The fuzzy control, with its tight relation to human decision making and reacting, is ever more often used in control applications because it is an advanced and for humans a more natural control method. It simply transforms human operator’s experience into control rules. A typical problem in fuzzy control arises with the number of control rules, because the number of control rules increases geometrically with the number of observed variables, thus increasing the required computing power. This thesis presents a method of separation of the fuzzy controller into cascade fuzzy controllers, significantly reducing the number of control rules. The control system stability is ensured by determining controller parameters according to Lyapunov stability theory, which has been adjusted to work with fuzzy variables. The cascade fuzzy controller was simulated in Matlab Simulink and then tested on a “ball and beam“. This thesis also presents an adaptive control method based on reference model and lead-lag compensator. The presented method does not require detailed previous knowledge about the controlled process, but only the basic information about its dynamic behaviour expressed in the form of a second-order reference model. An existing control system can be upgraded with this adaptation method regardless of the existing controller, because the compensator is added to the controller output. The implementation of this adaptation type requires a test signal which is used for determination of system changes and actions required for reduction of the effect of these changes on the system dynamic behavior. This adaptation algorithm has been tested on a permanent magnet synchronous motor model. cascade fuzzy control, adaptive control, intelligent controlThe presented adaptation algorithm, along with a classical fuzzy controller, was implemented as a feature in the software tool for off-line simulation of industrial robots – Leonardo.