The modern industrial processes are difficult to model and control by classical means for their nonlinearity, inertia, model uncertainty and varying parameters. The adaptive fuzzy logic controllers (AFLCs) improve the system performance but are computationally hard to design and embed in programmable logic controllers (PLCs) for wider industrial applications. In this chapter a design approach for simple AFLCs is suggested, based on main controllers—linear, FLC or parallel distributed compensation (PDC), and fuzzy logic supervisors (FLSs) for on-line auto-tuning of their gains or scaling factors. The effect is a continuous adaptation of the control surface in response to plant changes. Approximation of the designed AFLC to a PDC equivalent on the basis of neuro-fuzzy and optimization techniques enables the stability analysis of the AFLC system using the indirect Lyapunov method and also its PLC implementation. The AFLC is applied for the real time control of the processes in a chemical reactor, a dryer, a two-tank and an air-conditioning systems, decreasing overshoot, settling time, control effort and coupling compared to classical FLC and linear control systems.