A real-time control of a liquid level in a conical tank is taken for study. Control of liquid level in a conical tank is nonlinear due to variations in the area of cross section of the level system with height. System identification of this nonlinear process is done using both the Auto Regressive External (ARX) input model and the black box model, the latter identified to be nonlinear and approximated to be a First Order Plus Dead Time (FOPDT) model. Two controllersProportional Integral (PI) controller using Skogestad (2003) tuning rule and Fuzzy Logic Controller (FLC)are developed for the control of liquid level in the conical tank. The real-time control of the above said process is implemented in MATLAB using ADAM's data acquisition module. The performance of the two controllers is compared for various performance indices.

Most chemical process systems are nonlinear in nature. Liquid level control system is an important control problem. For example, the control of liquefied petroleum gas in a conical storage tank is difficult; as the level decreases, the liquid vaporizes. The control of liquid level is a nonlinear problem. This is due to the relationship between the controlled variable (level) and the manipulated variable (flow rate), which has a square root relationship. Conical tanks find wide applications in process industries. Their shapes contribute to better dispersal of solids when mixing, providing more complete drainage, especially for viscous liquids. Control of conical tank is a challenging problem due to its constantly changing cross section.

Designing a controller for a nonlinear system is complex and difficult to implement. The primary task of a controller is to maintain a process at the desired operating conditions and to achieve optimum performance when facing various types of disturbances. Proportional-Integral-Derivative (PID) controllers are extensively used in process industries for several reasons. The typical transfer function models used to represent processes can be easily controlled with PID controller. For a process without an integral term, an integrator produces zero steady state to a step input. Only a small number of parameters is needed for tuning the controller. Simple tests such as Ziegler and Nichols (1942) and Astrom and Haggland (1984) provide effective controller tuning for processes with typical transfer functions. Chen (1989) has proposed a simple method for on-line identification and controller tuning. Lee et al. (1990) have suggested an improved technique for PID controller tuning from closed loop test. O'Dwyer (2000) has suggested an improved Proportional Integral (PI) controller tuning for the time delay process. Basilio and Matos (2002) have proposed methodologies for tuning PI and PID controllers based on plant step response. Chen and Seborg (2002) have reported a design method for PID controllers based on the direct synthesis approach and specification of the desired closed loop transfer function for disturbances.

Electrical and Electronics Engineering Journal, Nonlinear Process, Fuzzy Logic Controller, Proportional Integral Controller, Proportional-Integral-Derivative Controllers, Integral Squared Error, ISE, Conventional Control Techniques, Nonlinear Systems, Statistical Parameters, Fuzzy Logic Control Techniques, Conical Tank System.