A simulation and animation tool for education in multivariable control is presented. The purpose of the tool is to support studies of various aspects of multivariable dynamical systems and design of multivariable feedback control systems. Different ways to use this kind of tool in control education are also presented and discussed.
This paper discusses the aspects of controllability in the iteration domain for systems that are controlled using iterative learning control (ILC). The focus is on controllability for a proposed state space model in the iteration domain and it relates to an assumption often used to prove convergence of ILC algorithms. It is shown that instead of investigating controllability it is more suitable to use the concept of target path controllability (TPC), where it is investigated if a system can follow a trajectory instead of the ability to control the system to an arbitrary point in the state space. Finally, a simulation study is performed to show how the ILC algorithm can be designed using the LQ-method, if the state space model in the iteration domain is output controllable. The LQ-method is compared to the standard norm-optimal ILC algorithm, where it is shown that the control error can be reduced significantly using the LQ-method compared to the norm-optimal approach.
Insulin and other hormones control target cells through a network of signal-mediating molecules. Such networks are extremely complex due to multiple feedback loops in combination with redundancy, shared signal mediators, and cross-talk between signal pathways. We present a novel framework that integrates experimental work and mathematical modeling to quantitatively characterize the role and relation between coexisting submechanisms in complex signaling networks. The approach is independent of knowing or uniquely estimating model parameters because it only relies on (i) rejections and (ii) core predictions (uniquely identified properties in unidentifiable models). The power of our approach is demonstrated through numerous iterations between experiments, model-based data analyses, and theoretical predictions to characterize the relative role of co-existing feedbacks governing insulin signaling. We examined phosphorylation of the insulin receptor and insulin receptor substrate-1 and endocytosis of the receptor in response to various different experimental perturbations in primary human adipocytes. The analysis revealed that receptor endocytosis is necessary for two identified feedback mechanisms involving mass and information transfer, respectively. Experimental findings indicate that interfering with the feedback may substantially increase overall signaling strength, suggesting novel therapeutic targets for insulin resistance and type 2 diabetes. Because the central observations are present in other signaling networks, our results may indicate a general mechanism in hormonal control.
It is pointed out that, when a current in a power system is measured using a transformer, the measurement can be distorted by transformer saturation. The authors examine two methods of estimating the primary current from the secondary in the presence of saturation. The first is based on an extended Kalman filter, while the second is based on an off-line Gauss-Newton method
In the simulation of hybrid systems, discontinuities can appear at mode changes. An algorithm is presented that gives initial values for the continuous state variables in a new mode. The algorithm is based on a switched bond graph representation of the system, and it handles discontinuities introduced by a changed number of state variables at a mode change. The algorithm is obtained by integrating the bond graph relations over the mode change and assuming that the physical variables are bounded. This gives a relation between the variables before and after the mode change. It is proved here that the equations for the new initial conditions are solvable. The algorithm is related to a singular perturbation theory by replacing the discontinuity by a fast continuous change. The action is considered of a single switch and the corresponding continuous change, tuned by a single parameter. By letting this parameter tend to zero, the same initial state values are achieved as those derived by the presented algorithm. The algorithm is also related to physical principles such as charge conservation.
A modeling framework for the class of piecewise linear switched systems is presented. Methods for abstraction using conservative discrete approximations are introduced and model checking is used for verifying specifications. A fairly complex example is treated, the main result being that abstraction is a promising tool for fully automated verification.
The analysis and design of control systems has been greatly influenced by the mathematical tools being used. Maxwell introduced linear differential equations in the 1860’s. Nyquist, Bode and others started the systematic use of tranfer functions, utilizing complex analysis in the 1930’s. Kalman brought forward state space analysis around 1960. For nonlinear systems, differential geometric concepts have been of great value recently. We will argue here that algebraic methods can be very useful for both linear and nonlinear systems. To give some motivation we will begin by looking at a few examples.
When estimating unknown parameters, it is important that the model is identifiable so that the parameters can be estimated uniquely. For nonlinear differential-algebraic equation models with polynomial equations, a differential algebra approach to examine identifiability is available. This approach can be slow, so the present paper describes how this method can be modularized for object-oriented models. A characteristic set of equations is computed for components in model libraries, and stored together with the components. When an object-oriented model is built using such models, identifiability can be examined using the stored equations.
It is a typical situation in modern modeling that a total model is built up from simpler submodels, or modules, for example residing in a model library. The total model could be quite complex, while the modules are well understood and analysed. A procedure to decide global parameter identifiability for such a collection of model equations of differential-algebraic nature is suggested. It is shown how to make use of the natural modularization of the model structure. Basically, global identifiability is obtained if and only if each module is identifiable, and the connecting signals can be retrieved from the external signals, without knowledge of the values of the parameters.
This report describes how parameter estimation can be performed in linear DAE systems. Both time domain and frequency domain identification are examined. The results are exemplified on a small system. A potential application for the algorithms is to make parameter estimation in models generated by a modeling language like Modelica.
Modern modeling tools often give descriptor or DAE models, i.e., models consisting of a mixture of diﬀerential and algebraic relationships. The introduction of stochastic signals into such models in connection with ﬁltering problems raises several questions of well-posedness. The main problem is that the system equations may contain hidden relationships aﬀecting variables deﬁned as white noise. The result might be that certain physical variables get inﬁnite variance or contain formal diﬀerentiations of white noise. The paper gives conditions for well-posedness in terms of certain subspaces deﬁned by the system matrices.
The current demand for more complex models has initiated a shift away from state-space models towards models described by differential-algebraic equations (DAEs). These models arise as the natural product of object-oriented modeling languages, such as Modelica. However, the mathematics of DAEs is somewhat more involved than the standard state-space theory. The aim of this work is to present a well-posed description of a linear stochastic differential-algebraic equation and more importantly explain how well-posed estimation problems can be formed. We will consider both the system identification problem and the state estimation problem. Besides providing the necessary theory we will also explain how the procedures can be implemented by means of efficient numerical methods.
Starting from the behavioral point of view a system is defined by its set of behaviors. In discrete time this is a relation over D N and hence a very infinite object. A model is a relation over D N for some finite N that can be extended to a behavior. Furthermore properties of a system is defined in terms of its behavior. Starting from a constructive point of view we need to be able to represent and manipulate systems. A natural choice is to use some a constructive model, i.e. one that can be finitely represented and manipulated. We will consider four such classes of models: polynomial and linear relations over finite and infinite fields. There are a number of restrictions on the geometric (or behavioral) operations that are possible for each of these classes and still remain within the class. If we want to interpret our models as systems and analyze system properties, then several properties become impossible to compute. Some examples: The set of reachable states for a polynomial model over an infinite field is in general impossible to compute. It may converge to be fractal. The set of reacable states i^{k} steps or less in a linear model cannot be represented as a linear set in general.
Extentions of the RGA (relative gain array) technique to nonlinear systems are considered. The steady-state properties are given by an array of nonlinear functions. It is shown that the corresponding dynamic description can be calculated using a reduction algorithm from differential algebra.
Extentions of the RGA (relative gain array) technique to nonlinear systems are considered. The steady-state properties are given by an array of nonlinear functions. The high frequency properties are characterized by forming the conventional RGA of the decoupling matrix.
When several control signals share the same physical actuators, actuator saturation has the effect of giving coupling between control loops which are otherwise decoupled. This effect occurs e.g. in aircraft control where the elevator and aileron functions often use the same physical control surfaces. The effect is particularly important when one of the loops is open loop unstable. The paper consider the effects on the stabilizable region of cross coupling with another control loop. To give further insight simple optimal control problems are considered.
The settling time for the step response of a nonlinear system can be computed using the Poincaré-Dulac normal form. It is then possible to use a simple approximate formula, whose error can be estimated. The data needed can all be calculated from the leading coefficients of series expansions of the Poincaré-Dulac transformation and the equilibrium curve.
It is described how set membership identification and model rejection for polynomial models can be described using polynomial inequalities and inequations. Using difference algebra methods these problems can be reduced to a form based on so called autoreduced sets. It is shown that these descriptions generalize state space descriptions. It is also discussed how special forms of autoreduced sets can make calculations based on interval methods easier to implement.
This paper discusses how the concepts of differential algebra can be used in the modelling of physical systems. In particular it is shown that the concepts of ranking and characteristic set can be used to give a structure to the set of physical equations. The characteristic set makes it easy to find the number of inputs and the order of the system. The question of observability can also be investigated using the characteristic set. In particular one can see that the order of an input output differential equation will be the same as the system order if and only if all variables are observable from the output.
The authors present an algorithm for computation of input-output descriptions. Characteristic sets can be considered as generalization of state space descriptions. Input-output descriptions can also be regarded as characteristic sets under a different ranking. This forms the basis of the differential algebraic algorithm for computation of input-output relations. For polynomial state-space descriptions the calculations are easier than the general computations of characteristic sets.