The objective of the present PhD thesis is the development of control methods, suitable for controlling complex multiple input - multiple output nonlinear dynamical systems. Particular emphasis will be given to the development of economic predictive control (EMPC) techniques, indented to be applied to complex physicochemical processes such as wastewater treatment plants. The control of these processes deals with many challenges due to their high complexity and as a result, development of controllers able to satisfy certain specifications not only regarding the operating points of these processes, but also regarding the consumption of energy resources, is indeed a challenging task. For the purpose of achieving these goals, in the context of this PhD thesis, methods of system identification based on computational intelligence techniques such as fuzzy logic, neural networks, evolutionary computation and swarm intelligence will be developed. Further to the aforementioned objectives, particular emphasis will be devoted to the study of stability and robustness of the designed controllers. More specifically, in order to prove the global stabilization of the dynamical systems with the integration of the controllers, methods of studying stability and robustness based on Lyapunov functions and theorems will be applied. Finally, part of the present PhD thesis will be committed to ways of formulating and solving the mathematical optimization problems to be found in the procedures of system identification and predictive control.