The Experts below are selected from a list of 21618 Experts worldwide ranked by ideXlab platform
Robert Babuska - One of the best experts on this subject based on the ideXlab platform.
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Learning Sequential Composition Control
IEEE transactions on cybernetics, 2015Co-Authors: Esmaeil Najafi, Robert Babuska, Gabriel A D LopesAbstract:Sequential Composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers Sequentially to achieve a control specification that cannot be realized by a single controller. As these controllers are designed offline, Sequential Composition cannot address unmodeled situations that might occur during runtime. This paper proposes a learning approach to augment the standard Sequential Composition framework by using online learning to handle unforeseen situations. New controllers are acquired via learning and added to the existing supervisory control structure. In the proposed setting, learning experiments are restricted to take place within the domain of attraction (DOA) of the existing controllers. This guarantees that the learning process is safe (i.e., the closed loop system is always stable). In addition, the DOA of the new learned controller is approximated after each learning trial. This keeps the learning process short as learning is terminated as soon as the DOA of the learned controller is sufficiently large. The proposed approach has been implemented on two nonlinear systems: 1) a nonlinear mass-damper system and 2) an inverted pendulum. The results show that in both cases a new controller can be rapidly learned and added to the supervisory control structure.
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an application of Sequential Composition control to cooperative systems
International Workshop on Robot Motion and Control, 2015Co-Authors: Esmaeil Najafi, Robert Babuska, Gabriel A D LopesAbstract:Sequential Composition is an effective supervisory control approach for addressing challenging control problems on complex dynamical systems. It constructs a back-chaining sequence of controllers to achieve the control objective using simple local controllers. Although Sequential Composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard Sequential Composition by introducing a novel approach to compose multiple Sequential Composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' supervisory finite-state machines, together with the estimation of the domains of attraction of the composed controllers. We present the simulation and experimental results of an inverted pendulum system collaborating with a second-order DC motor for cooperative swing-up maneuvers.
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RoMoCo - An application of Sequential Composition control to cooperative systems
2015 10th International Workshop on Robot Motion and Control (RoMoCo), 2015Co-Authors: Esmaeil Najafi, Robert Babuska, Gabriel A D LopesAbstract:Sequential Composition is an effective supervisory control approach for addressing challenging control problems on complex dynamical systems. It constructs a back-chaining sequence of controllers to achieve the control objective using simple local controllers. Although Sequential Composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard Sequential Composition by introducing a novel approach to compose multiple Sequential Composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' supervisory finite-state machines, together with the estimation of the domains of attraction of the composed controllers. We present the simulation and experimental results of an inverted pendulum system collaborating with a second-order DC motor for cooperative swing-up maneuvers.
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Learning Complex Behaviors via Sequential Composition and Passivity-Based Control
Studies in Systems Decision and Control, 2015Co-Authors: Gabriel A D Lopes, Esmaeil Najafi, Subramanya Nageshrao, Robert BabuskaAbstract:The model-free paradigm of Reinforcement learning (RL) is a theoretical strength. However in practice, the stringent assumptions required for optimal solutions (full state space exploration) and experimental issues, such as slow learning rates, render model-free RL a practical weakness. This paper addresses practical implementations of RL by interfacing elements of systems and control and robotics. In our approach space is handled by Sequential Composition (a technique commonly used in robotics) and time is handled by the use of passivity-based control methods (a standard nonlinear control approach) towards speeding up learning and providing a stopping time criteria. Sequential Composition in effect partitions the state space and allows for the Composition of controllers, each having different domains of attraction (DoA) and goal sets. This results in learning taking place in subsets of the state space. Passivity-based control (PBC) is a model-based control approach where total energy is computable. This total energy can be used as a candidate Lyapunov function to evaluate the stability of a controller and find estimates of its DoA. This enables learning in finite time: while learning the candidate Lyapunov function is monitored online to approximate the DoA of the learned controller. Once this DoA covers relevant states, from the point of view of Sequential Composition, the learning process is stopped. The result of this process is a collection of learned controllers that cover a desired range of the state space, and can be composed in sequence to achieve various desired goals. Optimality is lost in favour of practicality. Other implications include safety while learning and incremental learning.
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rapid learning in Sequential Composition control
Conference on Decision and Control, 2014Co-Authors: Esmaeil Najafi, Gabriel A D Lopes, Subramanya Nageshrao, Robert BabuskaAbstract:Sequential Composition is an effective approach to address the control of complex dynamical systems. However, it is not designed to cope with unforeseen situations that might occur during runtime. This paper extends Sequential Composition control via learning new policies. A learning module based on reinforcement learning is added to the traditional Sequential Composition that allows for the online creation of new control policies in a short amount of time, on a need basis. During learning, the domain of attraction (DOA) of the new control policy is continuously monitored. Hence, the learning process only executes until the supervisor is able to compose the new control policy with designed controllers via the overlap of DOAs. Estimating the DOAs of the learned controllers is achieved by solving an optimization problem. The proposed strategy has been simulated on a nonlinear system. The results show that the learning module can rapidly augment the designed Sequential Composition by new control policies such that the supervisor could handle unpredicted situations online.
Esmaeil Najafi - One of the best experts on this subject based on the ideXlab platform.
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towards cooperative Sequential Composition control
Conference on Decision and Control, 2016Co-Authors: Esmaeil Najafi, Gabriel A D LopesAbstract:Sequential Composition is a supervisory control architecture for addressing control problems in complex dynamical systems. Although Sequential Composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard Sequential Composition by introducing a novel approach to compose multiple Sequential Composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' control automaton, together with estimation for the domains of attraction of the resulting composed controllers. This typically results in new events for the original Sequential Composition controllers. Applying these events, the cooperative control system can fulfill the tasks which are not possible to satisfy with the original controllers individually. The simulation results of an inverted pendulum system collaborating with two second-order DC motors are presented for cooperative swing-up maneuvers.
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Automatic synthesis of supervisory control systems
2016Co-Authors: Esmaeil NajafiAbstract:Sequential Composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers Sequentially to achieve a control specification that cannot be realized by a single controller. Sequential Composition focuses on the interaction between a collection of pre-designed controllers, where each of them is associated with a domain of attraction (DoA) and a goal set. By design, if the goal set of one controller lies in the DoA of another controller (this is called the prepare relation), the supervisor can instantly switch from the first controller to the second without affecting the stability of the system. As these controllers are designed offline, Sequential Composition cannot address unmodeled situations that might occur during runtime. Moreover, Sequential Composition has not been developed for cooperative settings where the collaboration of multiple systems is required in order to fulfill the control specifications. This thesis studies automatic synthesis of supervisory control systems using the framework of Sequential Composition. First, a learning Sequential Composition control algorithm is developed so as to learn new controllers on demand, by means of reinforcement learning. Once learning is completed, the supervisory control structure is augmented with the learned controllers. As a consequence, the supervisor is able to cope with unforeseen situations for which new controllers are required. Second, a cooperative Sequential Composition control algorithm is proposed to enable the coordination between a set of Sequential Composition controllers, without any change in their low-level structures. Finally, a robot contact language is designed for the manipulation of multiple objects by multiple robots.
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CDC - Towards cooperative Sequential Composition control
2016 IEEE 55th Conference on Decision and Control (CDC), 2016Co-Authors: Esmaeil Najafi, Gabriel A D LopesAbstract:Sequential Composition is a supervisory control architecture for addressing control problems in complex dynamical systems. Although Sequential Composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard Sequential Composition by introducing a novel approach to compose multiple Sequential Composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' control automaton, together with estimation for the domains of attraction of the resulting composed controllers. This typically results in new events for the original Sequential Composition controllers. Applying these events, the cooperative control system can fulfill the tasks which are not possible to satisfy with the original controllers individually. The simulation results of an inverted pendulum system collaborating with two second-order DC motors are presented for cooperative swing-up maneuvers.
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Learning Sequential Composition Control
IEEE transactions on cybernetics, 2015Co-Authors: Esmaeil Najafi, Robert Babuska, Gabriel A D LopesAbstract:Sequential Composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers Sequentially to achieve a control specification that cannot be realized by a single controller. As these controllers are designed offline, Sequential Composition cannot address unmodeled situations that might occur during runtime. This paper proposes a learning approach to augment the standard Sequential Composition framework by using online learning to handle unforeseen situations. New controllers are acquired via learning and added to the existing supervisory control structure. In the proposed setting, learning experiments are restricted to take place within the domain of attraction (DOA) of the existing controllers. This guarantees that the learning process is safe (i.e., the closed loop system is always stable). In addition, the DOA of the new learned controller is approximated after each learning trial. This keeps the learning process short as learning is terminated as soon as the DOA of the learned controller is sufficiently large. The proposed approach has been implemented on two nonlinear systems: 1) a nonlinear mass-damper system and 2) an inverted pendulum. The results show that in both cases a new controller can be rapidly learned and added to the supervisory control structure.
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an application of Sequential Composition control to cooperative systems
International Workshop on Robot Motion and Control, 2015Co-Authors: Esmaeil Najafi, Robert Babuska, Gabriel A D LopesAbstract:Sequential Composition is an effective supervisory control approach for addressing challenging control problems on complex dynamical systems. It constructs a back-chaining sequence of controllers to achieve the control objective using simple local controllers. Although Sequential Composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard Sequential Composition by introducing a novel approach to compose multiple Sequential Composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' supervisory finite-state machines, together with the estimation of the domains of attraction of the composed controllers. We present the simulation and experimental results of an inverted pendulum system collaborating with a second-order DC motor for cooperative swing-up maneuvers.
Gabriel A D Lopes - One of the best experts on this subject based on the ideXlab platform.
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towards cooperative Sequential Composition control
Conference on Decision and Control, 2016Co-Authors: Esmaeil Najafi, Gabriel A D LopesAbstract:Sequential Composition is a supervisory control architecture for addressing control problems in complex dynamical systems. Although Sequential Composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard Sequential Composition by introducing a novel approach to compose multiple Sequential Composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' control automaton, together with estimation for the domains of attraction of the resulting composed controllers. This typically results in new events for the original Sequential Composition controllers. Applying these events, the cooperative control system can fulfill the tasks which are not possible to satisfy with the original controllers individually. The simulation results of an inverted pendulum system collaborating with two second-order DC motors are presented for cooperative swing-up maneuvers.
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CDC - Towards cooperative Sequential Composition control
2016 IEEE 55th Conference on Decision and Control (CDC), 2016Co-Authors: Esmaeil Najafi, Gabriel A D LopesAbstract:Sequential Composition is a supervisory control architecture for addressing control problems in complex dynamical systems. Although Sequential Composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard Sequential Composition by introducing a novel approach to compose multiple Sequential Composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' control automaton, together with estimation for the domains of attraction of the resulting composed controllers. This typically results in new events for the original Sequential Composition controllers. Applying these events, the cooperative control system can fulfill the tasks which are not possible to satisfy with the original controllers individually. The simulation results of an inverted pendulum system collaborating with two second-order DC motors are presented for cooperative swing-up maneuvers.
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Learning Sequential Composition Control
IEEE transactions on cybernetics, 2015Co-Authors: Esmaeil Najafi, Robert Babuska, Gabriel A D LopesAbstract:Sequential Composition is an effective supervisory control method for addressing control problems in nonlinear dynamical systems. It executes a set of controllers Sequentially to achieve a control specification that cannot be realized by a single controller. As these controllers are designed offline, Sequential Composition cannot address unmodeled situations that might occur during runtime. This paper proposes a learning approach to augment the standard Sequential Composition framework by using online learning to handle unforeseen situations. New controllers are acquired via learning and added to the existing supervisory control structure. In the proposed setting, learning experiments are restricted to take place within the domain of attraction (DOA) of the existing controllers. This guarantees that the learning process is safe (i.e., the closed loop system is always stable). In addition, the DOA of the new learned controller is approximated after each learning trial. This keeps the learning process short as learning is terminated as soon as the DOA of the learned controller is sufficiently large. The proposed approach has been implemented on two nonlinear systems: 1) a nonlinear mass-damper system and 2) an inverted pendulum. The results show that in both cases a new controller can be rapidly learned and added to the supervisory control structure.
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an application of Sequential Composition control to cooperative systems
International Workshop on Robot Motion and Control, 2015Co-Authors: Esmaeil Najafi, Robert Babuska, Gabriel A D LopesAbstract:Sequential Composition is an effective supervisory control approach for addressing challenging control problems on complex dynamical systems. It constructs a back-chaining sequence of controllers to achieve the control objective using simple local controllers. Although Sequential Composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard Sequential Composition by introducing a novel approach to compose multiple Sequential Composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' supervisory finite-state machines, together with the estimation of the domains of attraction of the composed controllers. We present the simulation and experimental results of an inverted pendulum system collaborating with a second-order DC motor for cooperative swing-up maneuvers.
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RoMoCo - An application of Sequential Composition control to cooperative systems
2015 10th International Workshop on Robot Motion and Control (RoMoCo), 2015Co-Authors: Esmaeil Najafi, Robert Babuska, Gabriel A D LopesAbstract:Sequential Composition is an effective supervisory control approach for addressing challenging control problems on complex dynamical systems. It constructs a back-chaining sequence of controllers to achieve the control objective using simple local controllers. Although Sequential Composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard Sequential Composition by introducing a novel approach to compose multiple Sequential Composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' supervisory finite-state machines, together with the estimation of the domains of attraction of the composed controllers. We present the simulation and experimental results of an inverted pendulum system collaborating with a second-order DC motor for cooperative swing-up maneuvers.
George J Pappas - One of the best experts on this subject based on the ideXlab platform.
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Sequential Composition of robust controller specifications
International Conference on Robotics and Automation, 2012Co-Authors: George J PappasAbstract:We present a general notion of robust controller specification and a mechanism for Sequentially composing them. These specifications form tubular abstractions of the trajectories of a system in different control modes, and are motivated by the techniques available for certifying the performance of low-level controllers. The notion of controller specification provides a rigorous interface for connecting a planner and lower-level controllers that are designed independently. With this approach, the planning layer does not integrate the closed-loop system dynamics and does not require the knowledge of how the controllers operate, but relies only on the specifications of the output tracking performance achieved by these controllers. The control layer aims at satisfying specifications that account quantitatively for robustness to unmodeled dynamics and various sources of disturbance and sensor noise, so that this robustness does not need to be revalidated at the planning level. As an illustrative example, we describe a randomized planner that composes different controller specifications from a given database to guarantee that any corresponding sequence of control modes steers a robot to a given region while avoiding obstacles.
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ICRA - Sequential Composition of robust controller specifications
2012 IEEE International Conference on Robotics and Automation, 2012Co-Authors: George J PappasAbstract:We present a general notion of robust controller specification and a mechanism for Sequentially composing them. These specifications form tubular abstractions of the trajectories of a system in different control modes, and are motivated by the techniques available for certifying the performance of low-level controllers. The notion of controller specification provides a rigorous interface for connecting a planner and lower-level controllers that are designed independently. With this approach, the planning layer does not integrate the closed-loop system dynamics and does not require the knowledge of how the controllers operate, but relies only on the specifications of the output tracking performance achieved by these controllers. The control layer aims at satisfying specifications that account quantitatively for robustness to unmodeled dynamics and various sources of disturbance and sensor noise, so that this robustness does not need to be revalidated at the planning level. As an illustrative example, we describe a randomized planner that composes different controller specifications from a given database to guarantee that any corresponding sequence of control modes steers a robot to a given region while avoiding obstacles.
M. Mert Ankarali - One of the best experts on this subject based on the ideXlab platform.
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feedback motion planning for a dynamic car model via random Sequential Composition
Systems Man and Cybernetics, 2019Co-Authors: Melih Ozcan, M. Mert AnkaraliAbstract:Autonomous cars and car-like robots have gained huge popularity recently due to the recent advancements in technology and AI industry. Motion and path planning is one of the most fundamental problems for such systems. In the literature, kinematic models are widely adopted for planning and control for these type of robots due to their simplicity (control and analysis) and fewer computational requirements. Though, applicability of kinematic models are limited to very low speeds or some specific cases, which can be easily violated in real life scenarios. Furthermore, most of the dynamical car models found in the literature assume that they are driven only in forward direction, at constant high speeds. In this study, we present a car model that captures the dynamics of both forward and backward driving, at low and high speeds. After creating the car model, we addressed the motion planning problem on this model, where we adopted a framework which combines Sequential Composition of Controllers (SCC) and Rapidly Exploring Random Trees (RRT). We performed simulations to show the effectiveness and robustness of our method, and results are promising for future experimental studies.
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Feedback motion planning of unmanned surface vehicles via random Sequential Composition
Transactions of the Institute of Measurement and Control, 2019Co-Authors: Emre Ege, M. Mert AnkaraliAbstract:In this paper, we propose a new motion planning method that aims to robustly and computationally efficiently solve path planning and navigation problems for unmanned surface vehicles (USVs). Our approach is based on synthesizing two different existing methodologies: Sequential Composition of dynamic behaviours and rapidly exploring random trees (RRT). The main motivation of this integrated solution is to develop a robust feedback-based and yet computationally feasible motion planning algorithm for USVs. In order to illustrate the main approach and show the feasibility of the method, we performed simulations and tested the overall performance and applicability for future experimental applications. We also tested the robustness of the method under relatively extreme environmental uncertainty. Simulation results indicate that our method can produce robust and computationally feasible solutions for a broad class of USVs.
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SMC - Feedback Motion Planning For a Dynamic Car Model via Random Sequential Composition
2019 IEEE International Conference on Systems Man and Cybernetics (SMC), 2019Co-Authors: Melih Ozcan, M. Mert AnkaraliAbstract:Autonomous cars and car-like robots have gained huge popularity recently due to the recent advancements in technology and AI industry. Motion and path planning is one of the most fundamental problems for such systems. In the literature, kinematic models are widely adopted for planning and control for these type of robots due to their simplicity (control and analysis) and fewer computational requirements. Though, applicability of kinematic models are limited to very low speeds or some specific cases, which can be easily violated in real life scenarios. Furthermore, most of the dynamical car models found in the literature assume that they are driven only in forward direction, at constant high speeds. In this study, we present a car model that captures the dynamics of both forward and backward driving, at low and high speeds. After creating the car model, we addressed the motion planning problem on this model, where we adopted a framework which combines Sequential Composition of Controllers (SCC) and Rapidly Exploring Random Trees (RRT). We performed simulations to show the effectiveness and robustness of our method, and results are promising for future experimental studies.
