Nonlinear Model Predictive Control of a Diesel Engine Air Path: A Comparison of Constraint Handling and Computational Strategies

Huang, Mike; Nakada, Hayato; Butts, Ken; Kolmanovsky, Ilya

doi:10.1016/j.ifacol.2015.11.308

Cited by 64 publications

(35 citation statements)

References 18 publications

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“…The major difficulty in MPC is how constraints are handled within the MPC solver. An overview and benchmark of computationally efficient inequality constraint handling techniques with CGMRES is given in Huang et al (2015). The disadvantage of these simplistic constraint handling techniques, as for example auxiliary variable and logarithmic barrier method, is that a violation leads to an infeasible OCP and accordingly to a crash of the MPC solver.…”

Section: Related Workmentioning

confidence: 99%

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

et al. 2018

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The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this cooperative localization is the requirement of continuous sensor information. Due to a limited sensor perception space, the tracking task to continuously maintain this sensor information is challenging. To address this problem, this contribution is presenting a model predictive control (MPC) approach for such cooperative localization scenarios. In particular, the present work shows a novel workflow to describe sensor limitations with the help of potential functions. In addition, a compact motion model for multi-rotor drones is introduced to achieve MPC real-time capability. The effectiveness of the presented approach is demonstrated in a numerical simulation, an experimental indoor scenario with two quadrotors as well as multiple indoor scenarios of a quadrotor obstacle evasion maneuver.Keywords Localization and navigation in multi-robot systems · Distributed robotic systems operating on land, sea and air · Multi-robot and multi-vehicle motion coordination · Model predictive control · Sensor constrained control · Unmanned aerial vehicle · Quadrotor

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Section: Related Workmentioning

confidence: 99%

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

et al. 2018

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“…One of the efficient methods to solve the resulting OCP in the receding horizon manner can be based on Pontryagin's Minimum Principle. The obtained dual OCP can be solved efficiently in real-time by the Continuation and Generalised Minimal RESidual (C/GMRES) with a proper inequality constraints handling method (for more details see [12,16]). …”

Section: Deadzone Penalty Functionsmentioning

confidence: 99%

Deadzone-Quadratic Penalty Function for Predictive Extended Cruise Control with Experimental Validation

Sajadi-Alamdari

Voos

Darouach

2017

ROBOT 2017: Third Iberian Robotics Conference

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Abstract. Battery Electric Vehicles have high potentials for the modern transportations, however, they are facing limited cruising range. To address this limitation, we present a semi-autonomous ecological driver assistance system to regulate the velocity with energy-efficient techniques. The main contribution of this paper is the design of a real-time nonlinear receding horizon optimal controller to plan the online cost-effective cruising velocity. Instead of conventional 2 -norms, a deadzone-quadratic penalty function for the nonlinear model predictive controller is proposed. Obtained field experimental results demonstrate the effectiveness of the proposed method for a semi-autonomous electric vehicle in terms of real-time energy-efficient velocity regulation and constraints satisfaction.

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“…Its source code is accessible via [15] and its computational efficiency has been validated in hover crafts [16], gasoline engines [17] and eco cruise control scenarios [18]. An overview on related constraint handling methods is given in [19]. A multiple-shooting derivative (MSCGMRES) has been developed in [20] to increase the computational stability.…”

Section: Related Workmentioning

confidence: 99%

“…Accordingly, the OCP is switching again from form (19) to form (18). The resulting change of the quadrotor formation is discussed in section V-B.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…At time t 1 ≈ 8s the first event is provoked by a ROS message which adds a drone (ag 2 ) to the system. Accordingly, also the quadrotor input constraints (c 2 ) and collision avoidance couplings (l 02 , l 12 ) to each of the previous agents (ag 0 , ag 1 ) are added, which results in OCP (19). As all drones are tracking the center point subject to collision avoidance constraints, this results in a triangular formation which is equidistant to the center.…”

Section: B Analysis Of the Experimentsmentioning

confidence: 99%

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Implementation and validation of an event-based real-time nonlinear model predictive control framework with ROS interface for single and multi-robot systems

Dentler

Kannan

Olivares-Méndez

et al. 2017

2017 IEEE Conference on Control Technology and Applications (CCTA)

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Abstract-This paper presents the implementation and experimental validation of a central control framework. The presented framework addresses the need for a controller, which provides high performance combined with a low-computational load while being on-line adaptable to changes in the control scenario. Examples for such scenarios are cooperative control, task-based control and fault-tolerant control, where the system's topology, dynamics, objectives and constraints are changing. The framework combines a fast Nonlinear Model Predictive Control (NMPC), a communication interface with the Robot Operating System (ROS) [1] as well as a modularization that allows an event-based change of the NMPC scenario. To experimentally validate performance and event-based adaptability of the framework, this paper is using a cooperative control scenario of Unmanned Aerial Vehicles (UAV s). The source code of the proposed framework is available under [2].

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Nonlinear Model Predictive Control of a Diesel Engine Air Path: A Comparison of Constraint Handling and Computational Strategies

Cited by 64 publications

References 18 publications

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

Deadzone-Quadratic Penalty Function for Predictive Extended Cruise Control with Experimental Validation

Implementation and validation of an event-based real-time nonlinear model predictive control framework with ROS interface for single and multi-robot systems

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