The Model Predictive Control (MPC) trajectory tracking problem of an unmanned quadrotor with input and output constraints is addressed. In this article, the dynamic models of the quadrotor are obtained purely from operational data in the form of probabilistic Gaussian Process (GP) models. This is ...
3、Gaussian Process for Modelling and Control of Dynamic Systems 这本书主要讲了高斯过程(GP)模型在非线性系统辨识和动态系统控制设计中的应用,这种方法在动态系统建模和工程实践中具有很大的潜力。 当然,读者可以在网站上下载所提出的一些算法的Matlab实现:Gaussian-Process-Model-based System-Identification Toolbox...
aA模型withmodelWithModel过程的模型 系统标签: predictivegaussiannonlinear非线性model模型 NonlinearPredictiveControlwithaGaussianProcessModelJuˇsKocijan1,2andRoderickMurray-Smith3,41JozefStefanInstitute,Jamova39,SI-1000Ljubljana,Sloveniajus.kocijan@ijs.si2NovaGoricaPolytechnic,NovaGorica3Dept.ComputingScience,Univers...
机译:四旋翼无人机的小波神经控制方案 F. Jurado ,S. Lopez -1 7.Gaussian Process Model Predictive Control of An Unmanned Quadrotor and spring 机译:无人四旋翼飞行器的高斯过程模型预测控制 Cao, Gang ,Lai, Edmund M-K ,Alam, Fakhrul 2017 查看全部 收藏 获取封面 代理获取 ...
Invalid JSONProject for the course Statistical Learning and Stochastic Control at University of Stuttgart
Gaussian process (GP) models based model predictive control (MPC) structure for cooperative motion planning of Unmanned Aerial and Ground Vehicle System (UAGVS) is proposed in this article. The GP models are firstly trained to describe the dynamics of UAVs and UGVs with their uncertainties. Stoch...
This paper presents a stochastic model predictive control method for linear time﹊nvariant systems subject to stateヾependent additive uncertainties modelled by Gaussian process (GP). The new method is developed by re‐building the tube‐based model predictive control framework with chance constraints ...
Trajectory tracking for autonomous surface ships using Gaussian process regression and model predictive control with BVS strategy View further author informationhttps://orcid.org/0000-0001-5091-9478Wenhe ShenView further author informationhttps://orcid.org/0000-0001-8304-9720Xinjue HuView further author...
Gaussian Process model # kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2)) # Instanciate a Gaussian Process model # kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2)) # gp = GaussianProcessRegressor(kernel=kernel, n_restarts...
(time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying ...