This paper describes an intelligent adaptive PID controller design procedure. The controller consists of a discrete time PID and an auto-tuning neural network unit. First system identification with a nonlinear autoregressive model (NARX) was performed. This model was then used to train the neural PID tuner. A special MATLAB toolbox “SmatPID Toolbox” was developed to automate the process of controller synthesis. The resulting controller was tested in a laboratory coal-gas furnace control system to track specified air flow rates.

Currently used recycling technologies have limitations on the composition of recyclable waste, which makes them specialized. Thus, the preliminary sorting of municipal solid waste is a necessary step, increasing the efficiency of using municipal solid waste as a resource. To sort municipal solid waste we developed a method for detecting and classifying waste on a conveyor line using neural network image processing. Images from a camera are fed to a neural network input, which determines the position and type of detected objects. To train the neural network a database of more than 13,000 municipal solid waste images was created. Mean-Average Precision for the neural network model was 64%.

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