Pub Date : 2017-05-24DOI: 10.23919/ACC.2017.7963738
Andrew P. Sabelhaus, Abishek K. Akella, Z. A. Ahmad, Vytas SunSpiral
The Underactuated Lightweight Tensegrity Robotic Assistive Spine (ULTRA Spine) project is an ongoing effort to develop a flexible, actuated backbone for quadruped robots. In this work, model-predictive control is used to track a trajectory in the robot's state space, in simulation. This is the first work that tracks an arbitrary trajectory, in closed-loop, in the state space of a spine-like tensegrity robot. The state trajectory used here corresponds to a bending motion of the spine, with translations and rotations of the three moving vertebrae. The controller uses a linearized model of the system dynamics, computed at each timestep, and has both constraints and weighted penalties to reduce linearization errors. For this robot, which measures 26cm × 26cm × 45cm, the tracking errors converge to less than 0.5cm even with disturbances, indicating that the controller is stable and could be used on a physical robot in future work.
{"title":"Model-Predictive Control of a flexible spine robot","authors":"Andrew P. Sabelhaus, Abishek K. Akella, Z. A. Ahmad, Vytas SunSpiral","doi":"10.23919/ACC.2017.7963738","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963738","url":null,"abstract":"The Underactuated Lightweight Tensegrity Robotic Assistive Spine (ULTRA Spine) project is an ongoing effort to develop a flexible, actuated backbone for quadruped robots. In this work, model-predictive control is used to track a trajectory in the robot's state space, in simulation. This is the first work that tracks an arbitrary trajectory, in closed-loop, in the state space of a spine-like tensegrity robot. The state trajectory used here corresponds to a bending motion of the spine, with translations and rotations of the three moving vertebrae. The controller uses a linearized model of the system dynamics, computed at each timestep, and has both constraints and weighted penalties to reduce linearization errors. For this robot, which measures 26cm × 26cm × 45cm, the tracking errors converge to less than 0.5cm even with disturbances, indicating that the controller is stable and could be used on a physical robot in future work.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128822098","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 : 2017-05-24DOI: 10.23919/ACC.2017.7962961
Yi-Rou Chen, Cheng-Yuan Chang, S. Kuo
This paper presents the development of active noise control (ANC) for earphones, which uses natural sound for estimating the secondary path model instead of extra random noise. Real-time experiments are conducted to evaluate the performance of the developed ANC earphones using the microphone inside KEMAR's ear. Experimental results show the developed light-weight ANC earphones achieve higher noise reduction in wider frequency range than the commercial ANC headphones and earphone, and natural sound can be used to replace annoying white noise as an excitation signal for adaptive identification of secondary path required by ANC system.
{"title":"Active noise control and secondary path modeling algorithms for earphones","authors":"Yi-Rou Chen, Cheng-Yuan Chang, S. Kuo","doi":"10.23919/ACC.2017.7962961","DOIUrl":"https://doi.org/10.23919/ACC.2017.7962961","url":null,"abstract":"This paper presents the development of active noise control (ANC) for earphones, which uses natural sound for estimating the secondary path model instead of extra random noise. Real-time experiments are conducted to evaluate the performance of the developed ANC earphones using the microphone inside KEMAR's ear. Experimental results show the developed light-weight ANC earphones achieve higher noise reduction in wider frequency range than the commercial ANC headphones and earphone, and natural sound can be used to replace annoying white noise as an excitation signal for adaptive identification of secondary path required by ANC system.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117025621","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963682
Ying Shi, K. Smith, R. Zane, Dyche Anderson
Lithium-ion battery packs take a major part of large-scale stationary energy storage systems. One challenge in reducing battery pack cost is to reduce pack size without compromising pack service performance and lifespan. Prognostic life model can be a powerful tool to handle the state of health (SOH) estimate and enable active life balancing strategy to reduce cell imbalance and extend pack life. This work proposed a life model using both empirical and physical-based approaches. The life model described the compounding effect of different degradations on the entire cell with an empirical model. Then its lower-level submodels considered the complex physical links between testing statistics (state of charge level, C-rate level, duty cycles, etc.) and the degradation reaction rates with respect to specific aging mechanisms. The hybrid approach made the life model generic, robust and stable regardless of battery chemistry and application usage. The model was validated with a custom pack with both passive and active balancing systems implemented, which created four different aging paths in the pack. The life model successfully captured the aging trajectories of all four paths. The life model prediction errors on capacity fade and resistance growth were within ±3% and ±5% of the experiment measurements.
{"title":"Life prediction of large lithium-ion battery packs with active and passive balancing","authors":"Ying Shi, K. Smith, R. Zane, Dyche Anderson","doi":"10.23919/ACC.2017.7963682","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963682","url":null,"abstract":"Lithium-ion battery packs take a major part of large-scale stationary energy storage systems. One challenge in reducing battery pack cost is to reduce pack size without compromising pack service performance and lifespan. Prognostic life model can be a powerful tool to handle the state of health (SOH) estimate and enable active life balancing strategy to reduce cell imbalance and extend pack life. This work proposed a life model using both empirical and physical-based approaches. The life model described the compounding effect of different degradations on the entire cell with an empirical model. Then its lower-level submodels considered the complex physical links between testing statistics (state of charge level, C-rate level, duty cycles, etc.) and the degradation reaction rates with respect to specific aging mechanisms. The hybrid approach made the life model generic, robust and stable regardless of battery chemistry and application usage. The model was validated with a custom pack with both passive and active balancing systems implemented, which created four different aging paths in the pack. The life model successfully captured the aging trajectories of all four paths. The life model prediction errors on capacity fade and resistance growth were within ±3% and ±5% of the experiment measurements.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127444046","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 : 2017-05-24DOI: 10.23919/ACC.2017.7962928
Achin Jain, Madhur Behl, R. Mangharam
Decisions on how to best optimize energy systems operations are becoming ever so complex and conflicting, that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC in buildings, is the cost, time, and effort associated with learning first-principles based dynamical models of the underlying physical system. This paper introduces an alternative approach for implementing finite-time receding horizon control using control-oriented data-driven models. We call this approach Data Predictive Control (DPC). Specifically, by utilizing separation of variables, two novel algorithms for implementing DPC using a single regression tree and with regression trees ensembles (random forest) are presented. The data predictive controller enables the building operator to trade off energy consumption against thermal comfort without having to learn white/grey box models of the systems dynamics. We present a comprehensive numerical study which compares the performance of DPC with an MPC based energy management strategy, using a single zone building model. Our results demonstrate that performance of DPC is comparable to an MPC controller, with only 3.8% additional cost in terms of optimal objective function and within 95% in terms of R2 score, thereby making it an alluring alternative to MPC, whenever the associated cost of learning the model is high.
{"title":"Data Predictive Control for building energy management","authors":"Achin Jain, Madhur Behl, R. Mangharam","doi":"10.23919/ACC.2017.7962928","DOIUrl":"https://doi.org/10.23919/ACC.2017.7962928","url":null,"abstract":"Decisions on how to best optimize energy systems operations are becoming ever so complex and conflicting, that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC in buildings, is the cost, time, and effort associated with learning first-principles based dynamical models of the underlying physical system. This paper introduces an alternative approach for implementing finite-time receding horizon control using control-oriented data-driven models. We call this approach Data Predictive Control (DPC). Specifically, by utilizing separation of variables, two novel algorithms for implementing DPC using a single regression tree and with regression trees ensembles (random forest) are presented. The data predictive controller enables the building operator to trade off energy consumption against thermal comfort without having to learn white/grey box models of the systems dynamics. We present a comprehensive numerical study which compares the performance of DPC with an MPC based energy management strategy, using a single zone building model. Our results demonstrate that performance of DPC is comparable to an MPC controller, with only 3.8% additional cost in terms of optimal objective function and within 95% in terms of R2 score, thereby making it an alluring alternative to MPC, whenever the associated cost of learning the model is high.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126241490","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963161
Cassiano O. Becker, V. Preciado
Subspace identification methods provide a reliable set of methods to recover system parameters of linear dynamical systems based on the observation of their inputs and outputs. However, in the common case where one does not have access to the inputs, the identification problem becomes harder, and is referred to as blind system identification. On the other hand, if the inputs can be assumed to lie on a known subspace, identification techniques based on low-rank matrix recovery can be applied. In this case, blind subspace system identification has been formulated as the problem of simultaneously recovering structured low-rank matrices associated with both the system and inputs. Notwithstanding, the convex relaxation approach to this problem, where the objective function is defined as a sum of the nuclear norms of two matrices, has been shown to be significantly sub-optimal as it typically favors one of the objective terms. In this work, we propose a method for the joint identification of system and inputs using optimization over Riemann manifolds. Riemannian optimization defines operators that allow low-rank matrix constraints to be incorporated in the search space, producing feasible solutions by construction. Our approach takes advantage of this capability and formulates blind subsystem identification as a low-rank matrix approximation problem over the product manifold of fixed-rank matrices.
{"title":"Blind subspace system identification with Riemannian optimization","authors":"Cassiano O. Becker, V. Preciado","doi":"10.23919/ACC.2017.7963161","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963161","url":null,"abstract":"Subspace identification methods provide a reliable set of methods to recover system parameters of linear dynamical systems based on the observation of their inputs and outputs. However, in the common case where one does not have access to the inputs, the identification problem becomes harder, and is referred to as blind system identification. On the other hand, if the inputs can be assumed to lie on a known subspace, identification techniques based on low-rank matrix recovery can be applied. In this case, blind subspace system identification has been formulated as the problem of simultaneously recovering structured low-rank matrices associated with both the system and inputs. Notwithstanding, the convex relaxation approach to this problem, where the objective function is defined as a sum of the nuclear norms of two matrices, has been shown to be significantly sub-optimal as it typically favors one of the objective terms. In this work, we propose a method for the joint identification of system and inputs using optimization over Riemann manifolds. Riemannian optimization defines operators that allow low-rank matrix constraints to be incorporated in the search space, producing feasible solutions by construction. Our approach takes advantage of this capability and formulates blind subsystem identification as a low-rank matrix approximation problem over the product manifold of fixed-rank matrices.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125161683","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963091
Sebastian Bernhard, J. Adamy
We address static decoupling control for linear over-actuated systems and time-varying references given by exogenous systems with arbitrary eigenvalues. Based on mild assumptions, additional degrees of freedom in form of an input are provided. Then an optimal tracking problem for quadratic integral cost is formulated. Despite the time dependency of the cost and dynamics, we derive a static feedback and pre-filter satisfying necessary optimality conditions for infinite final time. These can be calculated by the solution of an algebraic Riccati equation and a Sylvester equation, respectively. In spite of its simplicity in derivation as well as implementation - offering great convenience for practical use - we prove optimal transient behavior to a unique optimal stationary trajectory of the system states. Or, more precisely, of the internal dynamics which are proven to exist. Moreover, the static control law is verified to be a close approximation of the computationally expensive finite time optimal solution if simple qualitative criteria are met. An application to a helicopter model reveals the high efficiency of our approach compared to others.
{"title":"Static optimal decoupling control for linear over-actuated systems regarding time-varying references","authors":"Sebastian Bernhard, J. Adamy","doi":"10.23919/ACC.2017.7963091","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963091","url":null,"abstract":"We address static decoupling control for linear over-actuated systems and time-varying references given by exogenous systems with arbitrary eigenvalues. Based on mild assumptions, additional degrees of freedom in form of an input are provided. Then an optimal tracking problem for quadratic integral cost is formulated. Despite the time dependency of the cost and dynamics, we derive a static feedback and pre-filter satisfying necessary optimality conditions for infinite final time. These can be calculated by the solution of an algebraic Riccati equation and a Sylvester equation, respectively. In spite of its simplicity in derivation as well as implementation - offering great convenience for practical use - we prove optimal transient behavior to a unique optimal stationary trajectory of the system states. Or, more precisely, of the internal dynamics which are proven to exist. Moreover, the static control law is verified to be a close approximation of the computationally expensive finite time optimal solution if simple qualitative criteria are met. An application to a helicopter model reveals the high efficiency of our approach compared to others.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124333794","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963865
Esteban Jiménez‐Rodríguez, J. Sánchez‐Torres, D. Gómez‐Gutiérrez, A. Loukianov
The aim of this paper is to introduce a controller that stabilizes a class of arbitrary order systems in predefined-time. The proposed controller is designed with basis on the block-control principle yielding in a nested structure similar to high order sliding mode algorithms and terminal sliding mode algorithms. For this case, it is assumed the availability of the state and the absence of perturbations. Numerical simulations expose the desired performance of this controller.
{"title":"Predefined-time stabilization of high order systems","authors":"Esteban Jiménez‐Rodríguez, J. Sánchez‐Torres, D. Gómez‐Gutiérrez, A. Loukianov","doi":"10.23919/ACC.2017.7963865","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963865","url":null,"abstract":"The aim of this paper is to introduce a controller that stabilizes a class of arbitrary order systems in predefined-time. The proposed controller is designed with basis on the block-control principle yielding in a nested structure similar to high order sliding mode algorithms and terminal sliding mode algorithms. For this case, it is assumed the availability of the state and the absence of perturbations. Numerical simulations expose the desired performance of this controller.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124336834","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963069
Lei Zhang, S. Laghrouche, M. Harmouche, M. Cirrincione
This paper proposes a super twisting sliding mode control technique for linear induction motors (LIMs) with unknown load torque, taking into consideration the dynamic end effects. First, LIM's dynamic end effects are presented by Ducan's T-model, then following this model is controlled by a designed super twisting controller (STC) for flux tracking and speed tracking purpose. Simultaneously, an open loop flux observer and a reduced order load torque observer are designed based on Lyapunov's analysis. Finally, simulation results show that the designed observer-based super twisting controller has great tracking performance and the system is robust with disturbances and uncertainties, and flux observer and reduced torque observer show good estimate performance with nominal system and input-to-state stability (ISS) property with uncertainty system.
{"title":"Super twisting control of linear induction motor considering end effects with unknown load torque","authors":"Lei Zhang, S. Laghrouche, M. Harmouche, M. Cirrincione","doi":"10.23919/ACC.2017.7963069","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963069","url":null,"abstract":"This paper proposes a super twisting sliding mode control technique for linear induction motors (LIMs) with unknown load torque, taking into consideration the dynamic end effects. First, LIM's dynamic end effects are presented by Ducan's T-model, then following this model is controlled by a designed super twisting controller (STC) for flux tracking and speed tracking purpose. Simultaneously, an open loop flux observer and a reduced order load torque observer are designed based on Lyapunov's analysis. Finally, simulation results show that the designed observer-based super twisting controller has great tracking performance and the system is robust with disturbances and uncertainties, and flux observer and reduced torque observer show good estimate performance with nominal system and input-to-state stability (ISS) property with uncertainty system.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133792623","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963321
Zehui Mao, G. Tao, B. Jiang, Xing-gang Yan
This paper addresses the adaptive position tracking control problem for high-speed trains with time-varying resistances and mass in the motion dynamics. To handel these time-varying parameters with piecewise constant characteristics, a piecewise constant model with unknown parameters is introduced for different train operation conditions. An integrated adaptive controller structure is constructed to have the capacity to achieve plant-model matching with known parameters and complete system parametrization with unknown parameters, which is desirable for adaptive tracking control. For the train position tracking requirement, the reference model system is specifically chosen. Stable adaptive laws are designed to update the adaptive controller parameters in the presence of the unknown piecewise constant system parameters. Closed-loop stability and asymptotic state tracking are proved. Simulation results on a high-speed train model are presented to illustrate the desired adaptive position tracking control performance.
{"title":"Adaptive position tracking control of high-speed trains with piecewise dynamics","authors":"Zehui Mao, G. Tao, B. Jiang, Xing-gang Yan","doi":"10.23919/ACC.2017.7963321","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963321","url":null,"abstract":"This paper addresses the adaptive position tracking control problem for high-speed trains with time-varying resistances and mass in the motion dynamics. To handel these time-varying parameters with piecewise constant characteristics, a piecewise constant model with unknown parameters is introduced for different train operation conditions. An integrated adaptive controller structure is constructed to have the capacity to achieve plant-model matching with known parameters and complete system parametrization with unknown parameters, which is desirable for adaptive tracking control. For the train position tracking requirement, the reference model system is specifically chosen. Stable adaptive laws are designed to update the adaptive controller parameters in the presence of the unknown piecewise constant system parameters. Closed-loop stability and asymptotic state tracking are proved. Simulation results on a high-speed train model are presented to illustrate the desired adaptive position tracking control performance.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130955191","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 : 2017-05-24DOI: 10.23919/ACC.2017.7963204
S. Farahani, R. Majumdar, Vinayak S. Prabhu, S. Soudjani
We present Shrinking Horizon Model Predictive Control (SHMPC) for linear dynamical systems, under stochastic disturbances, with probabilistic constraints encoded as Signal Temporal Logic (STL) specifications. The control objective is to minimize a cost function under the restriction that the given STL specification be satisfied with some minimum probability. The presented approach utilizes the knowledge of the disturbance distribution to synthesize the controller in SHMPC. We show that this synthesis problem can be (conservatively) transformed into sequential optimizations involving linear constraints. We experimentally demonstrate the effectiveness of our proposed approach by evaluating its performance on room temperature control of a building.
{"title":"Shrinking Horizon Model Predictive Control with chance-constrained signal temporal logic specifications","authors":"S. Farahani, R. Majumdar, Vinayak S. Prabhu, S. Soudjani","doi":"10.23919/ACC.2017.7963204","DOIUrl":"https://doi.org/10.23919/ACC.2017.7963204","url":null,"abstract":"We present Shrinking Horizon Model Predictive Control (SHMPC) for linear dynamical systems, under stochastic disturbances, with probabilistic constraints encoded as Signal Temporal Logic (STL) specifications. The control objective is to minimize a cost function under the restriction that the given STL specification be satisfied with some minimum probability. The presented approach utilizes the knowledge of the disturbance distribution to synthesize the controller in SHMPC. We show that this synthesis problem can be (conservatively) transformed into sequential optimizations involving linear constraints. We experimentally demonstrate the effectiveness of our proposed approach by evaluating its performance on room temperature control of a building.","PeriodicalId":422926,"journal":{"name":"2017 American Control Conference (ACC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134124666","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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