Stochastic model predictive control of time-variant nonlinear systems with imperfect state information

Weissel, Florian; Schreiter, T.; Huber, Marco F.; Hanebeck, Uwe D.

doi:10.1109/mfi.2008.4648105

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…Future work will be concerned with the extension of this method by additionally incorporating properties of the measurement process in the planning procedure as in [19]. This allows to consider systematic errors not only in the system model but also systematic measurement errors as they often occur in real-world applications.…”

Section: Discussionmentioning

confidence: 99%

Nonlinear Model Predictive Control considering stochastic and systematic uncertainties with sets of densities

Hekler

Lyons

Noack

et al. 2010

2010 IEEE International Conference on Control Applications

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Abstract-In Model Predictive Control, the quality of control is highly dependent upon the model of the system under control. Therefore, a precise deterministic model is desirable. However, in real-world applications, modeling accuracy is typically limited and systems are generally affected by disturbances. Hence, it is important to systematically consider these uncertainties and to model them correctly. In this paper, we present a novel Nonlinear Model Predictive Control method for systems affected by two different types of perturbations that are modeled as being either stochastic or unknown but bounded quantities. We derive a formal generalization of the Nonlinear Model Predictive Control principle for considering both types of uncertainties simultaneously, which is achieved by using sets of probability densities. In doing so, a more robust and reliable control is obtained. The capabilities and benefits of our approach are demonstrated in real-world experiments with miniature walking robots.

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Section: Discussionmentioning

confidence: 99%

Nonlinear Model Predictive Control considering stochastic and systematic uncertainties with sets of densities

Hekler

Lyons

Noack

et al. 2010

2010 IEEE International Conference on Control Applications

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“…In operating a robot in a real environment, its behavior changes probabilistically due to slight fluctuations in the robot's state or errors in the actions taken at a given time. In this case, a stochastic technique is necessary for handling problems with unknown disturbances [2,3,4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Adaptability to Periodic Variable Disturbance Using Probabilistic State-Action Pair Prediction

Sugimoto¹

2016

IJNCAA

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When operating a robot in a real environment, its behavior is probabilistic because of slight transition of the robot's state or error in the action taken at a given time. In this case, it is difficult to operate the robot using rule-based-like action decision methods. Therefore, ad-hoc-like action decision methods are needed. A method is proposed for deciding on future actions based on a robot's present information. The state-action pair prediction method has been reported; it links the state and future actions of a robot using internal information. A statistical approach to state-action pair prediction has been introduced previously, in which the existence probability of a state and action in the future is calculated according to the normal distribution. This paper considers the situation where a command input is sent to an inverted pendulum. Based on this command input, the shape of the floor is changed from flat to undulating. The results of verification experiments confirm that the proposed method can adjust the shape of the floor autonomously.

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“…Also, an NMPC is designed in F. Weissel, T. Schreiter, M. F. Huber and U. D. Hanebeck (2008) for systems for which the state is not directly accessible, but has to be estimated from observations. The work D. Lyons, A. Hekler, M. Kuderer and U. D. Hanebeck (2010) presents a closed-loop NMPC for systems whose internal states are not completely accessible, incorporating the impact of possible future measurements into the planning process.…”

Section: Introductionmentioning

confidence: 99%

Stochastic MPC with applications to process control

Jurado

Millán

Quevedo

et al. 2014

International Journal of Control

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This paper presents a model predictive control formulation for Networked Control Systems subject to independent and identically distributed delays and packet dropouts. The design takes into account the presence of a communication network in the control loop, resorting to a buffer at the actuator side to store and consistently apply delayed control sequences when fresh control inputs are not available. The proposed approach uses a statistical description of transmissions to optimize the expected future control performance conditioned upon the current system state, previously calculated control packets and transmission acknowledgments. Experimental studies using a quadruple tank process illustrate the applicability of the method to process control.

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Stochastic model predictive control of time-variant nonlinear systems with imperfect state information

Cited by 11 publications

References 24 publications

Nonlinear Model Predictive Control considering stochastic and systematic uncertainties with sets of densities

Nonlinear Model Predictive Control considering stochastic and systematic uncertainties with sets of densities

Adaptability to Periodic Variable Disturbance Using Probabilistic State-Action Pair Prediction

Stochastic MPC with applications to process control

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