| 1. |
- Herceg, Domagoj, et al.
(författare)
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Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry
- 2017
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Ingår i: 2017 25th Mediterranean Conference on Control and Automation, MED 2017. - Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE). - 9781509045334 ; , s. 1361-1366
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Konferensbidrag (refereegranskat)abstract
- The steel industry involves energy-intensive processessuch as combustion processes whose accurate modellingvia first principles is both challenging and unlikely to leadto accurate models let alone cast time-varying dynamics anddescribe the inevitable wear and tear. In this paper we addressthe main objective which is the reduction of energy consumptionand emissions along with the enhancement of the autonomy ofthe controlled process by online modelling and uncertaintyawarepredictive control. We propose a risk-sensitive modelselection procedure which makes use of the modern theoryof risk measures and obtain dynamical models using processdata from our experimental setting: a walking beam furnaceat Swerea MEFOS. We use a scenario-based model predictivecontroller to track given temperature references at the threeheating zones of the furnace and we train a classifier whichpredicts possible drops in the excess of Oxygen in each heatingzone below acceptable levels. This information is then used torecalibrate the controller in order to maintain a high qualityof combustion, therefore, higher thermal efficiency and loweremissions
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| 2. |
- Lindqvist, Björn, et al.
(författare)
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A Scalable Distributed Collision Avoidance Scheme for Multi-agent UAV systems
- 2021
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Ingår i: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE. ; , s. 9212-9218
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Konferensbidrag (refereegranskat)abstract
- In this article we propose a distributed collisionavoidance scheme for multi-agent unmanned aerial vehicles(UAVs) based on nonlinear model predictive control (NMPC),where other agents in the system are considered as dynamicobstacles with respect to the ego agent. Our control schemeoperates at a low level and commands roll, pitch and thrustsignals at a high frequency, each agent broadcasts its predictedtrajectory to the other ones, and we propose an obstacleprioritization scheme based on the shared trajectories to allowup-scaling of the system. The NMPC problem is solved usingan embedded solver generated by Optimization Engine (OpEn)where PANOC is combined with an augmented Lagrangianmethod to compute collision-free trajectories. We evaluate theproposed scheme in several challenging laboratory experimentsfor up to ten aerial agents, in dense aerial swarms.
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| 3. |
- Lindqvist, Björn, et al.
(författare)
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Collision Avoidance for Multiple Micro Aerial Vehicles using Fast Centralized Nonlinear Model Predictive Control
- 2020
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Ingår i: 21th IFAC World Congress. - : Elsevier. ; , s. 9303-9309
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Konferensbidrag (refereegranskat)abstract
- This article proposes a novel control architecture using a centralized nonlinear model predictive control (CNMPC) scheme for controlling multiple micro aerial vehicles (MAVs). The control architecture uses an augmented state system to control multiple agents and performs both obstacle and collision avoidance. The optimization algorithm used is OpEn, based on the proximal averaged Newton type method for optimal control (PANOC) which provides fast convergence for non-convex optimization problems. The objective is to perform position reference tracking for each individual agent, while nonlinear constraints guarantee collision avoidance and smooth control signals. To produce a trajectory that satisfies all constraints a penalty method is applied to the nonlinear constraints. The efficacy of this proposed novel control scheme is successfully demonstrated through simulation results and comparisons, in terms of computation time and constraint violations, which are provided with respect to the number of agents.
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| 4. |
- Mansouri, Sina Sharif
(författare)
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On Visual Area Coverage Using Micro Aerial Vehicles
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The aim of this Licentiate is to advance the field of cooperative visual coverage path planners for multiple Micro Aerial Vehicles (MAVs), while aiming for their real life adoption towards the tasks of aerial infrastructure inspection. The fields that will be addressed are focusing in: a) the collaborative perception of the environment, b) the collaborative visual inspection, and c) the optimization of the aerial missions based on the remaining flying battery, camera constraints, coverage constraints and other real life mission induced constraints.Towards this envisioned aim, this Licentiate will present the following main theoretical contributions: a) centralized and distributed Model Predictive Control (MPC) schemes for the cooperative motion control of MAVs focusing in the establishing of a formation control architecture to enable a dynamic visual sensor from monocular cameras towards a reconfigurable environmental perception, b) revisiting the Cooperative Coverage Path Planning (C-CPP) problem for the inspection of complex infrastructures, c) developing a holistic approach to the problems of 2-D area coverage with MAVs for polygon areas, while considering the camera footprint, and d) designing of a scheme to estimate the Remaining Useful Life (RUL) of the battery during a flight mission, a fact that directly effects the flying capabilities of the MAVs. The theoretical contributions of this thesis have been extensively evaluated in simulation and real life large scale field trials, a direction that adds another contribution of the suggested framework towards the massive insertion of the aerial platforms as aerial tools in the close future.In the first part of this Licentiate, the vision, motivation, open challenges, contributions, and future works are discussed, while in the second part the full articles connected to the presented contributions in this Licentiate are presented in the annex.
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| 5. |
- Mansouri, Sina Sharif, et al.
(författare)
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Subterranean MAV Navigation based on Nonlinear MPC with Collision Avoidance Constraints
- 2020
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Ingår i: 21th IFAC World Congress. - : Elsevier. ; , s. 9650-9657
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Konferensbidrag (refereegranskat)abstract
- Micro Aerial Vehicles (MAVs) navigation in subterranean environments is gaining attention in the field of aerial robotics, however there are still multiple challenges for collision free navigation in such harsh environments. This article proposes a novel baseline solution for collision free navigation with Nonlinear Model Predictive Control (NMPC). In the proposed method, the MAV is considered as a floating object, where the velocities on the x, y axes and the position on altitude are the references for the NMPC to navigate along the tunnel, while the NMPC avoids the collision by considering kinematics of the obstacles based on measurements from a 2D lidar. Moreover, a novel approach for correcting the heading of the MAV towards the center of the mine tunnel is proposed, while the efficacy of the suggested framework has been evaluated in multiple field trials in an underground mine in Sweden.
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| 6. |
- Small, Elias, et al.
(författare)
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Aerial navigation in obstructed environments with embedded nonlinear model predictive control
- 2019
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Ingår i: 2019 18th European Control Conference (ECC). - : IEEE. ; , s. 3556-3563
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Konferensbidrag (refereegranskat)abstract
- We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAVs) using non-linear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A c89 implementation of PANOC solves the NMPC problem at a rate of 20 Hz on board a lab-scale MAV. The MAV performs smooth maneuvers moving around an obstacle. For increased autonomy, we propose a simple method to compensate for the reduction of thrust over time, which comes from the depletion of the MAV's battery, by estimating the thrust constant.
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