. Simulation results in the presence of aerodynamic disturbances, parametric and structural uncertainties are presented to corroborate the effectiveness and the robustness of the proposed strategy.

• • by Guilherme V . Raffo
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  • Engineering, Model Predictive Control, Modeling, Aerodynamics
• • by Saida Naik
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  • Electrical Engineering, Control Systems Engineering, Renewable Energy, Model Predictive Control
• • by Mohammad Reza Bakhtiari
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  • Chemical Engineering, Optimal Control, Model Predictive Control, Process Control
• • by Tobias Geyer
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  • Power Electronics, Model Predictive Control of Hybrid Systems, Model Predictive Control, Hybrid Systems
• • by M. Ramasamy
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  • Chemical Engineering, Model Predictive Control, Process Control, Operant Conditioning
• • by William Garrard
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  • Engineering, Technology, Model Predictive Control, Vehicle dynamic control
• • by Jing Zhou and +3
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  • Control Systems Engineering, Mathematics, Model Predictive Control, Control Systems
• • by Sohrab Rohani
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  • Chemical Engineering, Model Predictive Control, Quality Control, Linear Model
• • by Jose rodriguez
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  • Engineering, Model Predictive Control, Multilevel Inverter, Control Strategy
• • by Giorgos Papafotiou
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  • Engineering, Power Electronics, Model Predictive Control, Model Predictive Control (MPC)
• • by IOSR Journals
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  • Control Systems Engineering, Model Predictive Control, Control Systems, Optimization
• • by Bhau Anarase
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  • Mechanical Engineering, Control Systems Engineering, Applied Mathematics, Model Predictive Control

Enhancing traffic efficiency and alleviating (even circumventing) traffic congestion with advanced traffic signal control (TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive... more

Enhancing traffic efficiency and alleviating (even circumventing) traffic congestion with advanced traffic signal control (TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control (MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations, and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

• • by IEEE/CAA J. Autom. Sinica
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  • Model Predictive Control, Autonomous Vehicles, Traffic Signal Control, Mixed Integer Programming

A model predictive control (MPC) approach for controlling an active front steering (AFS) system in an autonomous vehicle is presented. At each time step a trajectory is assumed to be known over a finite horizon, and an MPC controller... more

A model predictive control (MPC) approach for controlling an active front steering (AFS) system in an autonomous vehicle is presented. At each time step a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to best follow the desired trajectory on slippery roads at the highest possible entry speed. We start from the results presented in (Int. J. Veh. Auton. Syst. 2005; 3(2–4):265–291; IEEE Trans. Contr. Syst. Technol. 2007; 15(3)) and formulate the MPC problem based on successive online linearization of the nonlinear vehicle model (linear time-varying (LTV) MPC). We present a sufficient stability condition for such LTV MPC scheme. The condition is derived for a general class of nonlinear discrete time systems and results into an additional convex constraint to be included in the LTV MPC design.For the AFS control problem, we compare the proposed LTV MPC scheme with the LTV MPC scheme in (IEEE Trans. Contr. Syst. Technol. 2007; 15(3)) where stability has been enforced with an ad hoc constraint. Simulation and experimental tests up to 17 m/s on icy roads show the effectiveness of the LTV MPC formulation. Copyright © 2007 John Wiley & Sons, Ltd.

• • by Jahan Asgari
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  • Mechanical Engineering, Applied Mathematics, Model Predictive Control, Stability Analysis
• • by Carlos Bordons
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  • Mechanical Engineering, Applied Mathematics, Model Predictive Control, Electrical And Electronic Engineering
• • by Carla Pinheiro
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  • Model Predictive Control, Case Study, Control Application, Real Time Control
• • by Chemistry International
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  • Model Predictive Control, Hydraulic and pneumatic control, Predictive Modeling
• • by José Rodriguez
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  • Engineering, Model Predictive Control, Controller Design, Industrial

Model Predictive Control (MPC) was originally developed for relatively slow processes in the petroleum and chemical industries and is well known to have difficulties in computing control inputs in real time for processes with fast... more

Model Predictive Control (MPC) was originally developed for relatively slow processes in the petroleum and chemical industries and is well known to have difficulties in computing control inputs in real time for processes with fast dynamics. In this paper a novel method called Sam- pling Based Model Predictive Control (SBMPC) is proposed as a "fast" MPC algorithm to generate control

• • by Emmanuel Collins
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  • Model Predictive Control, Nonlinear Programming, Motion Planning, Goal Orientation

Two formulations of a nonlinear model predictive control scheme based on the second-order Volterra series model are presented. The first formulation determines the control action using successive substitution, and the second method... more

Two formulations of a nonlinear model predictive control scheme based on the second-order Volterra series model are presented. The first formulation determines the control action using successive substitution, and the second method directly solves a fourth-order nonlinear ...

• • by Babatunde Ogunnaike
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  • Engineering, Model Predictive Control, Nonlinear Programming, Process Control
• • by Bindiya Kokila
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  • Renewable Energy, Model Predictive Control, Load Frequency control
• • by Tobias Geyer
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  • Model Predictive Control, Model Predictive Control (MPC), Electric motor drives, Ac Motors Drives
• • by reyhaneh khadem
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  • Control Systems Engineering, Renewable Energy, Model Predictive Control, Control Systems
• • by abbas karamodin
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  • Artificial Intelligence, Model Predictive Control, Neural Networks, Structural Control
• • by Tobias Geyer
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  • Engineering, Harmonic Analysis, Optimal Control, Model Predictive Control
• • by Bhau Anarase
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  • Engineering, Signal Processing, Power Electronics, Model Predictive Control

Process control refers to the methods that are used to control and manipulate process variables in manufacturing a product. The main aim of this work is to design and simulate the working of a single input-single output (SISO) tank system... more

Process control refers to the methods that are used to control and manipulate process variables in manufacturing a product. The main aim of this work is to design and simulate the working of a single input-single output (SISO) tank system using model predictive control (MPC) and conventional control and comparing the performances of both systems. The controlled variable is the liquid level in the tank and the manipulated variable is the inlet flow rate of the liquid. Control systems based on the servo and regulatory control schemes are designed and simulated in Scilab. Tuning methods like Ziegler-Nichols, Coon-Cohen, and Tyreus-Luyben are used for the design of conventional controllers (P, PI, and PID). MPC system is designed using Differential Evolution heuristic. From the results obtained, it is revealed that the model predictive control scheme developed was able to control the liquid level in the tank with no offset and a settling time which was considerably lower than those offered by the conventional control schemes. The validated MPC system provides zero offset, better settling times, self-learning control action and incorporation of non-linear models with ease. This model can serve as a base for future improvement studies on the said model predictive control scheme.

• • by Ijariit Journal and +1
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  • Control Systems Engineering, Model Predictive Control, Differential Evolution, Control Systems
• • by Tobias Geyer
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  • Optimal Control, Model Predictive Control, Mathematical Programming, Prediction Model
• • by Ming Kuang
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  • Model Predictive Control, Combustion, Power Management, Energy Management
• • by Costin Sorin Bildea
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  • Chemical Engineering, Model Predictive Control, Energy Saving, LQG
• • by Jose Castellanos
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  • Mechanical Engineering, Cognitive Science, Reinforcement Learning, Model Predictive Control

This paper considers development of a methodology for on-line energy and leakage management in water distribution systems, formulated within model predictive control framework. The approach involves calculation of control actions, i.e.... more

This paper considers development of a methodology for on-line energy and leakage
management in water distribution systems, formulated within model predictive control
framework. The approach involves calculation of control actions, i.e. time schedules for pumps,
valves and sources, to minimize the costs associated with energy used for water pumping and
treatment and water losses due to leakage, whilst satisfying all operational constraints. The
process of computing the control action utilises EPAnet hydraulic simulator, mathematical
modelling language called GAMS and a non-linear programming solver called CONOPT. The
proposed control scheme has been integrated with an industrial SCADA system from ABB
and interfaced with an actual medium-scale water distribution systems being part of Yorkshire
Water Services. The scheme is currently being tested using on-line telemetry data. It has been
operational for over 1 month with 1 hour sampling time and the preliminary results described
in this paper indicate a potential for savings of 30% of the cost of electrical energy.

• • by Hossam Abdelmeguid and +1
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  • Model Predictive Control, Water Distribution Systems, Power System - SCADA and Smart Grid Applications, DC PUMP CONTROLLER
• • by diego carrasco
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  • Model Predictive Control, Model Uncertainty, Feedforward, Noise Measurement
• • by Thomas Badgwell
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  • Chemical Engineering, Model Predictive Control, Process Control, Linear Model
• • by Luigi Glielmo
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  • Model Predictive Control, Vehicle Stability Control, Prediction Model, High Speed

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