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Wouter Saeys - One of the best experts on this subject based on the ideXlab platform.

  • robust trajectory tracking error model based predictive control for unmanned ground vehicles
    IEEE-ASME Transactions on Mechatronics, 2016
    Co-Authors: Erkan Kayacan, Herman Ramon, Wouter Saeys
    Abstract:

    This paper proposes a new robust trajectory tracking error-based control approach for unmanned ground vehicles. A trajectory tracking error-based model is used to design a linear model predictive controller and its control action is combined with feedforward and robust control actions. The experimental results show that the proposed control structure is capable to let a Tractor–Trailer system track both linear and curvilinear target trajectories with low tracking error.

  • robust tube based decentralized nonlinear model predictive control of an autonomous Tractor Trailer system
    IEEE-ASME Transactions on Mechatronics, 2015
    Co-Authors: Erkan Kayacan, Herman Ramon, Erdal Kayacan, Wouter Saeys
    Abstract:

    This paper addresses the trajectory tracking problem of an autonomous Tractor-Trailer system by using a decentralized control approach. A fully decentralized model predictive controller is designed in which interactions between subsystems are neglected and assumed to be perturbations to each other. In order to have a robust design, a tube-based approach is proposed to handle the differences between the nominal model and real system. Nonlinear moving horizon estimation is used for the state and parameter estimation after each new measurement, and the estimated values are fed to robust tube-based decentralized nonlinear model predictive controller. The proposed control scheme is capable of driving the Tractor-Trailer system to any desired trajectory ensuring high control accuracy and robustness against neglected subsystem interactions and environmental disturbances. The experimental results show an accurate trajectory tracking performance on a bumpy grass field.

  • Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System
    IEEE Transactions on Control Systems Technology, 2015
    Co-Authors: Erkan Kayacan, Herman Ramon, Erdal Kayacan, Wouter Saeys
    Abstract:

    One of the most critical tasks in Tractor operation is the accurate steering during field operations, e.g., accurate trajectory following during mechanical weeding or spraying, to avoid damaging the crop or planting when there is no crop yet. To automate the trajectory following problem of an autonomous Tractor-Trailer system and also increase its steering accuracy, a nonlinear model predictive control approach has been proposed in this paper. For the state and parameter estimation, moving horizon estimation has been chosen since it considers the state and the parameter estimation within the same problem and also constraints both on inputs and states can be incorporated. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the mean values of the Euclidean error for the Tractor and the Trailer, respectively, are 6.44 and 3.61 cm for a straight line trajectory and 49.78 and 41.52 cm for a curved line trajectory.

  • distributed nonlinear model predictive control of an autonomous Tractor Trailer system
    Mechatronics, 2014
    Co-Authors: Erkan Kayacan, Herman Ramon, Wouter Saeys
    Abstract:

    Abstract This paper addresses the trajectory tracking problem of an autonomous Tractor–Trailer system by using a fast distributed nonlinear model predictive control algorithm in combination with nonlinear moving horizon estimation for the state and parameter estimation in which constraints on the inputs and the states can be incorporated. The proposed control algorithm is capable of driving the Tractor–Trailer system to any desired trajectory ensuring high control accuracy and robustness against environmental disturbances.

  • nonlinear modeling and identification of an autonomous Tractor Trailer system
    Computers and Electronics in Agriculture, 2014
    Co-Authors: Erkan Kayacan, Herman Ramon, Wouter Saeys
    Abstract:

    This paper presents the nonlinear modeling of the yaw and longitudinal dynamics of a Tractor-Trailer system. First, the yaw dynamic models of both the Tractor and Trailer are derived considering the lateral forces and side-slip angles. In order to be able to calculate the side-slips precisely, the relaxation length approach is preferred. Since the obtained yaw dynamic models are nonlinear, a constrained nonlinear optimization problem is formulated for the parameter estimation. Second, the longitudinal dynamic model for the system is derived based-on the static and dynamic responses. The static model consist of two inputs, the hydrostat position and the diesel engine speed, and one output, the longitudinal speed of the system. Afterwards, a dynamic model is proposed to define the dynamic effect between the output of the static model and the actual longitudinal speed. Third, the mathematical models of the steering mechanisms both for the Tractor and Trailer are identified. Consequently, a complete nonlinear dynamic model for the Tractor-Trailer system is obtained. The overall resulting model is thought to provide useful physical insight on such a complex mechatronic system, and can serve as the input for model based controller design.

Erkan Kayacan - One of the best experts on this subject based on the ideXlab platform.

  • centralized decentralized and distributed nonlinear model predictive control of a Tractor Trailer system a comparative study
    Advances in Computing and Communications, 2016
    Co-Authors: Erkan Kayacan, Joshua M Peschel, Erdal Kayacan
    Abstract:

    This paper presents centralized, decentralized and distributed nonlinear model predictive controllers design for a Tractor-Trailer system. Several comparisons are made in terms of their performances and computation time. The experimental results show that the centralized nonlinear model predictive controller has ability to let a Tractor-Trailer system follow trajectories with the lowest tracking error, while the decentralized one has the lowest computation time.

  • robust trajectory tracking error model based predictive control for unmanned ground vehicles
    IEEE-ASME Transactions on Mechatronics, 2016
    Co-Authors: Erkan Kayacan, Herman Ramon, Wouter Saeys
    Abstract:

    This paper proposes a new robust trajectory tracking error-based control approach for unmanned ground vehicles. A trajectory tracking error-based model is used to design a linear model predictive controller and its control action is combined with feedforward and robust control actions. The experimental results show that the proposed control structure is capable to let a Tractor–Trailer system track both linear and curvilinear target trajectories with low tracking error.

  • robust tube based decentralized nonlinear model predictive control of an autonomous Tractor Trailer system
    IEEE-ASME Transactions on Mechatronics, 2015
    Co-Authors: Erkan Kayacan, Herman Ramon, Erdal Kayacan, Wouter Saeys
    Abstract:

    This paper addresses the trajectory tracking problem of an autonomous Tractor-Trailer system by using a decentralized control approach. A fully decentralized model predictive controller is designed in which interactions between subsystems are neglected and assumed to be perturbations to each other. In order to have a robust design, a tube-based approach is proposed to handle the differences between the nominal model and real system. Nonlinear moving horizon estimation is used for the state and parameter estimation after each new measurement, and the estimated values are fed to robust tube-based decentralized nonlinear model predictive controller. The proposed control scheme is capable of driving the Tractor-Trailer system to any desired trajectory ensuring high control accuracy and robustness against neglected subsystem interactions and environmental disturbances. The experimental results show an accurate trajectory tracking performance on a bumpy grass field.

  • Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System
    IEEE Transactions on Control Systems Technology, 2015
    Co-Authors: Erkan Kayacan, Herman Ramon, Erdal Kayacan, Wouter Saeys
    Abstract:

    One of the most critical tasks in Tractor operation is the accurate steering during field operations, e.g., accurate trajectory following during mechanical weeding or spraying, to avoid damaging the crop or planting when there is no crop yet. To automate the trajectory following problem of an autonomous Tractor-Trailer system and also increase its steering accuracy, a nonlinear model predictive control approach has been proposed in this paper. For the state and parameter estimation, moving horizon estimation has been chosen since it considers the state and the parameter estimation within the same problem and also constraints both on inputs and states can be incorporated. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the mean values of the Euclidean error for the Tractor and the Trailer, respectively, are 6.44 and 3.61 cm for a straight line trajectory and 49.78 and 41.52 cm for a curved line trajectory.

  • distributed nonlinear model predictive control of an autonomous Tractor Trailer system
    Mechatronics, 2014
    Co-Authors: Erkan Kayacan, Herman Ramon, Wouter Saeys
    Abstract:

    Abstract This paper addresses the trajectory tracking problem of an autonomous Tractor–Trailer system by using a fast distributed nonlinear model predictive control algorithm in combination with nonlinear moving horizon estimation for the state and parameter estimation in which constraints on the inputs and the states can be incorporated. The proposed control algorithm is capable of driving the Tractor–Trailer system to any desired trajectory ensuring high control accuracy and robustness against environmental disturbances.

Herman Ramon - One of the best experts on this subject based on the ideXlab platform.

  • robust trajectory tracking error model based predictive control for unmanned ground vehicles
    IEEE-ASME Transactions on Mechatronics, 2016
    Co-Authors: Erkan Kayacan, Herman Ramon, Wouter Saeys
    Abstract:

    This paper proposes a new robust trajectory tracking error-based control approach for unmanned ground vehicles. A trajectory tracking error-based model is used to design a linear model predictive controller and its control action is combined with feedforward and robust control actions. The experimental results show that the proposed control structure is capable to let a Tractor–Trailer system track both linear and curvilinear target trajectories with low tracking error.

  • robust tube based decentralized nonlinear model predictive control of an autonomous Tractor Trailer system
    IEEE-ASME Transactions on Mechatronics, 2015
    Co-Authors: Erkan Kayacan, Herman Ramon, Erdal Kayacan, Wouter Saeys
    Abstract:

    This paper addresses the trajectory tracking problem of an autonomous Tractor-Trailer system by using a decentralized control approach. A fully decentralized model predictive controller is designed in which interactions between subsystems are neglected and assumed to be perturbations to each other. In order to have a robust design, a tube-based approach is proposed to handle the differences between the nominal model and real system. Nonlinear moving horizon estimation is used for the state and parameter estimation after each new measurement, and the estimated values are fed to robust tube-based decentralized nonlinear model predictive controller. The proposed control scheme is capable of driving the Tractor-Trailer system to any desired trajectory ensuring high control accuracy and robustness against neglected subsystem interactions and environmental disturbances. The experimental results show an accurate trajectory tracking performance on a bumpy grass field.

  • Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System
    IEEE Transactions on Control Systems Technology, 2015
    Co-Authors: Erkan Kayacan, Herman Ramon, Erdal Kayacan, Wouter Saeys
    Abstract:

    One of the most critical tasks in Tractor operation is the accurate steering during field operations, e.g., accurate trajectory following during mechanical weeding or spraying, to avoid damaging the crop or planting when there is no crop yet. To automate the trajectory following problem of an autonomous Tractor-Trailer system and also increase its steering accuracy, a nonlinear model predictive control approach has been proposed in this paper. For the state and parameter estimation, moving horizon estimation has been chosen since it considers the state and the parameter estimation within the same problem and also constraints both on inputs and states can be incorporated. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the mean values of the Euclidean error for the Tractor and the Trailer, respectively, are 6.44 and 3.61 cm for a straight line trajectory and 49.78 and 41.52 cm for a curved line trajectory.

  • distributed nonlinear model predictive control of an autonomous Tractor Trailer system
    Mechatronics, 2014
    Co-Authors: Erkan Kayacan, Herman Ramon, Wouter Saeys
    Abstract:

    Abstract This paper addresses the trajectory tracking problem of an autonomous Tractor–Trailer system by using a fast distributed nonlinear model predictive control algorithm in combination with nonlinear moving horizon estimation for the state and parameter estimation in which constraints on the inputs and the states can be incorporated. The proposed control algorithm is capable of driving the Tractor–Trailer system to any desired trajectory ensuring high control accuracy and robustness against environmental disturbances.

  • nonlinear modeling and identification of an autonomous Tractor Trailer system
    Computers and Electronics in Agriculture, 2014
    Co-Authors: Erkan Kayacan, Herman Ramon, Wouter Saeys
    Abstract:

    This paper presents the nonlinear modeling of the yaw and longitudinal dynamics of a Tractor-Trailer system. First, the yaw dynamic models of both the Tractor and Trailer are derived considering the lateral forces and side-slip angles. In order to be able to calculate the side-slips precisely, the relaxation length approach is preferred. Since the obtained yaw dynamic models are nonlinear, a constrained nonlinear optimization problem is formulated for the parameter estimation. Second, the longitudinal dynamic model for the system is derived based-on the static and dynamic responses. The static model consist of two inputs, the hydrostat position and the diesel engine speed, and one output, the longitudinal speed of the system. Afterwards, a dynamic model is proposed to define the dynamic effect between the output of the static model and the actual longitudinal speed. Third, the mathematical models of the steering mechanisms both for the Tractor and Trailer are identified. Consequently, a complete nonlinear dynamic model for the Tractor-Trailer system is obtained. The overall resulting model is thought to provide useful physical insight on such a complex mechatronic system, and can serve as the input for model based controller design.

Erdal Kayacan - One of the best experts on this subject based on the ideXlab platform.

  • centralized decentralized and distributed nonlinear model predictive control of a Tractor Trailer system a comparative study
    Advances in Computing and Communications, 2016
    Co-Authors: Erkan Kayacan, Joshua M Peschel, Erdal Kayacan
    Abstract:

    This paper presents centralized, decentralized and distributed nonlinear model predictive controllers design for a Tractor-Trailer system. Several comparisons are made in terms of their performances and computation time. The experimental results show that the centralized nonlinear model predictive controller has ability to let a Tractor-Trailer system follow trajectories with the lowest tracking error, while the decentralized one has the lowest computation time.

  • robust tube based decentralized nonlinear model predictive control of an autonomous Tractor Trailer system
    IEEE-ASME Transactions on Mechatronics, 2015
    Co-Authors: Erkan Kayacan, Herman Ramon, Erdal Kayacan, Wouter Saeys
    Abstract:

    This paper addresses the trajectory tracking problem of an autonomous Tractor-Trailer system by using a decentralized control approach. A fully decentralized model predictive controller is designed in which interactions between subsystems are neglected and assumed to be perturbations to each other. In order to have a robust design, a tube-based approach is proposed to handle the differences between the nominal model and real system. Nonlinear moving horizon estimation is used for the state and parameter estimation after each new measurement, and the estimated values are fed to robust tube-based decentralized nonlinear model predictive controller. The proposed control scheme is capable of driving the Tractor-Trailer system to any desired trajectory ensuring high control accuracy and robustness against neglected subsystem interactions and environmental disturbances. The experimental results show an accurate trajectory tracking performance on a bumpy grass field.

  • Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System
    IEEE Transactions on Control Systems Technology, 2015
    Co-Authors: Erkan Kayacan, Herman Ramon, Erdal Kayacan, Wouter Saeys
    Abstract:

    One of the most critical tasks in Tractor operation is the accurate steering during field operations, e.g., accurate trajectory following during mechanical weeding or spraying, to avoid damaging the crop or planting when there is no crop yet. To automate the trajectory following problem of an autonomous Tractor-Trailer system and also increase its steering accuracy, a nonlinear model predictive control approach has been proposed in this paper. For the state and parameter estimation, moving horizon estimation has been chosen since it considers the state and the parameter estimation within the same problem and also constraints both on inputs and states can be incorporated. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the mean values of the Euclidean error for the Tractor and the Trailer, respectively, are 6.44 and 3.61 cm for a straight line trajectory and 49.78 and 41.52 cm for a curved line trajectory.

Jing Yuan - One of the best experts on this subject based on the ideXlab platform.

  • hierarchical motion planning for multisteering Tractor Trailer mobile robots with on axle hitching
    IEEE-ASME Transactions on Mechatronics, 2017
    Co-Authors: Jing Yuan
    Abstract:

    Normally, due to the strongly coupled kinematics and under-actuated property, motion planning for a multisteering Tractor–Trailer mobile robot (MSTTMR) is a complex problem. In this paper, an MSTTMR system with on-axle hitching is considered. Motion planning for it is divided into three independent subproblems: planning a collision-free geometric path for the Tractor, transforming the geometric path into a motion trajectory of the Tractor, as well as generating motion trajectories for the Trailers, and then they are solved by means of a three-level hierarchical method. At the highest level, a geometric path in the form of a cubic spline curve is planned for the Tractor. To consider obstacle avoidance and satisfy constraints on the control inputs of the Tractor, the path parameters are optimized by an improved quantum-behaved particle swarm optimization algorithm. At the middle level, the planned path is transformed into a feasible motion trajectory of the Tractor. At the lowest level, to guide all the Trailers to track the planned path, motion planning for the Trailers is achieved by the Gauss pseudospectral method (GPM). As a result, both the Tractor and Trailers can simultaneously move along the same path, and turning maneuvers in narrow and cluttered spaces can successfully be achieved. Simulation and experimental results are presented to show the effectiveness of the proposed approach.

  • trajectory generation and tracking control for double steering Tractor Trailer mobile robots with on axle hitching
    IEEE Transactions on Industrial Electronics, 2015
    Co-Authors: Jing Yuan, Fengchi Sun, Yalou Huang
    Abstract:

    A multisteering Tractor–Trailer mobile robot (MSTTMR) is a kind of complex multibody system, which consists of a Tractor and a chain of steerable Trailers. If both the Tractor and the Trailers can track an identical geometric path, the overall width of the system is only equal to that of the Tractor or the Trailer. Thus, the robot can perform transport tasks in a narrow space. The MSTTMR with one Trailer is referred to as the double-steering Tractor–Trailer mobile robot (DSTTMR), since the system has two steering inputs. In this paper, trajectory generation and tracking control for the DSTTMR with on-axle hitching are addressed. We first propose an approach to generate the desired full-state trajectory from a desired geometric path given in the Cartesian plane, in order that the desired state trajectories of the Tractor and Trailer can form the same desired path. Then, a trajectory tracking controller is designed based on the backstepping technique to drive the system's states to converge to their desired trajectories. As a result, the Tractor and the Trailer can track the desired geometric path simultaneously. Simulation and experimental results are presented to show the effectiveness of the proposed approach.