In theory and experiments, optimal and adaptive control strategies are employed to suppress chaotic convection in a thermal convection loop. The thermal convection loop is a relatively simple experimental paradigm that exhibits complex dynamic behavior and provides a convenient platform for evaluating and comparing various control strategies. The objective of this study is to evaluate the feasibility of employing optimal control and nonlinear estimator to alter naturally occurring flow patterns and to compare the performance of the optimal controller with that of other controllers such as neural network controllers. It is demonstrated that when the system's model is not known, experimental data alone can be utilized for the construction of a proportional controller.