Wave energy holds significant promise as a renewable energy source due to the consistent and predictable nature of ocean waves. However, optimizing wave energy devices is essential for achieving competitive viability ...
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Wave energy holds significant promise as a renewable energy source due to the consistent and predictable nature of ocean waves. However, optimizing wave energy devices is essential for achieving competitive viability in the energy market. This paper presents the application of a nonlinear model predictive controller (MPC) to enhance the energy extraction of a heaving point absorber. The wave energy converter (WEC) model accounts for the nonlinear dynamics and static Froude-Krylov forces, which are essential in accurately representing the system's behavior. The nonlinear MPC is tested under irregular wave conditions within the power production region, where constraints on displacement and the power take-off (PTO) force are enforced to ensure the WEC's safety while maximizing energy absorption. A comparison is made with a linear MPC, which uses a linear approximation of the Froude-Krylov forces. The study comprehensively compares power performance and computational costs between the linear and nonlinear MPC approaches. Both MPC variants determine the optimal PTO force to maximize energy absorption, utilizing (1) a linear WEC model (LMPC) for state predictions and (2) a nonlinearmodel (NLMPC) incorporating exact Froude-Krylov forces. Additionally, the study analyzes four controller configurations, varying the MPC prediction horizon and re-optimization time. The results indicate that, in general, the NLMPC achieves higher energy absorption than the LMPC. The nonlinearmodel also better adheres to system constraints, with the linear model showing some displacement violations. This paper further discusses the computational load and power generation implications of adjusting the prediction horizon and re-optimization time parameters in the NLMPC.
This paper develops a Lyapunov-based nonlinear model predictive control (NMPC) method for the path-following task of bevel-tip flexible needle in 3D environments. First, a nonholonomic and nonlinear kinematic model of...
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This paper develops a Lyapunov-based nonlinear model predictive control (NMPC) method for the path-following task of bevel-tip flexible needle in 3D environments. First, a nonholonomic and nonlinear kinematic model of the bevel-tip flexible needle system is established to describe the needle inserting and steering motion. Then, an efficient NMPC strategy is designed to deal with the system nonlinearity based on the kinematic model and further guide the flexible needle to accurately track the desired 3-dimensional path. Specifically, the control Lyapunov function is also integrated into the NMPC framework to ensure the operational safety of the bevel-tip flexible needle system in human tissue. Finally, simulation experiments are carried out to demonstrate the effectiveness of the proposed approach.
This paper presents a novel control parameter optimisation methodology for nonlinear model predictive control for floating offshore wind turbine operation, computing optimisation weights as environment conditions depe...
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This paper presents a novel control parameter optimisation methodology for nonlinear model predictive control for floating offshore wind turbine operation, computing optimisation weights as environment conditions dependent variables. The main objective is to reduce the required time to define the optimal control parameters for the nonlinearcontrol strategy, using an automated approach. To achieve this, an optimisation methodology based on extreme operational gust conditions is applied by employing a Random Walk-type Monte Carlo procedure. The primary aim is to introduce an advanced control design approach that addresses concerns related to the efficient power generation and longevity of floating systems, particularly considering the growing scale of wind turbines and the dynamic behaviour of floating platforms, which increase the system overall costs. The resulting optimised controller is also evaluated against state-of-the-art feedback-based control strategies in different operational environmental conditions.
In the present work, nonlinear model predictive control of a spacecraft equipped with reaction wheels as the actuators that generate the driving torque for the system is studied. The spacecraft body is also exposed to...
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ISBN:
(纸本)9781665454520
In the present work, nonlinear model predictive control of a spacecraft equipped with reaction wheels as the actuators that generate the driving torque for the system is studied. The spacecraft body is also exposed to external disturbance originated from the existence of off-center in the system structure. The system configuration manifold is Lie group SO(3). Discrete equations of motion of the system, referred to as Lie group variational integrators and the NMPC formulation are much more complicated due to the terms added to the equations considering the effects of speed of the wheels and the angular momentum exchanges between the wheels and the spacecraft body and also due to the effects of external disturbances. However, the model of the system and consequently, the obtained results are much more realistic in return. The necessary conditions of optimality are extracted and solved using indirect shooting method based on sensitivity derivatives. Since the equations of motion are established based on Lie group properties, the geometric structure of the system is preserved in long time integration. In addition, the extracted equations are symplectic and momentum preserving and show good energy behavior. The system of a spacecraft with three reaction wheels with the off-center in the z-direction which impose external disturbance to the system is simulated using the extracted equations for NMPC control of spacecraft on SO(3) and the simplification of sensitivity equations is used to reduce the computation time of optimizations. The obtained results show that our method is able to bring the system to the zero position even when there is an off-center in the yaw direction of the spacecraft by structure.
In this paper, nonlinear model predictive control (NMPC) of an Unmanned Aerial Vehicle (UAV) is used to avoid static obstacles. To reach this objective, a quaternion-based model of a UAV with 6 Degrees of Freedom (6-D...
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ISBN:
(纸本)9781665454520
In this paper, nonlinear model predictive control (NMPC) of an Unmanned Aerial Vehicle (UAV) is used to avoid static obstacles. To reach this objective, a quaternion-based model of a UAV with 6 Degrees of Freedom (6-DoF) is provided. The control effort, as well as a quadratic form of states, are considered as the cost function. Obstacles are introduced as optimization constraints besides some general and stability constraints. Two different scenarios are investigated;one illustrates trajectory tracking with an obstacle on the way while the other contains a path planning of the UAV in a hostile environment containing a cube and a sphere. The performance of the controller, both in processing time and path tracking, is promising because of the OpEn framework, the engine utilized here with PANOC-based algorithm.
A classic way to design a nonlinear model predictive control (NMPC) scheme with guaranteed stability is to incorporate a terminal cost and a terminal constraint into the problem formulation. While a long prediction ho...
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A classic way to design a nonlinear model predictive control (NMPC) scheme with guaranteed stability is to incorporate a terminal cost and a terminal constraint into the problem formulation. While a long prediction horizon is often desirable to obtain a large domain of attraction and good closed-loop performance, the related computational burden can hinder its real-time deployment. In this article, we propose an NMPC scheme with prediction horizon N=1$$ N=1 $$ and no terminal constraint to drastically decrease the numerical complexity without significantly impacting closed-loop stability and performance. This is attained by constructing a suitable terminal cost from data that estimates the cost-to-go of a given NMPC scheme with long prediction horizon. We demonstrate the advantages of the proposed control scheme in two benchmark control problems.
Energy management plays a decisive role in the design of efficient 48V mild hybrid drives owing to the limited amounts of energy and power. In particular, strong nonlinearities of an electrified engine air path and th...
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Energy management plays a decisive role in the design of efficient 48V mild hybrid drives owing to the limited amounts of energy and power. In particular, strong nonlinearities of an electrified engine air path and the high interaction between the 48V mild hybrid powertrain and the electrical system pose a major challenge. Current research results show the fundamental potential of optimization-based energy management strategies. However there is a lack of optimization-based solutions that allow online direct control of the electrified air path and the electric motor. In this context, this study presents a nonlinear model predictive control (NMPC) approach for a 48V mild hybrid powertrain with an electric supercharger, which is able to simultaneously improve the response behavior and energy consumption. The control concept is developed on the basis of a detailed system description. The special features of optimization-based control for the degrees of freedom of the powertrain, i.e., the belt starter generator (BSG) and the electrified air path through a throttle, waste gate, and electric supercharger (eC), are addressed. After an analysis and verification of the control properties, the NMPC is evaluated in dynamic driving cycle simulations through a comparison with a state-of-the-art rule-based control strategy. The investigations show that the NMPC is able to control the system well even under transient situations with a high influence of disturbance variables. In addition, the NMPC allows a specific and fuel saving use of the 48V system while improving the driving dynamics at the same time.
We provide insights into the structure of the set of active constraints arising for optimal solutions to nonlinear model predictive control problems along a shrinking horizon. The principle of optimality combined with...
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We provide insights into the structure of the set of active constraints arising for optimal solutions to nonlinear model predictive control problems along a shrinking horizon. The principle of optimality combined with a particular order of the constraints allows the prediction of the future active sets without solving the corresponding optimization problem. By describing the development of optimal active sets along a shrinking horizon, we state an important relationship for transferring ideas such as dynamic programming approaches from the linear to the nonlinear case. We further use the information about active and inactive constraints to rearrange and remove constraints of the original nonlinear program as described in previous work and thus simplify the problem. Numerical experiments show for the problem class treated here that the inherent robustness coming with the regional characteristic of the active sets with respect to the state space makes this approach useful also if uncertainties are present.
This study proposes a control structure based on imitation learning (IL) of nonlinear model predictive control (NMPC) for vehicle collision avoidance systems. An NMPC was employed to achieve maximum collision avoidanc...
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This study proposes a control structure based on imitation learning (IL) of nonlinear model predictive control (NMPC) for vehicle collision avoidance systems. An NMPC was employed to achieve maximum collision avoidance ability by integrated steering and braking, then later imitated by a deep neural network (DNN) to satisfy real-time capability. Previous studies that imitate NMPC have proven its control performance and computation efficiency. However, there were limitations in applying to vehicle collision avoidance systems. Despite its dangerous situation, data set for imitation should be obtained by experiments using the controlled plant, and weaknesses in handling model parameters were shown. Therefore, this article proposes a novel IL-based control structure suitable for collision avoidance systems that overcame such limitations by building a feedforward feedback structure so that the data set trained for imitation can be made offline and applying an input dimensionalization process to ensure robustness to parameter changes. CarSim-based human-vehicle interactive simulation experiments demonstrated that the proposed IL-based control structure had no issue applying the offline trained DNN in the simulation while showing robustness to parameter changes.
Active Debris removal using robotic manipulators onboard satellites is a promising way of cleaning up the space junk. However, complexities and non-linearities associated with the control of such coupled space-based s...
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ISBN:
(纸本)9781713867890
Active Debris removal using robotic manipulators onboard satellites is a promising way of cleaning up the space junk. However, complexities and non-linearities associated with the control of such coupled space-based systems present difficulties in their feasible implementation. Lack of a fixed base arises serious problems in controlling the space manipulators for precision tasks like capture of an orbiting space debris. This paper presents systematic modelling and control approaches for Rotation floating space robots in order to draw a comparison between them while tracking a moving target representing autonomous debris capture. We propose a nonlinear model predictive controller (NMPC) for the space robot in order to design an optimal path that the end-effector can follow while being controlled to capture the target. To the best of the knowledge of the authors, such a controller has not been tested for a Rotation floating space robot before. Further, the current work implements and reviews one of the most commonly used Transpose Jacobian Cartesian (TJC) controller for Rotation floating space robots through the use of Generalized Jacobian Matrix (GJM). The results provide sufficient evidence of the superior performance of the nonlinear model predictive controller over the TJC controller. Finally, the current work also implements the same nonlinear model predictive controller on a more popular state of the art Free floating space robot and compares it with the Rotation floating space robot.
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