The Experts below are selected from a list of 2526732 Experts worldwide ranked by ideXlab platform
Michel Fliess - One of the best experts on this subject based on the ideXlab platform.
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First Steps Toward a Simple but Efficient Model-Free Control Synthesis for Variable-speed Wind Turbines
International Journal of Circuits Systems and Signal Processing, 2021Co-Authors: Frédéric Lafont, Cédric Join, Jean-françois Balmat, Michel FliessAbstract:In Although variable-speed three-blade wind turbines are nowadays quite popular, their Control remains a challenging task. We propose a new easily implementable Model-Free Control approach with the corresponding intelligent Controllers. Several convincing computer simulations, including some fault accommodations, shows that Model-Free Controllers are more efficient and robust than classic proportional-integral Controllers.
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Model-Free Control algorithms for micro air vehicles with transitioning flight capabilities
International Journal of Micro Air Vehicles, 2020Co-Authors: Jacson Miguel Olszanecki Barth, Cédric Join, Jean-philippe Condomines, Jean-marc Moschetta, Murat Bronz, Michel FliessAbstract:Micro air vehicles with transitioning flight capabilities, or simply hybrid micro air vehicles, combine the beneficial features of fixed-wing configurations, in terms of endurance, with vertical take-off and landing capabilities of rotorcrafts to perform five different flight phases during typical missions, such as vertical takeoff, transitioning flight, forward flight, hovering and vertical landing. This promising micro air vehicle class has a wider flight envelope than conventional micro air vehicles, which implies new challenges for both Control community and aerodynamic designers. One of the major challenges of hybrid micro air vehicles is the fast variation of aerodynamic forces and moments during the transition flight phase which is difficult to model accurately. To overcome this problem, we propose a flight Control architecture that estimates and counteracts in real-time these fast dynamics with an intelligent feedback Controller. The proposed flight Controller is designed to stabilize the hybrid micro air vehicle attitude as well as its velocity and position during all flight phases. By using Model-Free Control algorithms, the proposed flight Control architecture bypasses the need for a precise hybrid micro air vehicle model that is costly and time consuming to obtain. A comprehensive set of flight simulations covering the entire flight envelope of tailsitter micro air vehicles is presented. Finally, real-world flight tests were conducted to compare the Model-Free Control performance to that of the Incremental Nonlinear Dynamic Inversion Controller, which has been applied to a variety of aircraft providing effective flight performances.
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Model-Free Control as a Service and the Internet of Things (IoT): Some preliminary considerations
2019Co-Authors: Cédric Join, Michel Fliess, Frédéric ChaxelAbstract:Model-Free Control (MFC), which is easy to implement both from software and hardware viewpoints, permits the introduction of a high level Control synthesis for IoT (Internet of Things) and Industry 4.0. The choice of the User Diagram Protocol (UDP) as the Internet Protocol permits to neglect the latency. In spite of most severe packet losses, convincing computer simulations show that MFC exhibits a good Quality of Service (QoS).
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Full Model-Free Control architecture for hybrid UAVs
2019Co-Authors: Jacson Miguel Olszanecki Barth, Cédric Join, Jean-philippe Condomines, Jean-marc Moschetta, Aurélien Cabarbaye, Michel FliessAbstract:This paper discusses the development of a Control architecture for hybrid Unmanned Aerial Vehicles (UAVs) based on Model-Free Control (MFC) algorithms. Hybrid UAVs combine the beneficial features of fixed-wing UAVs with Vertical Take-Off and Landing (VTOL) capabilities to perform five different flight phases during typical missions, such as vertical takeoff, transitioning flight, forward flight, hovering and vertical landing. Based on Model-Free Control principles, a novel Control architecture that handles the hybrid UAV dynamics at any flight phase is presented. This unified Controller allows autonomous flights without discontinuities of switching for the entire flight envelope with position tracking, velocity Control and attitude stabilization. Simulation results show that the proposed Control architecture provides an effective Control performance for the entire flight envelope and excellent disturbance rejections during the critical flight phases, such as transitioning and hovering flights in windy conditions.
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ACC - Full Model-Free Control Architecture for Hybrid UAVs
2019 American Control Conference (ACC), 2019Co-Authors: Jacson M. O. Barth, Cédric Join, Jean-philippe Condomines, Jean-marc Moschetta, Aurélien Cabarbaye, Michel FliessAbstract:This paper discusses the development of a Control architecture for hybrid Unmanned Aerial Vehicles (UAVs) based on Model-Free Control (MFC) algorithms. Hybrid UAVs combine the beneficial features of fixed-wing UAVs with Vertical Take-Off and Landing (VTOL) capabilities to perform five different flight phases during typical missions, such as vertical takeoff, transitioning flight, forward flight, hovering and vertical landing. Based on Model-Free Control principles, a novel Control architecture that handles the hybrid UAV dynamics at any flight phase is presented. This unified Controller allows autonomous flights without discontinuities of switching for the entire flight envelope with position tracking, velocity Control and attitude stabilization. Simulation results show that the proposed Control architecture provides an effective Control performance for the entire flight envelope and excellent disturbance rejections during the critical flight phases, such as transitioning and hovering flights in windy conditions.
Cédric Join - One of the best experts on this subject based on the ideXlab platform.
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First Steps Toward a Simple but Efficient Model-Free Control Synthesis for Variable-speed Wind Turbines
International Journal of Circuits Systems and Signal Processing, 2021Co-Authors: Frédéric Lafont, Cédric Join, Jean-françois Balmat, Michel FliessAbstract:In Although variable-speed three-blade wind turbines are nowadays quite popular, their Control remains a challenging task. We propose a new easily implementable Model-Free Control approach with the corresponding intelligent Controllers. Several convincing computer simulations, including some fault accommodations, shows that Model-Free Controllers are more efficient and robust than classic proportional-integral Controllers.
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Model-Free Control algorithms for micro air vehicles with transitioning flight capabilities
International Journal of Micro Air Vehicles, 2020Co-Authors: Jacson Miguel Olszanecki Barth, Cédric Join, Jean-philippe Condomines, Jean-marc Moschetta, Murat Bronz, Michel FliessAbstract:Micro air vehicles with transitioning flight capabilities, or simply hybrid micro air vehicles, combine the beneficial features of fixed-wing configurations, in terms of endurance, with vertical take-off and landing capabilities of rotorcrafts to perform five different flight phases during typical missions, such as vertical takeoff, transitioning flight, forward flight, hovering and vertical landing. This promising micro air vehicle class has a wider flight envelope than conventional micro air vehicles, which implies new challenges for both Control community and aerodynamic designers. One of the major challenges of hybrid micro air vehicles is the fast variation of aerodynamic forces and moments during the transition flight phase which is difficult to model accurately. To overcome this problem, we propose a flight Control architecture that estimates and counteracts in real-time these fast dynamics with an intelligent feedback Controller. The proposed flight Controller is designed to stabilize the hybrid micro air vehicle attitude as well as its velocity and position during all flight phases. By using Model-Free Control algorithms, the proposed flight Control architecture bypasses the need for a precise hybrid micro air vehicle model that is costly and time consuming to obtain. A comprehensive set of flight simulations covering the entire flight envelope of tailsitter micro air vehicles is presented. Finally, real-world flight tests were conducted to compare the Model-Free Control performance to that of the Incremental Nonlinear Dynamic Inversion Controller, which has been applied to a variety of aircraft providing effective flight performances.
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Model-Free Control as a Service and the Internet of Things (IoT): Some preliminary considerations
2019Co-Authors: Cédric Join, Michel Fliess, Frédéric ChaxelAbstract:Model-Free Control (MFC), which is easy to implement both from software and hardware viewpoints, permits the introduction of a high level Control synthesis for IoT (Internet of Things) and Industry 4.0. The choice of the User Diagram Protocol (UDP) as the Internet Protocol permits to neglect the latency. In spite of most severe packet losses, convincing computer simulations show that MFC exhibits a good Quality of Service (QoS).
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Full Model-Free Control architecture for hybrid UAVs
2019Co-Authors: Jacson Miguel Olszanecki Barth, Cédric Join, Jean-philippe Condomines, Jean-marc Moschetta, Aurélien Cabarbaye, Michel FliessAbstract:This paper discusses the development of a Control architecture for hybrid Unmanned Aerial Vehicles (UAVs) based on Model-Free Control (MFC) algorithms. Hybrid UAVs combine the beneficial features of fixed-wing UAVs with Vertical Take-Off and Landing (VTOL) capabilities to perform five different flight phases during typical missions, such as vertical takeoff, transitioning flight, forward flight, hovering and vertical landing. Based on Model-Free Control principles, a novel Control architecture that handles the hybrid UAV dynamics at any flight phase is presented. This unified Controller allows autonomous flights without discontinuities of switching for the entire flight envelope with position tracking, velocity Control and attitude stabilization. Simulation results show that the proposed Control architecture provides an effective Control performance for the entire flight envelope and excellent disturbance rejections during the critical flight phases, such as transitioning and hovering flights in windy conditions.
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ACC - Full Model-Free Control Architecture for Hybrid UAVs
2019 American Control Conference (ACC), 2019Co-Authors: Jacson M. O. Barth, Cédric Join, Jean-philippe Condomines, Jean-marc Moschetta, Aurélien Cabarbaye, Michel FliessAbstract:This paper discusses the development of a Control architecture for hybrid Unmanned Aerial Vehicles (UAVs) based on Model-Free Control (MFC) algorithms. Hybrid UAVs combine the beneficial features of fixed-wing UAVs with Vertical Take-Off and Landing (VTOL) capabilities to perform five different flight phases during typical missions, such as vertical takeoff, transitioning flight, forward flight, hovering and vertical landing. Based on Model-Free Control principles, a novel Control architecture that handles the hybrid UAV dynamics at any flight phase is presented. This unified Controller allows autonomous flights without discontinuities of switching for the entire flight envelope with position tracking, velocity Control and attitude stabilization. Simulation results show that the proposed Control architecture provides an effective Control performance for the entire flight envelope and excellent disturbance rejections during the critical flight phases, such as transitioning and hovering flights in windy conditions.
Han Zhi-gang - One of the best experts on this subject based on the ideXlab platform.
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An IPSO-based model free Control for freeway ramp metering
2012Co-Authors: Li Xiuying, Jin Xufu, Han Zhi-gangAbstract:A model free Control strategy based on improved particle swarm optimization (IPSO) is proposed for freeway ramp metering. First, the freeway traffic model is built which is nonlinear in nature and can be linearized to a universal model with error correction. The Control objective is to maintain the freeway operated at a desired traffic density. Then, an improved PSO algorithm is presented which has high speed convergence and can overcome the problem of being trapped in local optimum. The unknown parameters in the Controller are optimized by the IPSO and the range of the particles can be given by analyzing the convergence of the Controller. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.
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Model Free Control Method for Nonlinear Systems
Control Engineering of China, 2010Co-Authors: Han Zhi-gangAbstract:Nonlinear systems are widespread in the various practical problems,but it is very difficult to build the accurate mathematical model of complex nonlinear objects.To achieve the stability Control for nonlinear systems is a difficult task of Control engineering.The model free Control method is designed by nonlinear approach,so it can show its good quality to Control some nonlinear complex objects.This method has achieved an intelligent reasoning of the algorithm,so it is different from the general intelligent Control algorithms.The rapid convergence of the model free Control law for nonlinear Controlled objecets is analyzed.Specific engineering examples show the successful application of model free Control method for nonlinear systems.
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Performance Analysis of Model Free Control Method
Control Engineering of China, 2009Co-Authors: Han Zhi-gangAbstract:Model-Free Control method is discussed,which has received good results in the practical application because of excellent performance,such as adaptability,anti-jamming capability,decoupling performance,mandatory stability,etc.To the necessity and difficulty of the theory analysis of these performances,the simulation results of some important performances of Model-Free Control method are introduced.Because more than 90 percents of Controllers used in industrial process Control are PID regulators,the simulation is done to compare the corresponding performances of PID regulators.Simulation results show that the Model-Free Control method is better than PID regulator in adaptability,anti-jamming capability,decoupling performance,mandatory stability.The designed simulation system is simply introduced.
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Analysis of Disturbance-resistant Ability of Model Free Control Method
Computer Simulation, 2008Co-Authors: Han Zhi-gangAbstract:The model free Control method has obtained better effect in the practical application. This is because that the model free Control method has a series of excellent properties. In the practice,it has been indicated that the ability of disturbance-resistant is the most important. The convergence of the model free Control algorithm was analyzed,which shows that model free Control method has the strong anti-interference capability because of the fast convergence. The simulation compared with the PID's anti-interference capability further verifies that the model free Control method has greater ability of resisted disturbance.
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Improvement of Basic Control of DCS via Model Free Control Method
Control Engineering of China, 2008Co-Authors: Han Zhi-gangAbstract:The problems existing in the distribution Control systems that are widely used affect its application.The problems embody in the non-adaptality of PID Control methods in the basic Control parts to the complex objects.The important significance of closed-loop stable Control of the basic Control parts is explained.The model free Control is an effective method of colsed-loop stable Control to the basic Control parts.The applications of model free Control method in process Control are introduced.And the suggestions to reform DCS via model free Control method are proposed.
Dirk Söffker - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Different Model-Free Control Methods Concerning Real-Time Benchmark
Journal of Dynamic Systems Measurement and Control, 2018Co-Authors: Elmira Madadi, Yao Dong, Dirk SöffkerAbstract:The design of accurate model often appears as the most challenging tasks for Control engineers especially focusing to the Control of nonlinear system with unknown parameters or effects to be identified in parallel. For this reason, development of Model-Free Control methods is of increasing importance. The class of Model-Free Control approaches is defined by the nonuse of any knowledge about the underlying structure and/or related parameters of the dynamical system. Therefore, the major criteria to evaluate Model-Free Control performance are aspects regarding robustness against unknown inputs and disturbances and related achievable tracking performance. In this contribution, a detailed comparison of three different Model-Free Control methods (intelligent proportional-integral-derivative (iPID) using second-order sliding differentiator and two variations of Model-Free adaptive Control (using modified compact form dynamic linearization (CFDL) as well as modified partial form) is given. Using a three-tank system benchmark, the experimental results are validated concerning the performance behavior. The results obtained demonstrate the effectiveness of the methods introduced.
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Model-Free Control of unknown nonlinear systems using an iterative learning concept: theoretical development and experimental validation
Nonlinear Dynamics, 2018Co-Authors: Elmira Madadi, Dirk SöffkerAbstract:In this paper, a newly developed adaptive Model-Free Control method using an iterative learning Control method based on a robust Control design framework is discussed. Consequently, no system plant model is required for Control design; only the inputs and outputs are used as Controller input. Briefly, no assumption is required regarding the system plant model. In this contribution, a fully Model-Free Control method is developed to be applied to unknown nonlinear SISO systems using (i) a modified Model-Free adaptive scheme to estimate the system local dynamics iteratively, (ii) an optimal weighting matrices design method based on a designable relationship between the system output to its input, and (iii) an iterative learning concept to generate a suitable Control input according to the robust design framework. As an example, the task of perfect sinusoidal movement of a cart with a mounted inverted elastic cantilever beam is chosen to validate the performance of the proposed approach. The challenge is to realize the Control in the presence of unknown nonlinear interaction of the flexible beam to the moving carts motion. To demonstrate effectiveness of the proposed method, the results obtained are compared with classical and well-known model-based Control approaches.
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Model-Free Control Approach of a Three-Tank System Using an Adaptive-Based Control
Volume 6: 13th International Conference on Multibody Systems Nonlinear Dynamics and Control, 2017Co-Authors: Elmira Madadi, Yao Dong, Dirk SöffkerAbstract:For improving the dynamics of systems in the last decades model-based Control design approaches are continuously developed. The task to design an accurate model is the most relevant and related task for Control engineers, which is time consuming and difficult if in the case of complex nonlinear systems a complex modeling or identification problem arises. For this reason Model-Free Control methods become attractive as alternative to avoid modeling. This contribution focuses on design methods of a Model-Free adaptive-based Controller and modified Model-Free adaptive-based Controller. Modified approach is based on the same adaptive Model-Free Control algorithm performing tracking error optimization. Both approaches are designed for non-linear systems with uncertainties and in the presence of disturbances in order to assure suitable performance as well as robustness against unknown inputs. Using this approach, the Controller requires neither the information about the systems dynamical structure nor the knowledge about systems physical behaviors. The task is solved using only the system outputs and inputs, which are measurable. The effectiveness of the proposed method is validated by experiments using a three-tank system.
Elmira Madadi - One of the best experts on this subject based on the ideXlab platform.
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Model-Free Control Design for Nonlinear Mechanical Systems
2019Co-Authors: Elmira MadadiAbstract:As industrial processes and demands for precise process Control and quality enhancement become more advance and complex, the demand of designing appropriate Controllers increases continuously. Based on physical and mathematical knowledge about the system, well-established model-based Control methods have been successfully applied to linear and nonlinear systems, well-known benchmarks, and industrial processes. However, model-based schemes require detailed model knowledge and related modeling processes using first-order (theoretical) modeling or models identified by suitable approaches like recursive least-squares (RLS) or data-driven techniques. With the increasing scale of industrial plants and enterprises, production technology and processes also become complex, and the requirements on product quality are increasing. All these aspects which were previously not fully explored, cause considerable challenges to the theoretical studies and practical applications of model-based Control theory. To avoid the demanding and complex modeling procedures, Model-Free Control methods are utilized as an efficient alternative, in which the accurate system manual modeling step is not required. Accordingly, Model-Free Control technique can be a suitable technique targeting a high performance considering the following two key aspects: i) the dynamical behavior of the unknown system should be determined without the requirement of mathematical models of the system only using the data measured from the input and output of the system. It is worth noting that the dynamical behavior of the system to be Controlled should be estimated online to update the actual dynamics, and ii) an appropriate Control input should be generated for the upcoming time steps. Despite the strong researches and works in this area, there are still aspects to be improved. Therefore, this thesis focuses on improving the efficient design of Model-Free Control methods and development of precise concepts to describe the nonlinear systems combined with the optimal time-varying parameters for possible general applications. The Modified Model-Free Adaptive Control (Modified MFAC) method is introduced as the extended version of Model-Free Adaptive Control (MFAC) approach to overcome the disadvantages of MFAC and to realize a suitable Control performance in the case of time delay systems. Accordingly, Model-Free dynamically linearization approaches including Compact Form Dynamically Linearization (CFDL) and Partial Form Dynamically Linearization (PFDL) are focused to realize an equivalent transformation of an original nonlinear system. A modified objective is defined so that the Control performance and the related energy can be evaluated. Unlike MFAC method, Modified MFAC approach attempts to reduce time delay effects on the nonlinear system. Comparison between MFAC design has been done in the task of tracking trajectory considering a nonlinear well-known benchmark system. The stability of the system is established considering the convergence of Controller tracking error and the value of the Pseudo Partial Derivative. Experimental and simulation results for SISO and MIMO cases validate and verify the effectiveness of the proposed Modified MFAC in comparison to previous methods. Consequently, a general comparison of both proposed Model-Free approaches is provided based on the experimental results. Furthermore, a novel Model-Free Control approach is proposed to overcome the disadvantages/difficulties of Model-Free Adaptive Control approach. The idea of well-known classical PID Control is taken into consideration to design a Model-Free intelligent PID Controller. The proposed intelligent PID Controller uses the input-output information of the system instead of a mathematical plant model. The measured I/O data already contains information which describes the internal dynamics of the nonlinear system to eliminate the need for a precise plant model. In addition, the concept of using a robust sliding mode differentiator which can directly operate the original signal and achieve the derivative signal is introduced to estimate the derivative of output signal. The effectiveness of the proposed method is evaluated by experimental results using a three tank system test rig. Moreover, this thesis provides a novel fully Model-Free Controller as Model-Free Adaptive ILC to improve the position tracking performance of a nonlinear inverted elastic cantilever beam test rig as well as its robustness in the presence of external disturbances. The structure of Modified MFAC is integrated into the Iterative Learning Control approach to achieve a Control law that provides the desired performance. An equivalent transformation of the original nonlinear system is performed to integrate into the structure of Iterative Learning Control approach and weighting matrices design. Additionally, a new weighting matrices design approach considering Discrete Algebraic Ricatti Equation is proposed to achieve appropriate weighting matrices design and to eliminate the effects of related nonoptimal weighting matrices values. The convergence analysis is considered in terms of the convergence rate. Experimental results validate the advantages of proposed Model-Free Adaptive ILC approach in comparison to standard NOILC and model-based standard IMC in the presence of external disturbances. Finally, an iterative learning-based intelligent PI Controller is proposed for a nonlinear MIMO system. The introduced Model-Free intelligent PI Control approach is used in combination with a well-known PD-type ILC method. The tracking Control procedure and its evaluation are detailed by simulation results defining different scenarios in the context of a nonlinear MIMO system.
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Comparison of Different Model-Free Control Methods Concerning Real-Time Benchmark
Journal of Dynamic Systems Measurement and Control, 2018Co-Authors: Elmira Madadi, Yao Dong, Dirk SöffkerAbstract:The design of accurate model often appears as the most challenging tasks for Control engineers especially focusing to the Control of nonlinear system with unknown parameters or effects to be identified in parallel. For this reason, development of Model-Free Control methods is of increasing importance. The class of Model-Free Control approaches is defined by the nonuse of any knowledge about the underlying structure and/or related parameters of the dynamical system. Therefore, the major criteria to evaluate Model-Free Control performance are aspects regarding robustness against unknown inputs and disturbances and related achievable tracking performance. In this contribution, a detailed comparison of three different Model-Free Control methods (intelligent proportional-integral-derivative (iPID) using second-order sliding differentiator and two variations of Model-Free adaptive Control (using modified compact form dynamic linearization (CFDL) as well as modified partial form) is given. Using a three-tank system benchmark, the experimental results are validated concerning the performance behavior. The results obtained demonstrate the effectiveness of the methods introduced.
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Model-Free Control of unknown nonlinear systems using an iterative learning concept: theoretical development and experimental validation
Nonlinear Dynamics, 2018Co-Authors: Elmira Madadi, Dirk SöffkerAbstract:In this paper, a newly developed adaptive Model-Free Control method using an iterative learning Control method based on a robust Control design framework is discussed. Consequently, no system plant model is required for Control design; only the inputs and outputs are used as Controller input. Briefly, no assumption is required regarding the system plant model. In this contribution, a fully Model-Free Control method is developed to be applied to unknown nonlinear SISO systems using (i) a modified Model-Free adaptive scheme to estimate the system local dynamics iteratively, (ii) an optimal weighting matrices design method based on a designable relationship between the system output to its input, and (iii) an iterative learning concept to generate a suitable Control input according to the robust design framework. As an example, the task of perfect sinusoidal movement of a cart with a mounted inverted elastic cantilever beam is chosen to validate the performance of the proposed approach. The challenge is to realize the Control in the presence of unknown nonlinear interaction of the flexible beam to the moving carts motion. To demonstrate effectiveness of the proposed method, the results obtained are compared with classical and well-known model-based Control approaches.
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Model-Free Control Approach of a Three-Tank System Using an Adaptive-Based Control
Volume 6: 13th International Conference on Multibody Systems Nonlinear Dynamics and Control, 2017Co-Authors: Elmira Madadi, Yao Dong, Dirk SöffkerAbstract:For improving the dynamics of systems in the last decades model-based Control design approaches are continuously developed. The task to design an accurate model is the most relevant and related task for Control engineers, which is time consuming and difficult if in the case of complex nonlinear systems a complex modeling or identification problem arises. For this reason Model-Free Control methods become attractive as alternative to avoid modeling. This contribution focuses on design methods of a Model-Free adaptive-based Controller and modified Model-Free adaptive-based Controller. Modified approach is based on the same adaptive Model-Free Control algorithm performing tracking error optimization. Both approaches are designed for non-linear systems with uncertainties and in the presence of disturbances in order to assure suitable performance as well as robustness against unknown inputs. Using this approach, the Controller requires neither the information about the systems dynamical structure nor the knowledge about systems physical behaviors. The task is solved using only the system outputs and inputs, which are measurable. The effectiveness of the proposed method is validated by experiments using a three-tank system.