Control of a real chaotic cellular neural network

Summary form only given. We study the possibilities of suppressing chaotic behaviour of the three-cell cellular neural network. We present the laboratory environment and experimental results of stabilization of one of the existing unstable periodic orbits, by means of applying small periodic perturbations to one of the circuit parameters. The results obtained are promising. The data acquisition and identification part work correctly. Based on time series obtained from the real process, we have found several unstable periodic orbits and their parameters necessary for the control. We have performed a number of control experiments. We have measured the performance of the system and noticed that the trajectory remains longer in the neighbourhood of the stabilized periodic orbit in the case when the control is active. We conclude that the control method is sensitive to noise and accuracy of the computed parameters of the stabilized periodic orbit. We believe that with some modifications a successful control is possible.<>