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检索条件"主题词=Nonlinear Model Predictive Control"

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model predictive Path-Following for Constrained nonlinear Systems

Model Predictive Path-Following for Constrained Nonlinear Sy...

引用

Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： T.Faulwasser B.Kern R.Findeisen Institute for Automation Engineering Otto-von-Guericke University Magdeburg

We propose a model predictive control approach to path-following problems of constrained nonlinear *** directly consider input and state ***,we introduce an extended corridor path-following problem,which allows to add spatial degrees of freedom to the path *** give sufficient stability conditions for predictive solutions to 1d and corridor path-following *** illustrate the performance of our approach we discuss the example of an autonomous vehicle subject to input constraints.

关键词： nonlinear model predictive control corridor path-following input and state constraints stability

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Small Unmanned Helicopter Autorotation using Non-linear model predictive control

Small Unmanned Helicopter Autorotation using Non-linear Mode...

引用

2010 49th IEEE Conference on Decision and control

作者： Konstantinos Dalamagkidis Kimon P. Valavanis Les A. Piegl Computer Science and Engineering Department University of South Florida Electrical and Computer Engineering Department University of Denver

ISBN: (纸本)9781424477456

Small unmanned helicopters are suitable for a variety of applications including search and rescue, surveillance, communications, traffic monitoring as well as inspection of buildings, power lines and bridges. This paper presents an on-line, model-based, real-time autonomous autorotation controller, tailored for small helicopters. The approach selected is based on non-linear model-predictive control techniques that allow constraint handling and provide relatively good performance. The non-linear optimization is handled by a specially-designed recurrent neural network so that fast convergence is achieved. The goal of this controller is to eliminate the risk to people on the ground for failures that can be accommodated via autorotation. This is accomplished by appropriately lowering the kinetic energy of the helicopter during the last phase of its descent.

关键词： AUTOROTATION nonlinear model predictive control Small unmanned helicopter constraint handling controllers kinetic energy descent Buildings on-line model

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Robust model predictive control of a Laboratory Two-Tank System

Robust Model Predictive Control of a Laboratory Two-Tank Sys...

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American control Conference

作者： Monika Bakosova Juraj Oravec Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Radlinskeho 9 812 37 Bratislava Slovakia

ISBN: (纸本)9781479932757

Robust model predictive control was applied for the laboratory two-tank system. The controlled process is modeled as the uncertain polytopic system for the controller design. Two robust model predictive control design strategies were used. The first strategy uses the single Lyapunov function and the second one assumes the parameter-dependent Lyapunov functions. The design problem is converted to the solution of the system of linear matrix inequalities and a simple algorithm is also presented. The controllers designed using both strategies were tested on the laboratory process. The results confirmed that using the parameter-dependent Lyapunov function in the robust model predictive controller design improved the control performance in comparison with the single Lyapunov function based strategy.

关键词： Liapunov function nonlinear model predictive control model predictive control predictive control design problems control performance controller design controllers linear matrix inequalities

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Stochastic control with input and state constraints: a relaxation technique to ensure feasibility

Stochastic control with input and state constraints: a relax...

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IEEE Annual Conference on Decision and control

作者： Luca Deori Simone Garatti Maria Prandini Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano via Ponzio 34/5 20133 Milano Italy

ISBN: (纸本)9781479978878

We consider the problem of designing a finite-horizon control policy for a stochastic linear system subject to probabilistic constraints on both input and state variables. When the disturbance has unbounded support, a feasibility issue may arise due to the presence of the state constraint. In this paper, we address this issue by introducing a suitable relaxation of the original problem that ensures feasibility. The relaxation is such that the original state constraint is enforced whenever is possible;otherwise, the control that pushes the state closest to the constraint is chosen. This involves formulating a cascade of two chance-constrained optimization problems, which are tackled through a scenario-based randomized scheme expressly tailored to the problem at hand. The theoretical properties of the obtained solution are investigated and it is shown that randomization allows one to achieve computational tractability. The proposed approach finds immediate application to stochastic model predictive control.

关键词： Relaxation Techniques Constraint Ensure original problem Stochastic STOCHASTIC control State variable Randomisation nonlinear model predictive control ENTERING

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predictive Tracking for Respiratory Induced Motion Compensation in Adaptive Radiotherapy

Predictive Tracking for Respiratory Induced Motion Compensat...

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2006 UKACC control 2006 Mini Symposia

作者： Piotr Skworcow Devi Putra Abdelhamid Sahih John Goodband Olivier C.L. Haas Keith J. Burnham John A. Mills Control Theory and Applications Centre Coventry University UK University Hospitals Coventry and Warwickshire NHS Trust Coventry UK

ISBN: (纸本)9780863416705;0863416705

This paper considers a compensation strategy for respiratory-induced tumour motion for adaptive radiotherapy using a controlled patient support system (PSS). A model predictive control (MPC) scheme for the PSS to track the tumour motion is proposed together with two methods for predicting tumour motion, including a Kalman filter and neural networks. Simulation results using a clinical data set consisting of 27 traces of respiratory motion show the potential of the proposed control strategy

关键词： Adaptive radiotherapy Kalman filter model predictive control Neural networks Tracking control Tumour motion Kalman filters tracking control Neural network predictive control Radiotherapy compensated model predictive control PROBABILISTIC SAFETY STUDY control strategy distributed model predictive control nonlinear model predictive control Tumours

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On the Synchronization Problem for the Stabilization of Networked control Systems over Nondeterministic Networks

On the Synchronization Problem for the Stabilization of Netw...

引用

2009 American control Conference (ACC 2009)

作者： P. Varutti R. Findeisen Institute of Automation Engineering Otto-von-Guericke University 39016 Magdeburg Germany

ISBN: (纸本)9781424445233

In recent years, networked control systems have gained the attention of the control community, since they allow to re-use the preexisting infrastructure therefore reducing deployment time and costs. Unfortunately, they also introduce new control challenges due to the nondeterministic network behavior. predictive and model-based approaches can be used to compensate both delays and packet dropouts. However, a common time-frame among the involved components -sensors, actuators, plants and controllers- is required. This brings normally the necessity of keeping inner-clocks synchronized, which at the current state of art can be hard to realize. In this paper, a possible solution for the synchronization problem is presented. The idea is to keep using predictive techniques but utilizing a unique inner-clock on the system side. Assuming that actuator and sensor are directly connected to the system, the packets are time stamped, the delays are bounded, the maximum round-trip-time is known, and a limited amount of information is lost, it is possible to use model predictive control to stabilize the closed loop system by compensating delays and packet dropouts.

关键词： nonlinear continuous systems networked control systems stability nondeterministic networks packet dropouts nonlinear model predictive control Networked control systems state of the art control systems Packet actuators dropouts Student Dropouts Closed loop systems delay current status Arts

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Surface Excavation with model predictive control

Surface Excavation with Model Predictive Control

引用

2010 49th IEEE Conference on Decision and control

作者： Rohan C. Shekhar Jan M. Maciejowski Control Group Department of Engineering University of Cambridge CP2-1PZ UK

ISBN: (纸本)9781424477456

This paper describes the application of variable-horizon model predictive control to trajectory generation in surface excavation. A nonlinear dynamic model of a surface mining machine digging in oil sand is developed as a test platform. This model is then stabilised with an inner-loop controller before being linearised to generate a prediction model. The linear model is used to design a predictive controller for trajectory generation. A variable horizon formulation is augmented with extra terms in the cost function to allow more control over digging, whilst still preserving the guarantee of finite-time completion. Simulations show the generation of realistic trajectories, motivating new applications of variable horizon MPC for autonomy that go beyond the realm of vehicle path planning.

关键词： Digging model predictive control predictive control EXCAVATION predictive controllers nonlinear model predictive control trajectory Autonomy Horizon

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model predictive control using affine disturbance feedback for constrained linear system

Model Predictive Control using affine disturbance feedback f...

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Proceedings of the 46th IEEE Conference on Decision and control, Pages 3220-3677

作者： C. Wang C.J. Ong Melvyn Sim Department of Mechanical Engineering National University of Singapore Singapore Singapore-MIT Alliance Department of Mechanical Engineering National University of Singapore Alliance Singapore Singapore-MIT Alliance Business School National University of Singapore Alliance Singapore

ISBN: (纸本)9781424414970;1424414970

This paper proposes a control parametrization under model predictive control framework for constrained linear discrete time systems with bounded additive disturbances. The proposed approach has the same feasible domain as that obtained from parametrization over the family of time-varying state feedback policies. Under the proposed control law, the closed-loop system is stable in the sense that the state converges, in time, to a bounded set that has a characterization determined by a feedback gain.

关键词： Linear system model predictive control predictive control feedback gain parameterization State feedback Affine In-law control law distributed model predictive control nonlinear model predictive control Bounded discrete time systems Feedback

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Deep Koopman Operator for model predictive control

Deep Koopman Operator for Model Predictive Control

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第35届中国过程控制会议

作者： Tengfei Li Yuhong Wang College of Control Science and Engineering China University of Petroleum

The paper introduces a Koopman operator-based method designed to expedite model predictive control(MPC) for nonlinear systems, which are traditionally burdened by the need to solve complicated optimization problems. By leveraging the Koopman operator theory, the research transforms nonlinear dynamics into a linear framework in a higher-dimensional space, even considering system inputs. A deep learning architecture is then proposed to infer the necessary Koopman embeddings and operator, incorporating residual connections to refine the model's accuracy. The derived linear model underpins the development of an MPC controller, which is empirically tested on an anaerobic digestion reactor. Results confirm that the method markedly enhances computational efficiency in nonlinear MPC applications, demonstrating potential for broader implementation in complex control systems.

关键词： nonlinear model predictive control Koopman operator deep neural network

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model predictive control applied to a river system with two reaches

Model Predictive Control applied to a river system with two ...

引用

IEEE Annual Conference on Decision and control

作者： Maarten Breckpot Oscar Mauricio Agudelo Bart De Moor Department of Electrical Engineering SCD KU Leuven

ISBN: (纸本)9781467320658

Many control strategies can be found in literature for controlling a river system. Most of these methods focus on set-point control such that the most upstream or downstream part of each reach tracks a certain reference trajectory while minimizing the effect of disturbances. However many of the control techniques suitable for set-point control cannot be used at the same time for preventing a river from flooding when large disturbances take place. In this paper we show that model predictive control can be used for set-point control and flood control of a river system consisting of two reaches and one gate. For this we use a linearized version of the Saint-Venant equations with special attention to the gate dynamics.

关键词： set point control River systems model predictive control predictive control rivers OR gates distributed model predictive control nonlinear model predictive control Flood control

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