Browsing by Keyword "Model Predictive Control"
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Item A Comparison Between Coupled and Decoupled Vehicle Motion Controllers Based on Prediction Models(IEEE, 2019-06) Matute, Jose A.; Lattarulo, Ray; Zubizarreta, Asier; Perez, Joshue; CCAMIn this work, a comparative study is carried out with two different predictive controllers that consider the longitudinal jerk and steering rate change as additional parameters, as additional parameters, so that comfort constraints can be included. Furthermore, the approaches are designed so that the effect of longitudinal and lateral motion control coupling can be analyzed. This way, the first controller is a longitudinal and lateral coupled MPC approach based on a kinematic model of the vehicle, while the second is a decoupled strategy based on a triple integrator model based on MPC for the longitudinal control and a double proportional curvature control for the lateral motion control. The control architecture and motion planning are exhaustively explained. The comparative study is carried out using a test vehicle, whose dynamics and low-level controllers have been simulated using the realistic simulation environment Dynacar. The performed tests demonstrate the effectiveness of both approaches in speeds higher than 30 km/h, and demonstrate that the coupled strategy provides better performance than the decoupled one. The relevance of this work relies in the contribution of vehicle motion controllers considering the comfort and its advantage over decoupled alternatives for future implementation in real vehicles.Item Integrated model concept for district energy management optimisation platforms(2021-09) Sánchez, Víctor F.; Garrido Marijuan, Antonio; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVADistrict heating systems play a key role in reducing the aggregated heating and domestic hot water production energy consumption of European building stock. However, the operational strategies of these systems present further optimisation potential, as most of them are still operated according to reactive control strategies. To fully exploit the optimisation potential of these systems, their operations should instead be based on model predictive control strategies implemented through dedicated district energy management platforms. This paper describes a multiscale and multidomain integrated district model concept conceived to serve as the basis of an energy prediction engine for the district energy management platform developed in the framework of the MOEEBIUS project. The integrated district model is produced by taking advantage of co-simulation techniques to couple building (EnergyPlus) and district heating system (Modelica) physics-based models, while exploiting the potential provided by the functional mock-up interface standard. The district demand side is modelled through the combined use of physical building models and data-driven models developed through supervised machine learning techniques. Additionally, district production-side infrastructure modelling is simplified through a new Modelica library designed to allow a subsystem-based district model composition, reducing the time required for model development. The integrated district model and new Modelica library are successfully tested in the Stepa Stepanovic subnetwork of the city of Belgrade, demonstrating their capacity for evaluating the energy savings potential available in existing district heating systems, with a reduction of up to 21% of the aggregated subnetwork energy input and peak load reduction of 24.6%.Item Longitudinal Model Predictive Control with comfortable speed planner(IEEE, 2018-06-06) Matute, Jose A.; Marcano, Mauricio; Zubizarreta, Asier; Perez, Joshue; Calado, Joao; Bento, Luis Conde; Oliveira, Paulo; Costelha, Hugo; Lopes, Nuno; CCAMGuaranteeing simplicity and safety is a real challenge of Advanced Driver Assistance Systems (ADAS), being these aspects necessary for the development of decision and control stages in highly automated vehicles. Considering that a human-centered design is generally pursued, exploring comfort boundaries in passenger vehicles has a significant importance. This work aims to implement a simple Model Predictive Control (MPC) for longitudinal maneuvers, considering a bare speed planner based on the curvature of a predefined geometrical path. The speed profiles are constrained with a maximum value at any time, in such way that total accelerations are lower than specified constraint limits. A double proportional with curvature bias control was employed as a simple algorithm for lateral maneuvers. The tests were performed within a realistic simulation environment with a virtual vehicle model based on a multi-body formulation. The results of this investigation permits to determine the capabilities of simplified control algorithms in real scenarios, and comprehend how to improve them to be more efficient.Item Low Speed Longitudinal Control Algorithms for Automated Vehicles in Simulation and Real Platforms(2018) Marcano, Mauricio; Matute, José A.; Lattarulo, Ray; Martí, Enrique; Pérez, Joshué; CCAM; Tecnalia Research & InnovationAdvanced Driver Assistance Systems (ADAS) acting over throttle and brake are already available in level 2 automated vehicles. In order to increase the level of automation new systems need to be tested in an extensive set of complex scenarios, ensuring safety under all circumstances. Validation of these systems using real vehicles presents important drawbacks: the time needed to drive millions of kilometers, the risk associated with some situations, and the high cost involved. Simulation platforms emerge as a feasible solution.Therefore, robust and reliable virtual environments to test automated driving maneuvers and control techniques are needed. In that sense, this paper presents a use case where three longitudinal low speed control techniques are designed, tuned, and validated using an in-house simulation framework and later applied in a real vehicle. Control algorithms include a classical PID, an adaptive network fuzzy inference system (ANFIS), and a Model Predictive Control (MPC). The simulated dynamics are calculated using a multibody vehicle model. In addition, longitudinal actuators of a Renault Twizy are characterized through empirical tests. A comparative analysis of results between simulated and real platform shows the effectiveness of the proposed framework for designing and validating longitudinal controllers for real automated vehicles.Item Numerical Simulation of Control Strategies at Mutriku Wave Power Plant(American Society of Mechanical Engineers (ASME), 2018) Faÿ, François-Xavier; Kelly, James; Henriques, João; Pujana, Ainhoa; Abusara, Mohammad; Mueller, Markus; Touzon, Imanol; Ruiz-Minguela, Pablo; Tecnalia Research & Innovation; RENOVABLES EFICIENCIA ENERGETICA Y CIRCULARIDAD; DIGITAL ENERGY; RENOVABLES OFFSHOREIn order to de-risk wave energy technologies and bring confidence to the sector, it is necessary to gain experience and collect data from sea trials. As part of the OPERA H2020 project, the Mutriku Wave Power Plant (MWPP) is being used as a real condition laboratory for the experiment of innovative technologies. The plant is situated in the North shore of Spain and has been operating since 2011. It uses the Oscillating Water Column (OWC) principle, which consists in compressing and expanding the air trapped in a chamber due to the inner free-surface oscillation resulting from the incident waves. The pressure difference between the air chamber and the atmosphere is used to drive an air turbine. In that case, a self-rectifying air turbine is the best candidate for the energy conversion, as it produces a unidirectional torque in presence of a bi-directional flow. The power take-off system installed is composed of a biradial turbine connected to a 30kW off-the-shelf squirrel cage generator. One of the novelties of the turbine is a high-speed stop-valve installed close to the rotor. The valve may be used to control the flow rate through the turbine or for latching control. This paper focuses on the development, the implementation and the numerical simulation of five control strategies including turbine speed and generator torque controllers. The algorithms were designed thanks to a numerical model describing one of the OWC chambers of the Mutriku power plant. Numerical results are presented for a variety of sea states and a comparison between the proposed control laws in terms of energy production and power quality is performed.