Deep neural fuzzy based fractional order PID controller for level control applications in quadruple tank system

This research work focus on level control in quadruple tank systems based on proposed Deep Neural Fuzzy based Fractional Order Proportional Integral Derivative (DN-FFOPID) controller system. This is used for controlling the liquid level in these non- linear cylindrical systems. These model helps in identifying the dynamics of the tank system which gives the control signal feed forwarded from the reference liquid level. But, it fails to minimize the error and the system is also subjected to external disturbances. Hence, to minimize this drawback a novel controller must be introduced in it. The proposed Deep Neural model is a six layered network which are optimized with the back-propagation algorithm. It effectively trains the system thus reducing the steady state error, offset model errors and unmeasured disturbances. This neural intelligent system maintains the liquid level which fulfils the required design criteria like time constant, no overshoot, less rise time and less settling time, which can be applied to various fields. MATLAB/simulink at FOMCON toolbox is used to perform the simulation. Real time liquid control experimental results and simulation results are demonstrated which proves the effectiveness and feasibility of the proposed methods for the quadruple tank system which finds applications in effluent treatment, petrochemical, pharmaceutical and aerospace fields.