Pub Date : 2021-06-29DOI: 10.23919/ecc54610.2021.9655119
Faran A. Qureshi, J. Shamma
Wind power is a clean and sustainable source of electricity, but its intermittent nature creates challenges for its grid integration. Utility-scale batteries are a popular and effective solution to make wind power dispatchable, however, batteries are expensive and increase the overall cost of operation. In this paper, we propose utilizing the flexibility in the consumption of smart buildings to reduce the size of the utility-scale batteries needed for the dispatchability of wind energy. A method based on stochastic optimization is proposed to compute the dispatch plan required to participate in the day-ahead energy market. A stochastic model predictive controller is proposed for the control of the buildings and batteries to track the dispatch plan in real-time. Simulations are carried out with realistic building models and real weather, wind power production, and forecast data. Results demonstrate that utilizing the flexibility of building thermodynamics can significantly reduce the size (and usage) of the required battery for making the wind power production dispatchable.
{"title":"Control of Smart Buildings and Utility-Scale Batteries Enabling 24/7 Carbon-Free Energy*","authors":"Faran A. Qureshi, J. Shamma","doi":"10.23919/ecc54610.2021.9655119","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9655119","url":null,"abstract":"Wind power is a clean and sustainable source of electricity, but its intermittent nature creates challenges for its grid integration. Utility-scale batteries are a popular and effective solution to make wind power dispatchable, however, batteries are expensive and increase the overall cost of operation. In this paper, we propose utilizing the flexibility in the consumption of smart buildings to reduce the size of the utility-scale batteries needed for the dispatchability of wind energy. A method based on stochastic optimization is proposed to compute the dispatch plan required to participate in the day-ahead energy market. A stochastic model predictive controller is proposed for the control of the buildings and batteries to track the dispatch plan in real-time. Simulations are carried out with realistic building models and real weather, wind power production, and forecast data. Results demonstrate that utilizing the flexibility of building thermodynamics can significantly reduce the size (and usage) of the required battery for making the wind power production dispatchable.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115792645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.23919/ecc54610.2021.9654848
M. Villani, Ankur Shiledar, Q. Ahmed, G. Rizzoni
In this work we propose an on-line implementable hierarchical strategy merging heuristic and locally optimal features for the energy management of a range-extender electric vehicle. This class of vehicles can operate as pure electric as well as hybrid, with a range-extender internal combustion engine working as a generator. Standard energy management strategies, such as Charge Depleting/Charge Sustaining and Equivalent Consumption Minimization Strategy (ECMS), show several limitations when applied to range-extender electric powertrains. These limitations can include excessive engine start/stops, potential cold starts, distance from optimal fuel consumption, inefficient usage of the grid energy. Therefore, in this work the energy management problem has been split over two levels: at the higher level, a heuristic rule-based controller is used to decide when to operate the vehicle as pure electric; at the lower level, the ECMS is implemented to locally manage the powertrain energy flows whenever the vehicle is in hybrid mode. The lower level controller is implemented in three different variants in the effort of finding a trade-off between fuel consumption, emissions, drivability and efficient battery recharge. The performance of the hierarchical energy management strategy is numerically evaluated for a range-extender Class 6 delivery truck and compared to pure ECMS. The results show the ability of the proposed approach to ensure near-optimal fuel economy with an acceptable number of start/stops of the range-extender.
{"title":"Design of a Hierarchical Energy Management Strategy for a Range-Extender Electric Delivery Truck","authors":"M. Villani, Ankur Shiledar, Q. Ahmed, G. Rizzoni","doi":"10.23919/ecc54610.2021.9654848","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9654848","url":null,"abstract":"In this work we propose an on-line implementable hierarchical strategy merging heuristic and locally optimal features for the energy management of a range-extender electric vehicle. This class of vehicles can operate as pure electric as well as hybrid, with a range-extender internal combustion engine working as a generator. Standard energy management strategies, such as Charge Depleting/Charge Sustaining and Equivalent Consumption Minimization Strategy (ECMS), show several limitations when applied to range-extender electric powertrains. These limitations can include excessive engine start/stops, potential cold starts, distance from optimal fuel consumption, inefficient usage of the grid energy. Therefore, in this work the energy management problem has been split over two levels: at the higher level, a heuristic rule-based controller is used to decide when to operate the vehicle as pure electric; at the lower level, the ECMS is implemented to locally manage the powertrain energy flows whenever the vehicle is in hybrid mode. The lower level controller is implemented in three different variants in the effort of finding a trade-off between fuel consumption, emissions, drivability and efficient battery recharge. The performance of the hierarchical energy management strategy is numerically evaluated for a range-extender Class 6 delivery truck and compared to pure ECMS. The results show the ability of the proposed approach to ensure near-optimal fuel economy with an acceptable number of start/stops of the range-extender.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125254129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.23919/ecc54610.2021.9655201
G. B. Cáceres, P. Millán, M. Pereira, D. Lozano
The joint effects of rise of global population, climate change and water scarcity makes the shift towards an efficient and sustainable agriculture more and more urgent. Fortunately, recent developments in low-cost, IoT-based sensors and actuators can help us to incorporate advanced control techniques for efficient irrigation system. This paper proposes the use of an economic model predictive control at a farm scale. The controller makes use of soil moisture data sent by the sensors, price signals, operative restrictions, and accurate dynamical models of water dynamics in the soil. Its performance is demonstrated through simulations based on a real case-study, showing that it is possible to obtain significant reductions in water and energy consumption and operation costs.
{"title":"Economic Model Predictive Control for Smart and Sustainable Farm Irrigation*","authors":"G. B. Cáceres, P. Millán, M. Pereira, D. Lozano","doi":"10.23919/ecc54610.2021.9655201","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9655201","url":null,"abstract":"The joint effects of rise of global population, climate change and water scarcity makes the shift towards an efficient and sustainable agriculture more and more urgent. Fortunately, recent developments in low-cost, IoT-based sensors and actuators can help us to incorporate advanced control techniques for efficient irrigation system. This paper proposes the use of an economic model predictive control at a farm scale. The controller makes use of soil moisture data sent by the sensors, price signals, operative restrictions, and accurate dynamical models of water dynamics in the soil. Its performance is demonstrated through simulations based on a real case-study, showing that it is possible to obtain significant reductions in water and energy consumption and operation costs.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125504494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.23919/ecc54610.2021.9655165
Hakan Basargan, András Mihály, P. Gáspár, O. Sename
Multiple semi-active suspension control systems have been studied and adapted to vehicles in the past. Many of these systems work with actual road conditions, while oncoming road conditions are not considered. This paper presents a method to integrate look-ahead road information in the adaptive semi-active suspension control with energy-efficient cruise control. Oncoming road conditions and categories are known by using a global positioning system and historic road information. The control configuration has been designed with the Linear Parameter Varying framework. The behavior of the controller can be modified by the use of a dedicated scheduling variable, which is defined by considering a look-ahead estimation algorithm based on prehistoric simulations of passive suspension. The operation of the integrated adaptive semi-active suspension system is demonstrated through real-time simulation in the TruckSim environment with real geographical data. In order to prove the effectiveness of the proposed method two different simulations have been evaluated and compared: one with conventional semi-active suspension and another with an adaptive semi-active suspension. The simulation results show that the overall performance in road holding, suspension deflection and ride comfort has been improved, which effectively demonstrates the advantage of presented adaptive semi-active suspension control based on look-ahead information.
{"title":"Road adaptive semi-active suspension and cruise control through LPV technique","authors":"Hakan Basargan, András Mihály, P. Gáspár, O. Sename","doi":"10.23919/ecc54610.2021.9655165","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9655165","url":null,"abstract":"Multiple semi-active suspension control systems have been studied and adapted to vehicles in the past. Many of these systems work with actual road conditions, while oncoming road conditions are not considered. This paper presents a method to integrate look-ahead road information in the adaptive semi-active suspension control with energy-efficient cruise control. Oncoming road conditions and categories are known by using a global positioning system and historic road information. The control configuration has been designed with the Linear Parameter Varying framework. The behavior of the controller can be modified by the use of a dedicated scheduling variable, which is defined by considering a look-ahead estimation algorithm based on prehistoric simulations of passive suspension. The operation of the integrated adaptive semi-active suspension system is demonstrated through real-time simulation in the TruckSim environment with real geographical data. In order to prove the effectiveness of the proposed method two different simulations have been evaluated and compared: one with conventional semi-active suspension and another with an adaptive semi-active suspension. The simulation results show that the overall performance in road holding, suspension deflection and ride comfort has been improved, which effectively demonstrates the advantage of presented adaptive semi-active suspension control based on look-ahead information.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131877212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.23919/ecc54610.2021.9654966
D. Lupu, I. Necoara
We propose a stochastic higher-order algorithm for solving finite sum convex optimization problems. Our algorithmic framework is based on the notion of stochastic higher-order upper bound approximations of the finite sum objective function. For building such a framework we only require that this bound approximate the objective function up to an error that is p times differentiable and has a Lipschitz continuous p derivative. This leads to a stochastic higher-order majorization-minimization algorithm, which we call SHOM. We show that the algorithm SHOM achieves local linear convergence rate for the function values provided that the finite sum objective function is uniformly convex. Numerical simulations confirm the efficiency of our method.
{"title":"Local linear convergence of stochastic higher-order methods for convex optimization","authors":"D. Lupu, I. Necoara","doi":"10.23919/ecc54610.2021.9654966","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9654966","url":null,"abstract":"We propose a stochastic higher-order algorithm for solving finite sum convex optimization problems. Our algorithmic framework is based on the notion of stochastic higher-order upper bound approximations of the finite sum objective function. For building such a framework we only require that this bound approximate the objective function up to an error that is p times differentiable and has a Lipschitz continuous p derivative. This leads to a stochastic higher-order majorization-minimization algorithm, which we call SHOM. We show that the algorithm SHOM achieves local linear convergence rate for the function values provided that the finite sum objective function is uniformly convex. Numerical simulations confirm the efficiency of our method.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132379837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.23919/ecc54610.2021.9655046
R. Dyrska, R. Mitze, M. Mönnigmann
We propose a method for determining fallback control laws for nonlinear MPC problems. A fallback strategy is needed whenever a nonlinear MPC problem cannot be solved in due time. In this paper, we use the concept of regional MPC to compute local control laws that mimic the solution expected from the original problem. In regional MPC, the piecewise affine structure of the solution of linear-quadratic optimization problems is exploited by computing the current polytope and control law online. We use the regional control law, which is naturally stabilizing for more states than its corresponding polytope, and dismiss its region. In every time step, we use the solution of the original MPC problem as a basis to determine the regional control law. If in one time step no solution can be found, the regional control law from the previous time step is used as a fallback strategy. We show the effectiveness with an example of a wind turbine.
{"title":"Regional Control Laws as Fallback Strategy for Nonlinear MPC: Application to Wind Turbine Control *","authors":"R. Dyrska, R. Mitze, M. Mönnigmann","doi":"10.23919/ecc54610.2021.9655046","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9655046","url":null,"abstract":"We propose a method for determining fallback control laws for nonlinear MPC problems. A fallback strategy is needed whenever a nonlinear MPC problem cannot be solved in due time. In this paper, we use the concept of regional MPC to compute local control laws that mimic the solution expected from the original problem. In regional MPC, the piecewise affine structure of the solution of linear-quadratic optimization problems is exploited by computing the current polytope and control law online. We use the regional control law, which is naturally stabilizing for more states than its corresponding polytope, and dismiss its region. In every time step, we use the solution of the original MPC problem as a basis to determine the regional control law. If in one time step no solution can be found, the regional control law from the previous time step is used as a fallback strategy. We show the effectiveness with an example of a wind turbine.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133754605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.23919/ECC54610.2021.9655123
Christine Kloock, Herbert Werner
This paper presents an approach that bases on a matrix providing and supporting the time efficient access of agent related data in a multi-agent scenario, e.g. distributed model predictive control. Under some assumptions, the matrix possesses the potential of improving the amount of time required for collecting the required data compared to a basic search routine. Due to the special structure, the matrix can be derived dynamically and can be adapted to a changing number of agents.
{"title":"Dynamic and Time Efficient Data Assignment via the Pointers Matrix","authors":"Christine Kloock, Herbert Werner","doi":"10.23919/ECC54610.2021.9655123","DOIUrl":"https://doi.org/10.23919/ECC54610.2021.9655123","url":null,"abstract":"This paper presents an approach that bases on a matrix providing and supporting the time efficient access of agent related data in a multi-agent scenario, e.g. distributed model predictive control. Under some assumptions, the matrix possesses the potential of improving the amount of time required for collecting the required data compared to a basic search routine. Due to the special structure, the matrix can be derived dynamically and can be adapted to a changing number of agents.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133857708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.23919/ecc54610.2021.9655141
Mohamad Al Ahdab, H. Clausen, T. Knudsen, T. B. Aradóttir, S. Schmidt, K. Nørgaard, J. Leth
A least squares strategy to estimate states and parameters for type 2 diabetes (T2D) patients based only on continuous glucose measurements and injected insulin in the presence of unannounced meals and disturbances, e.g., physical activity and stress, is presented. The strategy is based on a simple T2D patient model and tested with clinical data in addition to simulated data generated by using jump diffusion models for meals and disturbances. Three parameters are estimated together with the states, meals, and disturbances. The estimated meal states were shown to follow the trend of the unannounced meals. The strategy can be used to obtain a model with the estimated parameters for predictive control design. In addition, the strategy can also be used to test different insulin and meal plans with the estimated disturbances and parameters. Moreover, the paper demonstrates the ability of jump diffusion models to simulate meals and disturbances.
{"title":"Parameter Estimation in Type 2 Diabetes in the Presence of Unannounced Meals and Unmodelled Disturbances*","authors":"Mohamad Al Ahdab, H. Clausen, T. Knudsen, T. B. Aradóttir, S. Schmidt, K. Nørgaard, J. Leth","doi":"10.23919/ecc54610.2021.9655141","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9655141","url":null,"abstract":"A least squares strategy to estimate states and parameters for type 2 diabetes (T2D) patients based only on continuous glucose measurements and injected insulin in the presence of unannounced meals and disturbances, e.g., physical activity and stress, is presented. The strategy is based on a simple T2D patient model and tested with clinical data in addition to simulated data generated by using jump diffusion models for meals and disturbances. Three parameters are estimated together with the states, meals, and disturbances. The estimated meal states were shown to follow the trend of the unannounced meals. The strategy can be used to obtain a model with the estimated parameters for predictive control design. In addition, the strategy can also be used to test different insulin and meal plans with the estimated disturbances and parameters. Moreover, the paper demonstrates the ability of jump diffusion models to simulate meals and disturbances.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"38 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132767873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.23919/ecc54610.2021.9655162
M. Morato, J. Normey-Rico, O. Sename
This paper develops a novel Linear Parameter Varying (LPV) Model Predictive Control (MPC) algorithm for Semi-Active Suspension systems. The current state-of-the-art comprises two possible implementations: a) to consider the future variations of the LPV scheduling variables as uncertainties, thereby solving a robust optimization, which is usually time-consuming; or b) to estimate the future scheduling variables and solve a sub-optimal quadratic program, which can be evaluated rapidly. This paper proposes a control paradigm in between these paths, considering a robust min-max procedure with small predictions horizons, being implementable within the short 5ms sampling period of the suspension system. The method includes terminal ingredients, derived via LMIs, that ensure input-to-state stability and recursive feasibility. Realistic simulations show the effectiveness of the proposed method, when compared against a nonlinear MPC and a sub-optimal LPV MPC. The results show that the method is indeed able to run in real-time (in the order of milliseconds), almost as fast as the sub-optimal MPC, while still guaranteeing good safety and comfort performances for the vehicle.
{"title":"Short-Sighted Robust LPV Model Predictive Control: Application to Semi-Active Suspension Systems","authors":"M. Morato, J. Normey-Rico, O. Sename","doi":"10.23919/ecc54610.2021.9655162","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9655162","url":null,"abstract":"This paper develops a novel Linear Parameter Varying (LPV) Model Predictive Control (MPC) algorithm for Semi-Active Suspension systems. The current state-of-the-art comprises two possible implementations: a) to consider the future variations of the LPV scheduling variables as uncertainties, thereby solving a robust optimization, which is usually time-consuming; or b) to estimate the future scheduling variables and solve a sub-optimal quadratic program, which can be evaluated rapidly. This paper proposes a control paradigm in between these paths, considering a robust min-max procedure with small predictions horizons, being implementable within the short 5ms sampling period of the suspension system. The method includes terminal ingredients, derived via LMIs, that ensure input-to-state stability and recursive feasibility. Realistic simulations show the effectiveness of the proposed method, when compared against a nonlinear MPC and a sub-optimal LPV MPC. The results show that the method is indeed able to run in real-time (in the order of milliseconds), almost as fast as the sub-optimal MPC, while still guaranteeing good safety and comfort performances for the vehicle.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121046277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.23919/ecc54610.2021.9655166
D. Saccani, L. Fagiano
A novel model predictive control (MPC) formulation, named multi-trajectory MPC (mt-MPC), is presented and applied to the problem of autonomous navigation of an unmanned aerial vehicle (UAV) in an unknown environment. The UAV is equipped with a LiDAR sensor, providing only a partial description of the surroundings and resulting in time-varying constraints as the vehicle navigates among the obstacles. The control system layout is hierarchical: the low-level loops stabilize the vehicle’s trajectories and track the set-points commanded by the high-level, mt-MPC controller. The latter is required to plan the UAV trajectory trading off safety, i.e. to avoid collisions with the uncertain obstacles, and exploitation, i.e. to reach an assigned target location. To achieve this goal, mt-MPC considers different future state trajectories in the same Finite Horizon Optimal Control Problem (FHOCP), enabling a partial decoupling between constraint satisfaction (safety) and cost function minimization (exploitation). Recursive feasibility and, consequently, persistent obstacle avoidance guarantees are derived under the assumption of a time invariant environment. The performance of the approach is studied in simulation and compared with that of a standard MPC, showing good improvement.
{"title":"Autonomous UAV Navigation in an Unknown Environment via Multi-Trajectory Model Predictive Control","authors":"D. Saccani, L. Fagiano","doi":"10.23919/ecc54610.2021.9655166","DOIUrl":"https://doi.org/10.23919/ecc54610.2021.9655166","url":null,"abstract":"A novel model predictive control (MPC) formulation, named multi-trajectory MPC (mt-MPC), is presented and applied to the problem of autonomous navigation of an unmanned aerial vehicle (UAV) in an unknown environment. The UAV is equipped with a LiDAR sensor, providing only a partial description of the surroundings and resulting in time-varying constraints as the vehicle navigates among the obstacles. The control system layout is hierarchical: the low-level loops stabilize the vehicle’s trajectories and track the set-points commanded by the high-level, mt-MPC controller. The latter is required to plan the UAV trajectory trading off safety, i.e. to avoid collisions with the uncertain obstacles, and exploitation, i.e. to reach an assigned target location. To achieve this goal, mt-MPC considers different future state trajectories in the same Finite Horizon Optimal Control Problem (FHOCP), enabling a partial decoupling between constraint satisfaction (safety) and cost function minimization (exploitation). Recursive feasibility and, consequently, persistent obstacle avoidance guarantees are derived under the assumption of a time invariant environment. The performance of the approach is studied in simulation and compared with that of a standard MPC, showing good improvement.","PeriodicalId":105499,"journal":{"name":"2021 European Control Conference (ECC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117269701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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