Design and Development of Efficient Control Strategies for Liquid Level Tank Systems

Since last two decades, several researchers have investigated to control the level of liquid in tank in the process industries. The process industries require liquids to be pumped as well as stored in tanks and then pumped to another tank. Most of the times the liquids will be processed in the tanks, but always the level of liquid in the tanks must be controlled and the flow between the tanks must be regulated. It is one of the most challenging benchmark control problems due to its coupling, nonlinear and non-minimum phase characteristics. For developing and designing the control algorithms, the mathematical model of the process must be developed. A mathematical model helps to explain a system, to study the effects of different components, and to make predictions about their behaviours. The design and analysis of traditional control systems are based on the mathematical models. Proportional-integral-derivative (PID) controller is the simple, reliable and robust control technique used in industrial feedback control loops. The PID controllers show poor control performances for an integrating process and a large time delay process. Fuzzy controller is one of the intelligent controller used to make the system fast and stable, but cannot eliminate the steady state error. However, the conventional PID controller is simple, accurate and reliable which eliminates the steady state error. Therefore, the fuzzy controller is combined with the PID controller and the combined controller has to take the advantages of both PID and fuzzy controller. Then, the combined controller is applied to the tank level control system to control the level of liquid in tank. The fuzzy-PID controller has less overshoot, good robustness and low settling time. The controller efficiently tracks the set point. The fuzzy-PID controller gives better performance in terms of error indices such as IAE, ISE, ITAE and ITSE. A fuzzy modified model reference adaptive control(FMMRAC)has proposed to find the PID controller parameters Kp,Ki and Kd present in modified model reference adaptive control (MMRAC) scheme using fuzzy logic controller. This method gives improved transient performance compared to model reference adaptive control (MRAC) and MMRAC methods. Also, we proposed a method where differential evolution (DE) application is used to fine tune the parameters of PID controller Kp,Ki and Kd present in MMRAC scheme called as DE based MMRAC (DEMMRAC). The simulation results show that the proposed DEMMRAC gives better performance than that of MRAC, MMRAC and FMMRAC. The DEMMRAC controller has performed very well even with different reference models as well as different command signals which indicate the robustness of the proposed design.One of the most effective ways to solve the control problems of tank system is the use of predictive control techniques. The Model Predictive Control (MPC) is an advanced method of process control that has been in use in the process industries in chemical plants and oil refineries. The model quality plays a vital role in MPC, but in practical, there always exists model uncertainties which can degrade the system performance. Such problems can be resolved by developing robust MPC methods. The MPC algorithm has been applied to the four tank system (FTS) and different performance indices as well as error indices have been calculated. The responses of MPC controller has been compared with other control algorithms through simulation. For the problem of set point regulation of the liquid level in coupled tank systems, we present a continuous sliding mode control (SMC) with a “conditional integrator”, which only provides integral action inside the boundary layer. For a special choice of the controller parameters, our design can be viewed as a PID controller with anti-windup, and it achieves robust regulation and recovers the transient response performance in ideal SMC, but without control chattering. Both full-state feedback as well as output-feedback designs are presented, with the latter being crucial in the case in which system parameters are unknown. In this case, our output-feedback design, which uses a high-gain observer (HGO) to robustly output derivatives recovers the performance of a state-feedback design where plant parameters are assumed known. We consider both the cases of interacting as well as non-interacting tanks, and present analytical results for stability and transient performance. Experimental results demonstrate good tracking performance in spite of unmodeled dynamics and disturbances