Pub Date : 2022-02-09DOI: 10.3389/fcteg.2021.786152
Robert Dehnert, Michelle Damaszek, Sabine Lerch, A. Rauh, B. Tibken
This paper deals with the design of linear observer-based state feedback controllers with constant gains for a class of nonlinear discrete-time systems in the form of a quasi-linear representation in presence of stochastic noise. For taking into account nonlinearities in the design of linear observer-based state feedback controllers, a polytopic modeling approach is investigated. An optimization problem is formulated to reduce the sensitivity of the controlled system towards stochastic input, state, and output noise with a predefined covariance. Due to the nonlinearities, the separation principle does not hold, thus, the controller and the observer have to be designed simultaneously. For this purpose, a Lyapunov-based method is used, which provides, in addition to the controller and observer gains, a stability proof for the nonlinear closed loop in a predefined polytopic domain. In general, this leads to nonlinear matrix inequalities. To solve these nonlinear matrix inequalities efficiently, we propose an approach based on linear matrix inequalities (LMIs) with a superposed iteration rule. When using this iterative LMI approach, a minimization task can be solved additionally, which desensitizes the closed loop to stochastic noise. The proposed method additionally enables the consideration of different linear closed loop structures by a unified Lyapunov-based framework. The efficiency of the proposed approach is demonstrated and compared with a classical LQG approach for a nonlinear overhead traveling crane.
{"title":"Robust Feedback Control for Discrete-Time Systems Based on Iterative LMIs with Polytopic Uncertainty Representations Subject to Stochastic Noise","authors":"Robert Dehnert, Michelle Damaszek, Sabine Lerch, A. Rauh, B. Tibken","doi":"10.3389/fcteg.2021.786152","DOIUrl":"https://doi.org/10.3389/fcteg.2021.786152","url":null,"abstract":"This paper deals with the design of linear observer-based state feedback controllers with constant gains for a class of nonlinear discrete-time systems in the form of a quasi-linear representation in presence of stochastic noise. For taking into account nonlinearities in the design of linear observer-based state feedback controllers, a polytopic modeling approach is investigated. An optimization problem is formulated to reduce the sensitivity of the controlled system towards stochastic input, state, and output noise with a predefined covariance. Due to the nonlinearities, the separation principle does not hold, thus, the controller and the observer have to be designed simultaneously. For this purpose, a Lyapunov-based method is used, which provides, in addition to the controller and observer gains, a stability proof for the nonlinear closed loop in a predefined polytopic domain. In general, this leads to nonlinear matrix inequalities. To solve these nonlinear matrix inequalities efficiently, we propose an approach based on linear matrix inequalities (LMIs) with a superposed iteration rule. When using this iterative LMI approach, a minimization task can be solved additionally, which desensitizes the closed loop to stochastic noise. The proposed method additionally enables the consideration of different linear closed loop structures by a unified Lyapunov-based framework. The efficiency of the proposed approach is demonstrated and compared with a classical LQG approach for a nonlinear overhead traveling crane.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41566070","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 : 2022-02-04DOI: 10.3389/fcteg.2022.803468
Myungjin Jung , Kai Chuen Tan , R. Dai
This paper demonstrates an innovative group of robots, consisting of jumping rovers and a charging station, improved traversability and extended energy endurance when traveling to multiple target locations. By employing different jumping rovers with distinct energy consumption characteristics and jumping capabilities, we focus on searching for the most energy-efficient path of each jumping rover in a multi-waypoints visiting mission with obstacles. As jumping rovers can jump onto or over some obstacles without navigating around them, they have the potential to save energy by generating alternative paths to overcome obstacles. Moreover, due to the energy demands for the multi-waypoints mission and the limited battery capacity, a charging station is considered to provide extra energy for enhanced endurance during the mission. We first apply a refined rapidly-exploring random tree star (RRT∗) algorithm to find energy-efficient paths between any two target locations. Then, the genetic algorithm (GA) is applied to select the most profitable combination of paths to visit all targets with energy constraints. Finally, we verify the improved mobility and energy efficiency in both virtual simulation and experimental tests using a group of customized jumping rovers with a charging station and the proposed path planning and task allocation method.
{"title":"Path Planning for a Jumping Rover Team With a Charging Station in Multi-Waypoints Visiting Missions","authors":"Myungjin Jung , Kai Chuen Tan , R. Dai","doi":"10.3389/fcteg.2022.803468","DOIUrl":"https://doi.org/10.3389/fcteg.2022.803468","url":null,"abstract":"This paper demonstrates an innovative group of robots, consisting of jumping rovers and a charging station, improved traversability and extended energy endurance when traveling to multiple target locations. By employing different jumping rovers with distinct energy consumption characteristics and jumping capabilities, we focus on searching for the most energy-efficient path of each jumping rover in a multi-waypoints visiting mission with obstacles. As jumping rovers can jump onto or over some obstacles without navigating around them, they have the potential to save energy by generating alternative paths to overcome obstacles. Moreover, due to the energy demands for the multi-waypoints mission and the limited battery capacity, a charging station is considered to provide extra energy for enhanced endurance during the mission. We first apply a refined rapidly-exploring random tree star (RRT∗) algorithm to find energy-efficient paths between any two target locations. Then, the genetic algorithm (GA) is applied to select the most profitable combination of paths to visit all targets with energy constraints. Finally, we verify the improved mobility and energy efficiency in both virtual simulation and experimental tests using a group of customized jumping rovers with a charging station and the proposed path planning and task allocation method.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42999678","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 : 2022-02-01DOI: 10.3389/fcteg.2021.787747
Emily A. Reed, Paul C Bogdan, S. Pequito
Assessing the stability of biological system models has aided in uncovering a plethora of new insights in genetics, neuroscience, and medicine. In this paper, we focus on analyzing the stability of neurological signals, including electroencephalogram (EEG) signals. Interestingly, spatiotemporal discrete-time linear fractional-order systems (DTLFOS) have been shown to accurately and efficiently represent a variety of neurological and physiological signals. Here, we leverage the conditions for stability of DTLFOS to assess a real-world EEG data set. By analyzing the stability of EEG signals during movement and rest tasks, we provide evidence of the usefulness of the quantification of stability as a bio-marker for cognitive motor control.
{"title":"Quantification of Fractional Dynamical Stability of EEG Signals as a Bio-Marker for Cognitive Motor Control","authors":"Emily A. Reed, Paul C Bogdan, S. Pequito","doi":"10.3389/fcteg.2021.787747","DOIUrl":"https://doi.org/10.3389/fcteg.2021.787747","url":null,"abstract":"Assessing the stability of biological system models has aided in uncovering a plethora of new insights in genetics, neuroscience, and medicine. In this paper, we focus on analyzing the stability of neurological signals, including electroencephalogram (EEG) signals. Interestingly, spatiotemporal discrete-time linear fractional-order systems (DTLFOS) have been shown to accurately and efficiently represent a variety of neurological and physiological signals. Here, we leverage the conditions for stability of DTLFOS to assess a real-world EEG data set. By analyzing the stability of EEG signals during movement and rest tasks, we provide evidence of the usefulness of the quantification of stability as a bio-marker for cognitive motor control.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49103284","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 : 2022-01-21DOI: 10.3389/fcteg.2021.771857
L. Qiu, Tao Deng, Xiaomei Yang, Marzieh Najariyan, Jian-fei Pan, Chengxiang Liu
In this article, a proportional predictive (PP) control strategy is proposed and a high-precision position tracking controller is designed for the networked linear switched reluctance machine system (NLSRMS) with time-varying delay. The closed-loop system model of the NLSRMS is constructed based on the PP control strategy. The stability conditions of the system are proposed by combining the optimal control value calculated by the cost function. The controller of the closed-loop NLSRMS is designed by using continuously updated predictive output equation information based on calculation. The comparative study of the proposed PP and PID control methods is presented by tracking several types of signals. The maximum steady errors of PP and PID control strategies are 0.2 mm and 2 mm under the sinusoidal signal, respectively. The maximum steady errors of PP and PID control strategies are 0.07 mm and 0.7 mm under the square signal, respectively. The simulation results show the effectiveness of the proposed PP control strategy.
{"title":"Proportional Predictive Control of Networked Linear Switched Reluctance Machine System With Time-Varying Delay","authors":"L. Qiu, Tao Deng, Xiaomei Yang, Marzieh Najariyan, Jian-fei Pan, Chengxiang Liu","doi":"10.3389/fcteg.2021.771857","DOIUrl":"https://doi.org/10.3389/fcteg.2021.771857","url":null,"abstract":"In this article, a proportional predictive (PP) control strategy is proposed and a high-precision position tracking controller is designed for the networked linear switched reluctance machine system (NLSRMS) with time-varying delay. The closed-loop system model of the NLSRMS is constructed based on the PP control strategy. The stability conditions of the system are proposed by combining the optimal control value calculated by the cost function. The controller of the closed-loop NLSRMS is designed by using continuously updated predictive output equation information based on calculation. The comparative study of the proposed PP and PID control methods is presented by tracking several types of signals. The maximum steady errors of PP and PID control strategies are 0.2 mm and 2 mm under the sinusoidal signal, respectively. The maximum steady errors of PP and PID control strategies are 0.07 mm and 0.7 mm under the square signal, respectively. The simulation results show the effectiveness of the proposed PP control strategy.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44027334","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 : 2022-01-17DOI: 10.3389/fcteg.2021.786318
Jianting Lyu, Lianghui Sun, Xin Wang, Dai Gao
This article focuses on the consensus problem of linear multi-agent systems under denial-of-service attacks and directed switching topologies. With only intermittent communication, the leader-following consensus can be preserved by fully distributed event-triggered strategies. Theoretical analysis shows that the proposed event-triggered resilient controller guarantees the exponential convergence in the presence of denial-of-service attacks and the exclusion of Zeno behavior. Compared to the existing studies where continuous communication between neighboring agents is required, the event-triggered data reduction scheme is provided to tackle the effects of denial-of-service attacks on directed switching topology as well as to avoid continuous communication and reduce energy consumption. The obtained results can be extended to the scenario without a leader. Numerical simulations are finally given to illustrate the effectiveness of the proposed method.
{"title":"Event-Based Resilient Control of Multi-Agent Systems in Non-Ideal Communication Networks","authors":"Jianting Lyu, Lianghui Sun, Xin Wang, Dai Gao","doi":"10.3389/fcteg.2021.786318","DOIUrl":"https://doi.org/10.3389/fcteg.2021.786318","url":null,"abstract":"This article focuses on the consensus problem of linear multi-agent systems under denial-of-service attacks and directed switching topologies. With only intermittent communication, the leader-following consensus can be preserved by fully distributed event-triggered strategies. Theoretical analysis shows that the proposed event-triggered resilient controller guarantees the exponential convergence in the presence of denial-of-service attacks and the exclusion of Zeno behavior. Compared to the existing studies where continuous communication between neighboring agents is required, the event-triggered data reduction scheme is provided to tackle the effects of denial-of-service attacks on directed switching topology as well as to avoid continuous communication and reduce energy consumption. The obtained results can be extended to the scenario without a leader. Numerical simulations are finally given to illustrate the effectiveness of the proposed method.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47070538","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 : 2022-01-11DOI: 10.3389/fcteg.2021.786877
Xiaohu Zhao, Yuanyuan Zou, Shaoyuan Li
This paper investigates the multi-agent persistent monitoring problem via a novel distributed submodular receding horizon control approach. In order to approximate global monitoring performance, with the definition of sub-modularity, the original persistent monitoring objective is divided into several local objectives in a receding horizon framework, and the optimal trajectories of each agent are obtained by taking into account the neighborhood information. Specifically, the optimization horizon of each local objective is derived from the local target states and the information received from their neighboring agents. Based on the sub-modularity of each local objective, the distributed greedy algorithm is proposed. As a result, each agent coordinates with neighboring agents asynchronously and optimizes its trajectory independently, which reduces the computational complexity while achieving the global performance as much as possible. The conditions are established to ensure the estimation error converges to a bounded global performance. Finally, simulation results show the effectiveness of the proposed method.
{"title":"A Submodular Receding Horizon Control Strategy to Distributed Persistent Monitoring","authors":"Xiaohu Zhao, Yuanyuan Zou, Shaoyuan Li","doi":"10.3389/fcteg.2021.786877","DOIUrl":"https://doi.org/10.3389/fcteg.2021.786877","url":null,"abstract":"This paper investigates the multi-agent persistent monitoring problem via a novel distributed submodular receding horizon control approach. In order to approximate global monitoring performance, with the definition of sub-modularity, the original persistent monitoring objective is divided into several local objectives in a receding horizon framework, and the optimal trajectories of each agent are obtained by taking into account the neighborhood information. Specifically, the optimization horizon of each local objective is derived from the local target states and the information received from their neighboring agents. Based on the sub-modularity of each local objective, the distributed greedy algorithm is proposed. As a result, each agent coordinates with neighboring agents asynchronously and optimizes its trajectory independently, which reduces the computational complexity while achieving the global performance as much as possible. The conditions are established to ensure the estimation error converges to a bounded global performance. Finally, simulation results show the effectiveness of the proposed method.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43766502","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 : 2022-01-01DOI: 10.3389/fcteg.2022.1017256
Mohammad Alali, Mahdi Imani
A major goal in genomics is to properly capture the complex dynamical behaviors of gene regulatory networks (GRNs). This includes inferring the complex interactions between genes, which can be used for a wide range of genomics analyses, including diagnosis or prognosis of diseases and finding effective treatments for chronic diseases such as cancer. Boolean networks have emerged as a successful class of models for capturing the behavior of GRNs. In most practical settings, inference of GRNs should be achieved through limited and temporally sparse genomics data. A large number of genes in GRNs leads to a large possible topology candidate space, which often cannot be exhaustively searched due to the limitation in computational resources. This paper develops a scalable and efficient topology inference for GRNs using Bayesian optimization and kernel-based methods. Rather than an exhaustive search over possible topologies, the proposed method constructs a Gaussian Process (GP) with a topology-inspired kernel function to account for correlation in the likelihood function. Then, using the posterior distribution of the GP model, the Bayesian optimization efficiently searches for the topology with the highest likelihood value by optimally balancing between exploration and exploitation. The performance of the proposed method is demonstrated through comprehensive numerical experiments using a well-known mammalian cell-cycle network.
{"title":"Inference of regulatory networks through temporally sparse data.","authors":"Mohammad Alali, Mahdi Imani","doi":"10.3389/fcteg.2022.1017256","DOIUrl":"https://doi.org/10.3389/fcteg.2022.1017256","url":null,"abstract":"A major goal in genomics is to properly capture the complex dynamical behaviors of gene regulatory networks (GRNs). This includes inferring the complex interactions between genes, which can be used for a wide range of genomics analyses, including diagnosis or prognosis of diseases and finding effective treatments for chronic diseases such as cancer. Boolean networks have emerged as a successful class of models for capturing the behavior of GRNs. In most practical settings, inference of GRNs should be achieved through limited and temporally sparse genomics data. A large number of genes in GRNs leads to a large possible topology candidate space, which often cannot be exhaustively searched due to the limitation in computational resources. This paper develops a scalable and efficient topology inference for GRNs using Bayesian optimization and kernel-based methods. Rather than an exhaustive search over possible topologies, the proposed method constructs a Gaussian Process (GP) with a topology-inspired kernel function to account for correlation in the likelihood function. Then, using the posterior distribution of the GP model, the Bayesian optimization efficiently searches for the topology with the highest likelihood value by optimally balancing between exploration and exploitation. The performance of the proposed method is demonstrated through comprehensive numerical experiments using a well-known mammalian cell-cycle network.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10470625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-10DOI: 10.3389/fcteg.2021.710388
Saeed Salavati, Karolos Grigoriadis , Matthew Franchek
This paper examines the control design for parameter-dependent input-delay linear parameter-varying (LPV) systems with saturation constraints and matched input disturbances. A gain-scheduled dynamic output feedback controller, coupled with a disturbance observer to cancel out input disturbance effects, was augmented with an anti-windup compensator to locally stabilize the input-delay LPV system under saturation, model uncertainty, and exogenous disturbances. Sufficient delay-dependent conditions to asymptotically stabilize the closed-loop system were derived using Lyapunov-Krasovskii functionals and a modified generalized sector condition to address the input saturation nonlinearity. The level of disturbance rejection was characterized via the closed-loop induced L 2 -norm of the closed-loop system in the form of linear matrix inequality (LMI) constraints. The results are examined in the context of the mean arterial pressure (MAP) control in the clinical resuscitation of critical hypotensive patients. The MAP variation response to the injection of vasopressor drugs was modeled as an LPV system with a varying input delay and was susceptible to model uncertainty and input/output disturbances. A Bayesian filtering method known as the cubature Kalman filter (CKF) was used to estimate the instantaneous values of the parameters. The varying delay was estimated via a multiple-model approach. The proposed input-delay LPV control was validated in closed-loop simulations to demonstrate its merits and capabilities in the presence of drug administration constraints.
{"title":"Observer-Based Control of LPV Systems with Input Delay and Saturation and Matched Disturbances via a Generalized Sector Condition","authors":"Saeed Salavati, Karolos Grigoriadis , Matthew Franchek ","doi":"10.3389/fcteg.2021.710388","DOIUrl":"https://doi.org/10.3389/fcteg.2021.710388","url":null,"abstract":"This paper examines the control design for parameter-dependent input-delay linear parameter-varying (LPV) systems with saturation constraints and matched input disturbances. A gain-scheduled dynamic output feedback controller, coupled with a disturbance observer to cancel out input disturbance effects, was augmented with an anti-windup compensator to locally stabilize the input-delay LPV system under saturation, model uncertainty, and exogenous disturbances. Sufficient delay-dependent conditions to asymptotically stabilize the closed-loop system were derived using Lyapunov-Krasovskii functionals and a modified generalized sector condition to address the input saturation nonlinearity. The level of disturbance rejection was characterized via the closed-loop induced L 2 -norm of the closed-loop system in the form of linear matrix inequality (LMI) constraints. The results are examined in the context of the mean arterial pressure (MAP) control in the clinical resuscitation of critical hypotensive patients. The MAP variation response to the injection of vasopressor drugs was modeled as an LPV system with a varying input delay and was susceptible to model uncertainty and input/output disturbances. A Bayesian filtering method known as the cubature Kalman filter (CKF) was used to estimate the instantaneous values of the parameters. The varying delay was estimated via a multiple-model approach. The proposed input-delay LPV control was validated in closed-loop simulations to demonstrate its merits and capabilities in the presence of drug administration constraints.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41747085","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 : 2021-12-03DOI: 10.3389/fcteg.2021.782121
D. Paley, A. A. Thompson, A. Wolek, Paul Ghanem
This paper presents a nonlinear control design for the stabilization of parallel and circular motion in a school of robotic fish actuated with internal reaction wheels. The closed-loop swimming dynamics of the fish robots are represented by the canonical Chaplygin sleigh. They exchange relative state information according to a connected, undirected communication graph to form a system of coupled, nonlinear, second-order oscillators. Prior work on collective motion of constant-speed, self-propelled particles serves as the foundation of our approach. However, unlike a self-propelled particle, the fish robots follow limit-cycle dynamics to sustain periodic flapping for forward motion with time-varying speed. Parallel and circular motions are achieved in an average sense without feedback linearization of the agents’ dynamics. Implementation of the proposed parallel formation control law on an actual school of soft robotic fish is described, including system identification experiments to identify motor dynamics and the design of a motor torque-tracking controller to follow the formation torque control. Experimental results demonstrate a school of four robotic fish achieving parallel formations starting from random initial conditions.
{"title":"Planar Formation Control of a School of Robotic Fish: Theory and Experiments","authors":"D. Paley, A. A. Thompson, A. Wolek, Paul Ghanem","doi":"10.3389/fcteg.2021.782121","DOIUrl":"https://doi.org/10.3389/fcteg.2021.782121","url":null,"abstract":"This paper presents a nonlinear control design for the stabilization of parallel and circular motion in a school of robotic fish actuated with internal reaction wheels. The closed-loop swimming dynamics of the fish robots are represented by the canonical Chaplygin sleigh. They exchange relative state information according to a connected, undirected communication graph to form a system of coupled, nonlinear, second-order oscillators. Prior work on collective motion of constant-speed, self-propelled particles serves as the foundation of our approach. However, unlike a self-propelled particle, the fish robots follow limit-cycle dynamics to sustain periodic flapping for forward motion with time-varying speed. Parallel and circular motions are achieved in an average sense without feedback linearization of the agents’ dynamics. Implementation of the proposed parallel formation control law on an actual school of soft robotic fish is described, including system identification experiments to identify motor dynamics and the design of a motor torque-tracking controller to follow the formation torque control. Experimental results demonstrate a school of four robotic fish achieving parallel formations starting from random initial conditions.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41611534","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 : 2021-11-30DOI: 10.3389/fcteg.2021.788492
Mo Tao, Tianyi Gao, Xianling Li, Kuan Li
This paper presents a data-driven predictive controller based on the broad learning algorithm without any prior knowledge of the system model. The predictive controller is realized by regressing the predictive model using online process data and the incremental broad learning algorithm. The proposed model predictive control (MPC) approach requires less online computational load compared to other neural network based MPC approaches. More importantly, the precision of the predictive model is enhanced with reduced computational load by operating an appropriate approximation of the predictive model. The approximation is proved to have no influence on the convergence of the predictive control algorithm. Compared with the partial form dynamic linearization aided model free control (PFDL-MFC), the control performance of the proposed predictive controller is illustrated through the continuous stirred tank heater (CSTH) benchmark.
{"title":"Broad Learning Aided Model Predictive Control With Application to Continuous Stirred Tank Heater","authors":"Mo Tao, Tianyi Gao, Xianling Li, Kuan Li","doi":"10.3389/fcteg.2021.788492","DOIUrl":"https://doi.org/10.3389/fcteg.2021.788492","url":null,"abstract":"This paper presents a data-driven predictive controller based on the broad learning algorithm without any prior knowledge of the system model. The predictive controller is realized by regressing the predictive model using online process data and the incremental broad learning algorithm. The proposed model predictive control (MPC) approach requires less online computational load compared to other neural network based MPC approaches. More importantly, the precision of the predictive model is enhanced with reduced computational load by operating an appropriate approximation of the predictive model. The approximation is proved to have no influence on the convergence of the predictive control algorithm. Compared with the partial form dynamic linearization aided model free control (PFDL-MFC), the control performance of the proposed predictive controller is illustrated through the continuous stirred tank heater (CSTH) benchmark.","PeriodicalId":73076,"journal":{"name":"Frontiers in control engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47882035","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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