The availability of good continuous predictions allows control at a higher rate than that of the measurements. Recently, an online optimization approach for stochastic nmpc based on a gaussian process model was proposed. Gps have received increased attention in the machinelearning community over the past decade, and this book provides a longneeded systematic and unified treatment of theoretical and practical aspects of gps in machine learning. The predictions obtained from the gp model are then used in a model predictive control. The framework has been implemented with the principles of being flexible enough to experiment with different gp methods, optimization of gp models. Gaussian processes massachusetts institute of technology. May 11, 2015 it is also shown how the periodic structure can be exploited in the hyperparameter optimization. When such a function defines the mean response in a regression model with gaussian. Particle swarm optimization for model predictive control. The extra information provided by the gaussian process model is used in predictive control, where optimization of the control signal takes the variance information into account.
Modelling and control of dynamic systems using gaussian process models. Two main issues associated with model predictive control mpc are learning the unknown dynamics of the system and handling model uncertainties. This chapter illustrates possible application of gaussian process models within model predictive control. Nonlinear adaptive control using nonparametric gaussian process prior models. Cautious model predictive control using gaussian process. Online reduced gaussian process regression based generalized. Stochastic nonlinear model predictive control of batch processes phd description.
Danie krige, is generally credited with the first use of a gplike model in the 1950s to model the distribution of ore content in south african mines from a small number of samples. Dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc. Part of the lecture notes in computer science book series lncs, volume 3355. A framework for using gaussian process together with model predictive control for optimal control. Systems control design relies on mathematical models and these may be developed from measurement data. Gaussian process based model predictive control for linear time. Modelling and control of dynamic systems using gaussian. Computationally efficient model predictive control. The t iare used to express the uneven time sampling.
Model predictive control in this chapter we consider model predictive control mpc, an important advanced control technique for dif. Computationally efficient model predictive control algorithms. The extra information provided within gaussian process model is used in predictive control. Chapter 6 presents a series of concepts and models related to. Gaussian process models provide a probabilistic nonparametric modelling approach for blackbox identification of. This study developed a statebased gaussian process regression gpr model to monitor the filamentous sludge bulking related parameter, sludge volume index svi, in such a way that the. Learning dynamic models for open loop predictive control. Designs of feedback controllers for fluid flows based on. Nonlinear system identification with selective recursive. Learning dynamics using gaussian process regression for model predictive control was done in 19, where gaussian processes also provide additional information about the uncertainty in prediction. Gaussian processbased predictive control for periodic error.
Gaussian processes gps provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes for machine learning, carl edward rasmussen and chris williams, the mit press. Learning a gaussian process model with uncertain inputs. The gaussian process gp model has been applied to the identification of a process model. My phd projects deals with the optimal operation of batch processes employing nonlinear model predictive.
The model predictive control mpc trajectory tracking problem of an unmanned quadrotor with input and output constraints is addressed. Accurate mean value process models for modelbased engine control concepts by means of hybrid. One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of. 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 paper describes model based predictive control based on gaussian processes. Automated insulin delivery for type 1 diabetes mellitus. Gpmpc gaussian process linear model predictive control. The idea of using the learned model in predictive control is conceptually similar to 5, 6, 12, with the key difference that we use a gp to predict time varying effects.
Keywordssmodel based predictive control, nonlinear control, gaussian process models, constraint optimisation. This paper illustrates possible application of gaussian process models within modelbased predictive control. This study considers an adaptive cruise control problem of connected vehicles in the vehicular adhoc network and proposes a gaussian learningbased fuzzy predictive cruise control approach to enhance. Prediction of filamentous sludge bulking using a state.
Given any set of n points in the desired domain of your functions, take a multivariate gaussian whose covariance matrix parameter is the gram matrix of your n points with some desired kernel, and sample from that gaussian. The goal is to find a gaussian process model f x x for the unknown true dynamics, denoted by 5 x. A gaussian process can be used as a prior probability distribution over functions in bayesian inference. Stay on top of important topics and build connections by joining wolfram community groups relevant. The gp model can be represented by its mean and covariance function. In the final sections of this chapter, these methods are applied to learning in gaussian process models for regression and classification. Gaussian process based predictive control for periodic error. However, the presented method also works with any other model type, e. Once all of the bump test, and system identification activities have been performed, the complete process model is used directly in the model predictive controller. Nonlinear model predictive control for models with local information and. Read modelling and control of dynamic systems using gaussian process models by jus kocijan available from rakuten kobo. Pdf cautious model predictive control using gaussian process. What are some applications of gaussian process models. The predictive control principle is demonstrated on a simulated example of nonlinear system.
Gaussian process model based predictive control ieee xplore. After deriving the model predictive path integral control mppi algorithm, we compare it with. Stabilizing and optimizing control for timedelay systems. Introduction the demand for faulttolerant control ftc comes from safety requirements and from economics. Version 15 jmp, a business unit of sas sas campus drive cary, nc 275 15. To reduce the computational complexity, we propose a method to. Nonlinear predictive control with a gaussian process model.
Gaussian process model predictive control of an unmanned quadrotor. Zeilinger abstractgaussian process gp regression has been widely used in supervised machine learning due to its. Learningbased robust model predictive control with state. In this paper, we propose a model predictive controller mpc based on gaussian process for nonlinear systems with uncertain delays and external gaussian disturbances. A neural network approach ebook written by maciej lawrynczuk. Learning stochastically stable gaussian process statespace. Mbpc, mpc, nmpc, linear, nonlinear, model, process, process model. The primary focus for diabetes modeling in recent years has been for model predictive control in an artificial pancreas system. Suppose that we wish to control a multipleinput, multipleoutput process while satisfying inequality constraints on the. Gaussian process models provide a probabilistic nonparametric. A gaussian process based model predictive controller for. The predictive control principle is demonstrated via the control of a ph process benchmark. Gaussian process model predictive control of an unmanned. The predictions obtained from the gp model are then used in a model predictive control framework to correct the external effect.
This enables the algorithm to converge fast enough that it can be applied in a model predictive control setting. The most downloaded articles from journal of process control in the last 90 days. Predicting postprandial glucose excursions using gaussian. Apr 02, 2015 dynamic control is also known as nonlinear model predictive control nmpc or simply as nonlinear control nlc. Pdf gaussian process model based predictive control. Gaussian process model predictive control of unmanned. Gaussian process gp regression has been widely used in supervised machine learning for its flexibility and inherent ability to describe uncertainty in the prediction. A hybrid global optimization algorithm based on particle.
In this study, we construct gaussian process regression models with a combination of the maximum likelihood perceptron kernel and students t kernel as shown in equations 18. Regression and classification using gaussian process priors radford m. Using gp, the variances computed during the modelling and inference processes allow us to take model uncertainty into account. Abstract nonlinear model predictive control nmpc algorithms are based on various nonlinear models. This web site aims to provide an overview of resources concerned with probabilistic modeling, inference and learning based on gaussian processes. Sep 01, 2008 the coverage, although expected to be lower given that there is less uncertainty in the predictive process than in the parent process section 2. It is also shown how the periodic structure can be exploited in the hyperparameter optimization. Accurate mean value process models for modelbased engine. The textbook provides a general introduction to gaussian processes. Journal of process control shop books, ebooks and journals. As the guide for researchers and engineers all over the world concerned with the latest.
Explicit stochastic nonlinear predictive control based on. Cautious model predictive control using gaussian process regression lukas hewing, juraj kabzan, melanie n. In predictive control, gps were successfully applied to improve control performance when learning periodic timevarying. This process of system identification, when based on gp models, can play an integral part of control design in databased control and its description as such is an essential aspect of the text. Model predictive control mpc refers to a class of control algorithms in which a dynamic process model is used to predict and optimize process performance. Gaussian process model based predictive control jus kocijan, roderick murraysmith, carl edward rasmussen, agathe girard abstract gaussian process models provide a probabilistic nonparametric modelling approach for blackbox identifica tion of nonlinear dynamic systems. A gaussian process model is parametrized by two objects. Rupert l, sherrod v and killpack m 2016 a new soft robot control method.
Forecasting with paneldata anngaussian process model. The dirichletmultinomial model, likelihood, prior, posterior, posterior predictive, language model using bag of words. Modeling the unknown system with a gaussian process has several advantages. Gaussian process based output model predictive control in this section we present the output model predictive control formulation, based on a gaussian process nonlinear autoregressive model for prediction.
Stability of gaussian process learning based output feedback. Zeilinger, cautious model predictive control using gaussian process regression. To reduce the computational complexity, we propose a. This paper proposes the use of risksensitive costs in a model predictive controller mpc with gaussian process gp models, for more effective online learning. Gaussian process model predictive control of unknown nonlinear. We present a combination of a output feedback model predictive control scheme, which does not require full state information, and a gaussian process prediction model that is capable of online. Gps have received increased attention in the machinelearning community over the past. This process of system identification, when based on gp models, can play an integral part of.
The following optimality conditions can be found in any textbook on. Dynamic gaussian process models for model predictive control. A significant advantage of the gaussian process models is that they provide information. The predictive control principle is demonstrated on control of ph process. Gaussian predictive process models for large spatial data sets.
Probabilistic forecasting of the disturbance storm time. Model predictive control mpc of an unknown system that is modelled by gaussian process gp techniques is studied. This monograph opens up new horizons for engineers and. We investigate the ability of gaussian process based mpc on handling the variable delay that follows a gaussian distribution through a properly selected observation horizon. The appeal of using gaussian process regression for model learning stems from the fact that it requires little prior process knowledge and directly provides a measure of residual model uncertainty. This is different from conventional models obtained through newtonian analysis. One advantage of these gaussian process methods is that the priors and hyperparameters of the trained models are easy to interpret. Evaluation of gaussian processes and other methods for non. Jan 21, 2012 gps actually arose out of an application. This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or intereste.
The gaussian process methods are benchmarked against several other. Stability of gaussian process learning based output. Model predictive control and optimization for papermaking. Macadams driver model 1980 consider predictive control design simple kinematical model of a. This paper describes modelbased predictive control based on gaussian processes. Gaussian process model predictive control prediction horizon internal. Gaussian process kernels for popular statespace time. Gaussian process models provide a probabilistic nonparametric modelling approach for blackbox identification of nonlinear dynamic systems. Risksensitive model predictive control with gaussian process. Complexity of online computation is a drawback of model predictive control mpc when applied to the navierstokes equations. Regression and classification using gaussian process priors. Gaussian process based iaq distribution mapping using an. Learning stochastically stable gaussian process state. The gaussian processes can highlight areas of the input space where prediction quality is poor, due to the.
Tpros is the gaussian process program written by mark gibbs and david mackay. Unscented kalman filters with gaussian process prediction and observation models. The extra information provided within gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The concept history and industrial application resource. Nlc with predictive models is a dynamic optimization approach that seeks to. Important topics covered include model predictive control from an optimal control point of view, the use of state and parameter identification for implementation of optimal adaptive control, a variational approach to development of necessary conditionsfor defining optimal control problems, and the treatment of both regulatory control and time. Firstly, the first issue of modelling unknown dynamical motions is solved by using gp models based on sampled data. We explore how the bigthree computing paradigmssymmetric multiprocessor, graphical processing units gpus, and cluster computingcan together be brought to bear on largedata gaussian. Two issues of quadrotor control without deterministic dynamical equations are addressed in this paper by using gaussian process gp based model predictive control mpc algorithm. The resulting problem of finding optimal control action sequences based on model.
Nlc with predictive models is a dynamic optimization approach that seeks to follow. From lower request of modeling accuracy and robustness to complicated process plants, mpc has been widely accepted in many practical fields. Dynamic gaussian process models for model predictive control of vehicle roll by david j. Books and resources gaussian processes for machine learning c. Abstract computed torque control allows the design of considerably more precise, energyefficient and compliant controls for robots. Gaussian process model based predictive control 2003. Automated insulin delivery for type 1 diabetes mellitus patients using gaussian processbased model predictive control. Gaussian process, model predictive control, stability. Gaussian process model predictive control of unknown non. The basic ideaof the method isto considerand optimizetherelevant variables, not. Gaussian process based output model predictive control in this section we present the output model predictive control formulation, based on a gaussian process nonlinear autoregressive.
However, the major obstacle is the requirement of an accurate model for torque generation, which cannot be obtained in some cases using rigidbody formulations due to unmodeled nonlinearities, such as complex friction or actuator dynamics. Download nonlinear model predictive control theory and algorithms communications and control engineering ebook free in pdf and epub format. Gaussian process model based predictive control ju. The basic mpc concept can be summarized as follows. The model predictive control mpc trajectory tracking problem of an unmanned quadrotor with input and output constraints is. Gaussian processbased predictive control for periodic. Pdf predictive control with gaussian process models.
Massively parallel approximate gaussian process regression. Gaussian process model based predictive control request pdf. Learning dynamic models for open loop predictive control of soft robotic manipulators. Pdf gaussian process gp regression has been widely used in supervised machine learning for its flexibility and inherent ability to describe. Gaussian process model predictive control of unknown nonlinear systems abstract. Gaussian process gp can directly capture the model uncertainty.
Wolfram community forum discussion about forecasting with paneldata anngaussian process model. Using gp, the variances computed during the modelling and inference. Neal university of toronto, canada summary gaussian processes are a natural way of specifying prior distributions over functions of one or more input variables. Gaussian process based iaq distribution mapping using an interactive service robot. Model predictive control mpc of an unknown system that is modelled by gaussian process gp techniques is studied in this paper. Nonlinear model predictive control for models with local information and uncertainties. Gaussian learningbased fuzzy predictive cruise control. In safetycritical applications, there is always some requirement for a safe backup in case the nominal system fails. Pdf this paper describes modelbased predictive control based on gaussian processes. Bayesian methods for surrogate modeling and dimensionality. Recedinghorizon, or model predictive, linear quadratic lq, linearquadraticgaussian and h. Gaussian process models and its variants have been applied in a number of diverse.245 330 1425 201 1492 127 1559 732 892 1225 538 330 1467 1101 40 1016 386 454 415 1440 793 1568 744 354 506 1368 495 349 642 116 893