This paper deals with the application of adaptive model objects for integration of modeling products and robot systems. Product modeling is an advanced method for integration of product related computer descriptions. Product model describes engineering related information about parts and assemblies in a mechanical system. Robots use this information for flexible automated manufacturing of the modeled product. Consequence of definition or change of model objects may be unacceptable change in production. To prevent similar problems by improper design at production, two-way communication is needed between product design and production planning. The authors proposed highly integrated model objects for this purpose. Model objects evaluate and react inside and outside changes by analysis of the related behaviors. Adaptivity features are created and then applied for behavior driven generation of modification of model entities. This paper analyzes process-oriented product modeling of robot application related mechanical systems. The proposed objects are composed by elementary, structural, relationship, behavior, knowledge and adaptivity features. This paper also discusses integration of multi layered robot process model with shape models, procedure of modification handling by behavior features in a product-robot model system, general architecture of the proposed model objects and behavior feature driven activities of active model objects on four levels.

The present state of development of a new branch of soft computing (SC) developed for the adaptive control of a special class of nonlinear coupled MIMO systems is reported. Its uniform structures are obtained from certain Lie groups, unlike that of the traditional SC approaches. The advantages include: a priori known, very much reduced in size, and increased in lucidity; and the parameter tuning or learning is replaced by a simple and short explicit algebraic procedure. The only disadvantage is a limited circle of applicability. Convergence considerations are also discussed for MIMO and SISO systems. Simulation examples are presented for the control of an inverted pendulum by using the generalized Lorentzian matrices. It is concluded that the method is promising and probably imposes acceptable convergence requirements in many practical cases.

An efficient approach for the adaptive control of approximately and partially known mechanical systems, like traditional soft computing, uses "uniform structures" for modeling, but these structures are obtained from certain Lie groups as the symplectic group or the generalized Lorentz group. This approach considerably reduces the number of free parameters in the model. Until now its efficiency was investigated for mechanical uncertainties and external dynamic interactions. In this paper the behavior of the electric drives are also included in the investigations. A 1-DOF mechanical system, a pendulum driven by a DC motor in a computed torque control, is investigated via simulation. The necessary torque is calculated from a formal primitive mechanical model and an adaptivity rule. For adaptivity real number scaling and the generalized Lorentz group elements are used. It is concluded that a single adaptive loop can compensate for the mechanical and the electrical uncertainties simultaneously.

Traditional fuzzy controllers or artificial neural networks are constrained by standardized formal procedures and structural restrictions. While in general it is guaranteed that these approaches or their integration can solve a quite wide class of problems, their "orthodox" application may result in too complicated control not exploiting the peculiarities of the given task under consideration. The aim of the paper is to demonstrate that unconventional integration of simple elements such as classic PID/ST, regression analysis, saturated sigmoid transition functions, fuzzy sets and uniform structures obtained from the Lagrangian classical mechanics instead of the connection structure of a feedforward artificial neural networks can result in a simple and efficient adaptive control for robots involved in unknown environmental dynamic interaction. Due to their simplicity and reduced number of parameters real time tuning can be carried out in these structures. Most of the parameters are independent of the particular problem to be solved and neither "scaling problems" nor "network paralysis" occur during the learning phase. It is concluded that the different components of the control can successfully co-operate in finding the "proper" system model even in the case of very rough initial model estimation and external interaction.

On the basis of Lagrangian mechanics uniform structures made of simple and standardized procedures and closed form analytical formulas were previously used to develop an adaptive control for SCARA robots in dynamic interaction with an unmodeled environment. This standardized form contains well defined free parameters by tuning of which the complex effect of the behavior of the controlled system as well as that of the external interactions can be 'imperfectly' modeled and learned. These structures, free parameters and procedures play exactly the same role as that of the traditional artificial neural networks (ANNs) or fuzzy controllers: the structures and the procedures are fit for a wide class of problems more or less similar to each other, while parameter tuning corresponds to learning the concrete properties of a particular element of this wider set. While in the case of the standard soft computing methods there is no reliable a priori information on the number of the concrete elements in the uniform structures, the proposed method has definite indication for this by using the Lie parameters of the orthogonal group. In the initial stage of learning the proposed methods also use a standardized ancillary procedure, a very simple form of regression analysis based controller compensating the remnant errors whenever appropriate. In this paper certain details of the uniform control and parameter-tuning are discussed on the basis of simulation results. It can be concluded that the approach is promising therefore experimental investigations are under preparation.

In this paper a theoretical approach to performance improvement of fuzzy logic robot controllers is presented. The approach is based on two new sets of T-operations introduced by the authors. The T-norms and T-conorms are defined as minimum and maximum entropy operations. Simulation has been carried out so as to compare the effects of the new and the conventional T-operators in case of a 4 DOF rigid-link flexible-joint SCARA type robot. It is concluded that by fixing the other parameters of the controller certain sets of T-operations can improve the performance of the controller.