State estimation using fuzzy logic: a case study for a nonlinear plant

Process engineering, process design and simulation, process supervision, control and estimation, process fault detection and diagnosis rely on the effective processing of unpredictable and imprecise information. In such situations, the fuzzy logic, which can achieve the sophisticated level of information processing the brain is capable of, can excel. They are generally viewed as process modeling formalism and given the appropriate network topology; they are capable of characterizing nonlinear functional relationships. The structure of the resulting fuzzy based process model may be considered generic, in the sense that little prior process knowledge alone is required. The knowledge about the plant dynamics and mapping characteristics are implicitly stored within the network. In this paper, an estimator using fuzzy logic, to estimate the process variable level in a nonlinear process control plant is presented. The estimator help in extending the range of operation of the conventional control systems with respect to sensor validation at no extra (hardware) costs. The main idea, therefore, is to monitor and approximate any off-nominal behavior in the dynamical system by using on-line fuzzy approximation structures. Experimental results obtained from the real-time plant shows that, the designed estimator successfully take cans of the feedback sensor failure. The performance of the estimator is evaluated using the mean square error criterion. The proposed method finds application mainly in the area of sensor validation, control engineering and other related fields to estimate the true variations of the signal during the failure period of a sensor.