Using the interpolation/extrapolation skills of the core function of an iterative adaptive controller, a structurally simple single essential layer neural network-based topological structure is suggested with fast and explicit single-step teaching and data-retrieving abilities. Its operation does not assume massive parallelism, therefore it easily can be simulated by simple sequential program codes not needing sophisticated data synchronization mechanisms. It seems to be advantageous in approximate model-based common, robust, or adaptive controllers that can compensate for the effects of minor modeling imprecisions. In this structure a neuron can be in either a firing or a passive (i.e., producing zero output) state. In firing state its activation function realizes an abstract rotation that maps the desired kinematic data into the space of the necessary control forces. The activation function allows the use of a simple and fast incremental model modification for slowly varying dynamic models. Its operation is exemplified by numerical simulations for a van der Pol oscillator in free motion, and within a Computed Torque type control. To reveal the possibility for efficient model correction, a robust Variable Structure/Sliding Mode Controller is applied, too. The novel structure can be obtained by approximate experimental observations as e.g., the fuzzy models.

In this paper a system of cooperating robots manipulating a rigid object is considered. The robots are constructed of redundant open kinematics chains containing prismatic and rotary joints, and may have different, general structures. For solving the inverse kinematic task of such structures generally only differential approaches are applicable that may cope with problems in the unavoidable kinematic singularities. In the present approach the “master” robot can rigidly grip the workpiece and moves it accordingly. The cooperating ones (the “slaves”) can grip the same workpiece with grippers provided with“elastic adapters” that can be deformed if they execute their motion nearby their kinematic singularities where the tracking precision of the construction is limited. A general differential solution is provided for the inverse kinematic task that is based on the Moore-Penrose pseudoinverse with a tracking errordependent damping factor. Simulation results are presented for two cooperating 8 Degree of Freedom robot arms to exemplify the simplicity of the suggested method.

In this paper a fuzzy inference system in fuzzy toolbox package in MATLAB using two “fuzzified” control parameters for the fixed point transformation-based adaptive controller is applied for the strongly dynamic non-linear system, the “van der Pol Oscillator”. The paper presents a comparison of the operation of the classical controller and a novel one combined with fuzzy inference system.