Informatica

In this paper, at first, we define the notion of general fuzzy automaton over a field; we call this automaton vector general fuzzy automaton (VGFA). Moreover, we present the concept of max-min vector general fuzzy automaton. We show that if two max-min VGFA are similar, they constitute an isomorphism. After that, we prove that if two VGFA constitute an isomorphism with threshold α, they are equivalent with threshold α, where $\alpha \in [0,1]$. Also, some examples are given to clarify these new notions.

This paper investigates in a formal context some fundamental controllability properties “from” and “to” the origin of probabilistic discrete-time dynamic systems as well as their uniform versions and complete controllability in a class of probabilistic metric spaces or probabilistic normed spaces, in particular, in probabilistic Menger spaces. Some related approximate probabilistic controllability properties are also investigated for the case when a nominal controllable system is subject to either parametrical perturbations or unmodelled dynamics. In this context, the approximate controllability of a perturbed system is a robustness-type approximate controllability provided that the nominal system is controllable. Some illustrative examples are also given.

This paper discusses the disease-free and endemic equilibrium points of a SVEIRS propagation disease model which potentially involves a regular constant vaccination. The positivity of such a model is also discussed as well as the boundedness of the total and partial populations. The model takes also into consideration the natural population growing and the mortality associate to the disease as well as the lost of immunity of newborns. It is assumed that there are two finite delays affecting to the susceptible, recovered, exposed and infected population dynamics.

Robust stability results for nominally linear hybrid systems are obtained from total stability theorems for purely continuous-time and discrete-time systems. The class of hybrid systems dealt with consists of, in general, coupled continuous-time and digital systems subject to state perturbations whose nominal (i.e., unperturbed) parts are linear and time-varying, in general. The obtained sufficient conditions on robust stability are dependent on the values of the parameters defining the over-bounding functions of the uncertainties and the weakness of the coupling between the analog and digital sub-states provided that the corresponding uncoupled nominal subsystems are both exponentially stable.

This paper develops a representation of multi‐model based controllers by using artificial intelligence typical structures. These structures will be neural networks, genetic algorithms and fuzzy logic. The interpretation of multimodel controllers in an artificial intelligence frame will allow the application of each specific technique to the design of improved multimodel based controllers. The obtained artificial intelligence based multimodel controllers are compared with classical single model based ones. It is shown through simulation examples that a transient response improvement can be achieved by using multiestimation based techniques. Furthermore, a method for synthesizing multimodel based neural network controllers from already designed single model based ones is presented. The proposed methodology allows to extend the existing single model based neural controllers to multimodel based ones, extending the applicability of this kind of techniques to a more general type of controllers. Also, some applications of genetic algorithms and fuzzy logic to multimodel controller design are proposed. Thus, the mutation operation from genetic algorithms inspires a robustness test which consists of a random modification of the estimates which is used to select the estimates leading to the better identification performance towards parameterizing online the adaptive controller. Such a test is useful for plants operating in a noisy environment. The proposed robustness test improves the selection of the plant model used to parameterize the adaptive controller in comparison to classical multimodel schemes where the controller parameterization choice is basically taken based on the identification accuracy of each model. Moreover, the fuzzy logic approach suggests new ideas to the design of multiestimation structures which can be applied to a broad variety of adaptive controllers such as robotic manipulator controller design.

This paper deals with the absolute stability of single‐input single‐output time‐delay systems with, in general, a finite number of non commensurate constant internal point delays for any nonlinearity satisfying a time positivity inequality related to the first and third quadrants. The results are obtained based on Lyapunov's stability analysis via appropriate Lyapunov's functions and the related stability study is performed to obtain both delay independent and delay dependent results.

An adaptive control scheme for mechanical manipulators is proposed. The control loop essentially consists of a network for learning the robot's inverse dynamics and on-line generating the control signal. Some simulation results are provided to evaluate the design. A supervisor is used to improve the performances of the system during the adaptation transients. The supervisor exerts two supervisory actions. The first one consists basically of updating the free-design adaptive controller parameters so that the value of a quadratic loss function is maintained sufficiently small. Such a function involves past tracking errors and their predictions both on appropriate time horizons of low performances during the adaptation transients. The supervisor exerts two supervisory actions. The second supervisory action consists basically of a on-line adjustment of the sampling period within an interval centered in a nominal value of the sampling period. The sampling period is selected so that the transient of the tracking error is improved according to the simple intuitive rule of using a sampling rate faster as the tracking error changes faster.

The objective of expert systems is the use of Artificial Intelligence tools so as to solve problems within specific prefixed applications. Even when such systems are widely applied in diverse applications, as manufacturing or control systems, until now, there is an important gap in the development of a theory being applicable to a description of the involved problems in a unified way. This paper is an attempt in supplying a simple formal description of expert systems together with an application to a robot manipulator case.

This paper deals with the study of the optimal control problem for the objective F(x,u,v)=∫^T0f(x(t),x(t−h),ζ(t),u(t),v(t),t)dt, with x∈X,u∈U,v∈V;X,U and V being vector spaces, and ζ(t)=∫^h0R(t,τ)x(t−τ)dτ subject to the differential equation $\frac{d}{dt}x(t)=m(x(t),x(t-h),\zeta(t),u(t),v(t),t)(0\leq t\leq T)$, and the constraints g1(u(t),t)∈S1,g2(v(t),t)∈S2;n1(x(t),t)∈V1,n2(x(t−h),t)∈V2;n3(ζ(t),t)∈V3(0≤t≤T), where x(t)∈Rⁿ;ζ(t)∈Rⁿ;u(t)∈R^k;f,m,gi(i=1,2),ni(1≤i≤3) and the entries to r(t,τ):R+×R+→L(X,X) are continuously differentiable functions. It is assumed that boundary conditions x(0)=x(T)=0 are imposed. Si(i=1;2) and Vi(1≤i≤3) are convex cones. The existence of a time-optimal control in analytic linear systems is also investigated via an extension of the bang-bang principle.

This note presents an indirect adaptive control scheme applicable to nominally controllable non-necessarily inversely stable first-order continuous linear time-invariant systems with unmodelled dynamics. The control objective is to achieve a bounded tracking-error between the system output and a reference signal. A least-squares algorithm with normalization is used to estimate the plant parameters by using two additional design tools, namely: 1) a modification of the parameter estimates and 2) a relative adaptation dead-zone. The modification is based on the properties of the inverse of the least-squares covariance matrix and it uses an hysteresis switching function. In this way, the non-singularity of the controllability matrix of the estimated model of the plant is ensured. The relative dead-zone is used to turn off the adaptation process when an absolute augmented error is smaller than the value of an available overbounding function of the unmodelled dynamics contribution plus, eventually, bounded noise.