Juš Kocijan

Dynamic systems identification with Gaussian processes

This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) prior model. This model is an example of the use of a probabilistic non-parametric modelling approach. GPs are flexible models capable of modelling complex nonlinear systems. Also, an attractive feature of this model is that the variance associated with the model response is readily obtained, and it can be used to highlight areas of the input space where prediction quality is poor, owing to the lack of data or complexity (high variance). We illustrate the GP modelling technique on a simulated example of a nonlinear system.

Predictive control with Gaussian process models

This paper describes model-based predictive control based on Gaussian processes. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. It offers more insight in variance of obtained model response, as well as fewer parameters to determine than other models. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. This property is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on a simulated example of nonlinear system.

Explicit model predictive control of gas–liquid separation plant via orthogonal search tree partitioning

Exact or approximate solutions to constrained linear model predictive control problems can be pre-computed off-line in an explicit form as a piecewise linear state feedback defined on a polyhedral partition of the state space. This leads to efficient real-time computations and admits implementation at high sampling frequencies in real-time systems with high reliability and low software complexity. In this paper, an explicit model predictive controller for a gas-liquid separation plant is designed and experimentally tested.

Nonlinear predictive control with a gaussian process model

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This chapter illustrates possible application of Gaussian process models within 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. The predictive control principle is demonstrated via the control of a pH process benchmark.

Derivative observations used in predictive control

Gaussian processes provide approach to probabilistic nonparametric modelling which allows a straightforward combination of measured data and local linear models in an empirical model. This is of particular importance in the identification of nonlinear dynamic systems from experimental data where usually more data are available far from equilibrium points. We illustrate the utility of such simple nonlinear predictive control example.

Simulation of Fuzzy-Logic-Based Intelligent Wheelchair Control System

Fuzzy logic control system for an intelligent wheelchair aimed for assistance by the severely handicapped persons is presented in the paper. It is based on a computer simulation of wheelchair navigation, in which fuzzy logic enables control priority to smoothly alternate between manual and automatic control of the wheelchair in the vicinity of obstacles. The main purpose of designing and simulating this control approach is to improve the safety of a wheelchair in the presence of obstacles. To analyze the success of the wheelchair control, a dynamic model of the wheelchair, together with the models of distance sensors, has been developed using Lagrange analysis.

Auto-tuning non-linear controller for industrial use

This paper presents an advanced control system named ASPECT for control of nonlinear or slowly time varying systems. The described control system was developed and implemented in programmable logic controller to meet industrial demands. The case study, which is control of gas-liquid separation unit, is intended to illustrate the operation and benefits of the controller.

Neuro-fuzzy Model-based Control

The paper deals with the Neuro-fuzzy model-based control and its application. Various types of the fuzzy logic and neural-net-based nonlinear autoregressive models with exogenous variables are reviewed with respect to the model error. Two types of model-based neuro-fuzzy control – a cancellation controller and a predictive controller are reviewed – and the robustness issues of such control are discussed. Finally, the application of the proposed design method to a laboratory scale heat exchanger is given.

On-line control performance monitor with robust properties

A control performance monitor (CPM) as a software agent is developed for assessment of the SISO control loops behaviour. A dedicated system for data pre-processing is applied in order to guaranty robust properties of the extracted process features. Using a fuzzy and rule based evaluation procedure an overall performance index is also calculated from the estimated indicators. The CPM provides early warning information when an inadmissible performance is detected. Some results of experimental verification of the CPM are reported.

An approach to robust multivariable combustion control design

Two robust controller structures are presented. These two structures affect the process in different ways, and so both could be joined together to give more robust control than each controller separately. The structure was used for combustion control which represents multivariable and nonlinear problems with time delay. Simulation results for a closed-loop control system are presented and discussed. The procedure for designing robust multivariable control was supported with computer-aided control system design tools.<<ETX>>