Informatica

Due to high nonlinearities and time-varying dynamics of today's control systems fuzzy learning controllers find appliance in practice. The present paper proposes a method for the synthesis of the learning fuzzy controllers where an expert knowledge about a process is applied to form a learning mechanism that is used to acquire information for the knowledge base of the main fuzzy controller. According to the proposed method an expert knowledge is used to describe how the controller should learn to control rather than to control the process. The results of experiments on heating system and level/pressure system prove the practical relevance of the design strategy of a learning fuzzy controller.

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.

This paper presents a direct adaptive, control algorithm, based on a σ-modification rule, which is robust respect to additive and multiplicative plant unmodelled dynamics for plants involving both internal (i.e., in the state) and external (i.e., in the output or input) known point delays. Several adaptive controller structures are given and analyzed for the case of plants with unknown parameters while being assumed that the nominal plant is of known order and relative order. The parametrized parts of two of the controller structures involve delays while those of the two remaining controllers are delay-free. However, auxiliary compensating signals which weight the plant input and output integrals are incorporated in all the controller structures for stabilization purposes. It is proved that, if the unmodelled dynamics is sufficiently small at low frequencies, then the adaptive algorithm guarantees boundedness of all the signals in the closed-loop system.

We consider a class of identification algorithms for distributed parameter systems. Utilizing stochastic optimization techniques, sequences of estimators are constructed by minimizing appropriate functionals. The main effort is to develop weak and strong invariance principles for the underlying algorithms. By means of weak convergence methods, a functional central limit theorem is established. Using the Skorohod imbedding, a strong invariance principle is obtained. These invariance principles provide very precise rates of convergence results for parameter estimates, yielding important information for experimental design.