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The aim of this paper is to present basic hardware components of a Flexible Assembly System (FAS) and investigate possible faults, classify the errors and implement real diagnosis. The mentioned system offers professional training according to the industrial reality, simulating a real assembly process and including different technologies from Industry 4.0. This equipment consists of an automated flexible assembly cell with five different stages: assembly, handling, quality inspection, transfer and warehouse. The pneumatic system repeats the identical programmed sequence during it operation. The data was collected when the pneumatic system worked perfectly and had some generated faults including empty magazine, zero vacuum, inappropriate material, no pressure, closed manual pressure valve, missing drilling stroke, poorly located material, not vacuuming the material and low air pressure. The faults are simulated according to developed algorithms in this investigation. The case study with fuzzy logic is used to develop a new sequence for one pneumatic module diagnosis. The analysis of the simulated faults is discussed and the obtained results can be applied in the similar pneumatic systems in industry.

At the present time, various types of autonomous mobile robots have found their place in many applications related with surveillance, transportation, inspection, and other problems. The trajectory tracking problem stands along with this growth. In real world, uncertainty and fuzziness of information cause additional difficulties. Hence, deterministic models insufficient usually are, one possible way to overcome the problem is to resort to the use of type-2 fuzzy systems. In this paper an adaptive tuned interval type-2 Takagi-Sugeno fuzzy-neural PID controller for compensation of friction and disturbance effects during the trajectory tracking control of a non-holonomic mobile robot is proposed. The parameters of the adaptive controller are updated according the introduced on-line learning algorithm. Preformed simulation experiments demonstrate the effect of the developed control algorithm on the trajectory tracking performance of a mobile robot.

This paper describes the development of a novel state-space model predictive controller. The proposed modelling structure used to capture and predict the nonlinear process dynamics lies on the concept for a neo-fuzzy neuron, deployed in state-space. The introduced approach represents a set of simple fuzzy inferences along the temporal behaviour of each input node, whose dynamics is expressed as a singleton function. The learning algorithm for the proposed modelling structure is realized as a gradient descent procedure. On the basis of the obtained neo-fuzzy state-space model, a fuzzy predictor for the purpose of predictive control is developed. The achieved predictions are used to optimize the future system response by implementing a quadratic programming optimization procedure along the stated controller horizons. The potentials of the proposed approach are studied by simulation experiments to modelling and control of a nonlinear drying plant.

Navigation and collision avoidance are major areas of research in mobile robotics that involve varying degrees of uncertainty. In this paper, a new approach is proposed for navigation and control of a wheeled mobile robot in a partially known environment. A collision-free path is calculated using an efficient neural motion planner. An additional fuzzy logic based navigation strategy is developed to adjust the moving information in the cases when the mobile robot encounters unexpected obstacles. The output of the navigation level is transformed into a time indexed data sequence which is fed into a tracking controller that takes into account the complete dynamics of the mobile base. The locomotion control structure is based on the integration of a kinematic controller and an adaptive fuzzy-net torque controller.

A complete motion planning and control procedure for mobile robot is presented. A collision-free path is calculated using a neural-net motion planner. The output of the planner is then transformed into a time indexed data sequence which is fed into a tracking controller that takes into account the complete dynamics of the mobile base. A locomotion control structure based on the integration of a kinematic controller and an adaptive fuzzy-net torque controller is proposed. An evolutionary feedback-error-learning method for automatic elicitation of knowledge in the form of fuzzy if-then rules is developed. The results of the simulations show the effectiveness of the proposed approach.