The CAPTAIN Toolbox for System Identification, Time Series Analysis, Forecasting and Control: Guide to TVPMOD: Time Variable Parameter Models

The CAPTAIN Toolbox is a collection of MATLAB functions for non-stationary time series analysis, forecasting and control. The toolbox is useful for system identification, signal extraction, interpolation, forecasting, data-based mechanistic modelling and control of a wide range of linear and non-linear stochastic systems. The toolbox consists of three modules, organised into three folders as follows: TVPMOD: Time Variable Parameter (TVP) MODels. For the identification of unobserved components models, with a particular focus on state-dependent and time-variable parameter models (includes the popular dynamic harmonic regression model). RIVSID: Refined Instrumental Variable (RIV) System Identification algorithms. For optimal RIV estimation of multiple-input, continuous- and discrete-time Transfer Function models. TDCONT: True Digital CONTrol (TDC). For multivariable, non-minimal state space control, including pole assignment and optimal design, and with backward shift and delta-operator options. The present document is a guide to the TVPMOD module. The Toolbox files and Getting Started Guide are also available for download

The CAPTAIN Toolbox for System Identification, Time Series Analysis, Forecasting and Control:Getting Started Guide

The CAPTAIN Toolbox is a collection of MATLAB functions for non-stationary time series analysis, forecasting and control. The toolbox is useful for system identification, signal extraction, interpolation, forecasting, data-based mechanistic modelling and control of a wide range of linear and non-linear stochastic systems. The toolbox consists of three modules, organised into three folders as follows: TVPMOD: Time Variable Parameter (TVP) MODels. For the identification of unobserved components models, with a particular focus on state-dependent and time-variable parameter models (includes the popular dynamic harmonic regression model). RIVSID: Refined Instrumental Variable (RIV) System Identification algorithms. For optimal RIV estimation of multiple-input, continuous- and discrete-time Transfer Function models. TDCONT: True Digital CONTrol (TDC). For multivariable, non-minimal state space control, including pole assignment and optimal design, and with backward shift and delta-operator options. The Toolbox files and Getting Started Guide are free to download. Additional handbooks are also available from the authors

Petri nets for systems and synthetic biology

We give a description of a Petri net-based framework for modelling and analysing biochemical pathways, which uni¯es the qualita- tive, stochastic and continuous paradigms. Each perspective adds its con- tribution to the understanding of the system, thus the three approaches do not compete, but complement each other. We illustrate our approach by applying it to an extended model of the three stage cascade, which forms the core of the ERK signal transduction pathway. Consequently our focus is on transient behaviour analysis. We demonstrate how quali- tative descriptions are abstractions over stochastic or continuous descrip- tions, and show that the stochastic and continuous models approximate each other. Although our framework is based on Petri nets, it can be applied more widely to other formalisms which are used to model and analyse biochemical networks

New developments in the CAPTAIN Toolbox for Matlab with case study examples

The CAPTAIN Toolbox is a collection of Matlab algorithmic routines for time series analysis, forecasting and control. It is intended for system identification, signal extraction, interpolation, forecasting and control of a wide range of linear and non-linear stochastic systems across science, engineering and the social sciences. This article briefly reviews the main features of the Toolbox, outlines some recent developments and presents a number of examples that demonstrate the performance of these new routines. The examples range from consideration of global climate data, through to electro-mechanical systems and broiler chicken growth rates. The new version of the Toolbox consists of the following three modules that can be installed independently or together: off-line, time-varying parameter estimation routines for Unobserved Component (UC) modelling and forecasting; Refined Instrumental Variable (RIV) algorithms for the identification and estimation of both discrete and hybrid continuous-time transfer function models; and various routines for Non-Minimal State Space (NMSS) feedback control system design. This new segmented approach is designed to provide new users with a gentler introduction to Toolbox functionality; one that focuses on their preferred application area. It will also facilitate more straightforward incorporation of novel algorithms in the future

Model checking embedded system designs

We survey the basic principles behind the application of model checking to controller verification and synthesis. A promising development is the area of guided model checking, in which the state space search strategy of the model checking algorithm can be influenced to visit more interesting sets of states first. In particular, we discuss how model checking can be combined with heuristic cost functions to guide search strategies. Finally, we list a number of current research developments, especially in the area of reachability analysis for optimal control and related issues

Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation

Multivariable proportional-integral-plus (PIP) control methods are applied to the nonlinear ALSTOM Benchmark Challenge II. The approach utilises a data-based combined model reduction and linearisation step, which plays an essential role in satisfying the design specifications. The discrete-time transfer function models obtained in this manner are represented in a non-minimum state space form suitable for PIP control system design. Here, full state variable feedback control can be implemented directly from the measured input and output signals of the controlled process, without resorting to the design and implementation of a deterministic state reconstructor or a stochastic Kalman filter. Furthermore, the non-minimal formulation provides more design freedom than the equivalent minimal case, a characteristic that proves particularly useful in tuning the algorithm to meet the Benchmark specifications. The latter requirements are comfortably met for all three operating conditions by using a straightforward to implement, fixed gain, linear PIP algorithm