This chapter provides an overview of system identification and the system identi- fication procedure. An introduction to the properties of both linear and non-linear systems is made. The entire identification procedure is discussed in detail and a number of different methods are introduced for the main steps: parameter esti- mation, structure detection, model selection and model validation. Following this, Bayesian inference is introduced providing a different perspective on the mod- elling task. By identifying models within a Bayesian framework uncertainty is naturally incorporated. In the next chapter non-linear system identification and frequency domain analysis methods are applied to the application of film-type DEAs.
Nonlinear System Identification
and Frequency Domain Analysis
of Dielectric Elastomer Actuators
In the previous chapter DEAs were introduced as a new type of actuation device with huge potential for replacing traditional actuators such as motors for many applications, especially where robotic devices are interacting with humans be- cause of the inherent compliance of the constituent materials. However, in order to realise the potential of these actuators the non-linearity and time-variation in the dynamic behaviour of DEAs necessitates the need for advanced control algo- rithms before these actuators can be successfully implemented [90, 133, 139]. A crucial step in achieving this is the development of techniques for control-oriented modelling and analysis.In this chapter a novel control-focused analysis framework for DEAs is de- veloped. The framework is based on the NARX model, which is discussed in detail in Section 3.3.2. In the context of control, the NARX model provides a num- ber of advantages: it is able to produce parsimonious model descriptions with a high prediction accuracy that naturally incorporate a description of the plant non-linearities [91]. Many approaches are available for the structure detection of NARX models, a selection of which are discussed in Section 3.5. Here a simulation prediction error identification approach is taken in which term selection is driven by the models ability to predict unseen data, in-line with the objectives of this work.
A key attribute of the framework developed in this investigation, as well as throughout the thesis, is the use of non-linear frequency domain analysis tech- niques by both GFRF [20] and NOFRF [67] analysis. This allows, for the first time,
the frequency response analysis of non-linear DEA dynamics. Linear frequency domain methods are widely used in the field of control and provide a powerful tool for both implementing control architectures as well as analysis and interpre- tation of system dynamics [35]. The GFRF and NOFRF methodologies extends the use of frequency response analysis from linear to non-linear models.
The developed analysis framework is applied to the set of film-type DEA ac- tuators presented in Section 2.3. This is done with an aim to demonstrate the ap- plicability of the analysis framework as well as to investigate the insight that can be obtained from the non-linear frequency domain analysis. This analysis enables direct comparisons between the different actuators providing insight into their dy- namic similarities and differences. This is especially interesting when DEAs are fabricated to the same specifications using identical fabrication methods.
The aims of this chapter are summarised below:
1 The identification of control focussed non-linear models of DEAs that are compact and accurate.
2 The use of identified non-linear models of DEAs in model based frequency domain analysis.
3 Demonstration of the analysis framework on a set of film-type DEAs, used to gain insight into the dynamic similarities and differences across this set of actuators.
Part of the work presented in this chapter has been published previously by the author in a peer reviewed journal [54].
The remainder of this chapter is structured as follows: In Section 4.1 the joint structure detection and parameter estimation used for identification of non-linear models for DEAs is introduced. If identified non-linear models contain a DC component it is necessary to remove this term from the model prior to performing frequency domain analysis, this is discussed in Section 4.2. In Section 4.3 advanced frequency domain analysis techniques are introduced. Section 4.4 collects the modelling and analysis methods into a framework which is then applied to a set of DEA actuators. The chapter is concluded with a discussion of the results and a summary in Sections 4.5 and 4.6 respectively.
4.1
Simulation based structure detection with the SEMP
identification algorithm
The first step in the framework is the joint structure detection and parameter es- timation of non-linear models of the DEA system of the NARX class. The model structure was identified in this work using a simulation based term selection al- gorithm named the simulation error minimization with pruning (SEMP) algorithm - which has been shown to provide greater discrimination between model terms than one-step-ahead predictive algorithms [99]. Term selection is driven by the reduction of the squared error over simulated model predictions using the MSSE encouraging the identification of models that perform well at long term predic- tion [99]. The criterion for assessing terms is the simulated error reduction ratio (SERR), given by Equation (3.50) and repeated here for clarity
SERRi = MSSE(Mi)−MSSE(Mi+1) 1 N N ∑ k=1y 2 k (4.1)
where Mi is the model at the ith iteration and Mi+1 is the model to be tested at the subsequent iteration. MSSE is the Mean Squared Simulation Error, given by
MSSE= 1 N N
∑
k=1 ˆe2 k (4.2)where ˆek = yk− ˆyk, with ˆyk being the simulated system output at sample time k given by ˆyk = M
∑
m=1 ˆ θmφm(ˆxk) (4.3) where ˆxk = ˆyk−1, ..., ˆyk−ny, uk−1, ..., uk−nu (4.4) is a vector of lagged system input terms and simulated system output terms at sample time k.The structure detection algorithm proceeds as follows: some initial model structure, M0, is chosen. Typically this is composed of the empty set or a lin- ear basis of a predefined dynamic order. SERR0is initialised as
SERR0= MSSE1 (M0)
N ∑ty2k
(4.5)
Figure 4.1: The SEMP algorithm identifies models based on their prediction accuracy.Schematic of the SEMP algorithm containing two distinct parts: Forward selection and removal of redundant terms at each iteration.
by Equation (4.1) for a proposed modelMi+1. The proposed modelMi+1consists of the current model,Mi, with the addition of a basis function from the set of all remaining basis functions, where each new basis function is tested one at a time. The model Mi+1 that produces the minimum value of MSSEi+1 is then chosen and the newly selected basis function is removed from the set of remaining basis functions. After a new term has been selected a check for redundant terms is performed. Each term in the proposed model Mi+1 is removed and the MSSE is calculated. The worst basis function is selected as the one that produces the minimum of the MSSE. If the MSSE taken for the worst term is less than MSSEi
then the term is actually removed from the proposed model and returned to the set of potential model terms and another check for redundant terms is performed. The model is updated as Mi = Mi+1 with MSSEi = MSSEi+1 where MSSEi+1 is the MSSE of the newly selected model and the SERR is calculated by Equation (4.1). The algorithm can be terminated when MSSEi+1 > MSSEi indicating that the proposed model decreases the prediction accuracy. For each proposed model the parameters are necessarily re-estimated using LS and simulated over the data set in order to assess the MSSE. The algorithm is shown schematically in Figure 4.1.
Due to a finite data limit the algorithm may not terminate after the correct model structure is found and redundant terms may be selected that are over fitting to the noise. The SERR can be used to assess which terms should be included in the model by assessing the relative magnitudes of the SERR values.
If the mean level of the system output is non zero then a constant (or DC) term should be included into the structure detection as biased parameters may be estimated by LS if the DC component is neglected [14]. This is simply achieved by including a basis function into the superset of basis functions, with output unity for any excitation. The inclusion of a DC term in the final model structure affects subsequent analysis and so needs to be taken into account.