Learning References with Gaussian Processes in Model Predictive Control applied to Robot Assisted Surgery

Matschek, Janine; Gonschorek, Tim; Hanses, Magnus; Elkmann, Norbert; Ortmeier, Frank; Findeisen, Rolf

doi:10.23919/ecc51009.2020.9143600

Cited by 16 publications

(10 citation statements)

References 20 publications

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“…The prediction models of different governing equations in MPC have a different effect on the path tracking control. The Gaussian model considers the Gaussian distribution as the model parameter [74]. It has a better control performance when dealing with periodically time-varying disturbances.…”

Section: Theoretical Basis For Modelingmentioning

confidence: 99%

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

et al. 2022

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The learning-based model predictive control (LB-MPC) is an effective and critical method to solve the path tracking problem in mobile platforms under uncertain disturbances. It is well known that the machine learning (ML) methods use the historical and real-time measurement data to build data-driven prediction models. The model predictive control (MPC) provides an integrated solution for control systems with interactive variables, complex dynamics, and various constraints. The LB-MPC combines the advantages of ML and MPC. In this work, the LB-MPC technique is summarized, and the application of path tracking control in mobile platforms is discussed by considering three aspects, namely, learning and optimizing the prediction model, the controller design, and the controller output under uncertain disturbances. Furthermore, some research challenges faced by LB-MPC for path tracking control in mobile platforms are discussed.

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Section: Theoretical Basis For Modelingmentioning

confidence: 99%

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

et al. 2022

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“…We end this section by mentioning that fine tuning or learning these driver models is out-of-scope and will be subject to future research. Inspired by [32], we intend to use Gaussian processes -a powerful tool in machine learning. 1).…”

Section: Stochastic Driver Modelsmentioning

confidence: 99%

AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle

Teodorescu¹,

Carlson²

2022

Preprint

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In this article we design a physically-inspired model-based assist-as-needed semi-autonomous control (ASC) algorithm to address the problem of safely driving a vehicle (a power wheelchair) in an environment with static obstacles. Once implemented online, the proposed algorithm requires limited computing power and relies on pre-computed (offline) maps (look-up tables). These are readily available by implementing policy iteration that minimizes the expected time to termination (safely stopping near an obstacle), by taking into account: (i) the vehicle dynamics; (ii) the drivers' intention modeled as three separate stochastic processes. We call them the expert driver, the naughty child and the blind driver models.A study with healthy participants confirmed that ASC outperforms a baseline rule-based control (a statistically significant result).

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“…GPs can be trained (as e.g. done in (Matschek et al, 2020) or correlations between the outputs can be modelled, see e.g. (Rasmussen and Williams, 2006) (Chapter 9.1) or (Salzmann and Urtasun, 2010), and references therein.…”

Section: Gps As Reference Predictorsmentioning

confidence: 99%

“…Gaussian processes were used in Maiworm et al (2018) as a feedforward controller for quantum dot microscopy. In (Klenske et al, 2016) Gaussian processes are used to provide external signals to a model predictive controller correcting the orientation of an astronomic telescope, while in (Matschek et al, 2020) in minimally invasive surgery are modelled via Gaussian processes and are provided to a predictive motion compensation controller. Here, we show how Gaussian processes can be trained to model an external reference signal based on (noisy) measurement data and extrapolate its evolution into the future while guaranteeing trackability of the reference for the controlled system, i.e.…”

Section: Introductionmentioning

confidence: 99%

Constrained Gaussian Process Learning for Model Predictive Control

Matschek¹,

Himmel²,

Sundmacher³

et al. 2019

Preprint

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Many control tasks can be formulated as a tracking problem of a known or unknown reference signal. Examples are movement compensation in collaborative robotics, the synchronisation of oscillations for power systems or reference tracking of recipes in chemical process operation. Tracking performance as well as guaranteeing stability of the closed loop strongly depends on two factors: Firstly, it depends on whether the future desired tracking reference signal is known and, secondly, whether the system can track the reference at all. This paper shows how to use machine learning, i.e. Gaussian processes, to learn a reference from (noisy) data, while guaranteeing trackability of the modified desired reference predictions in the framework of model predictive control. Guarantees are provided by adjusting the hyperparameters via a constrained optimization. Two specific scenarios, i.e. asymptotically constant and periodical references, are discussed.

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Learning References with Gaussian Processes in Model Predictive Control applied to Robot Assisted Surgery

Cited by 16 publications

References 20 publications

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms

AssistMe: Policy iteration for the longitudinal control of a non-holonomic vehicle

Constrained Gaussian Process Learning for Model Predictive Control

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