In this paper, a Q($lambda$)-learning fuzzy inference system (QLFIS) is applied to a differential game. We use the homicidal chauffeur differential game as an example of the method. The suggested method allows both the evader and the pursuer to learn their optimal strategies. The parameters of the input and the fuzzy rules of a fuzzy controller are tuned autonomously using Q($lambda$)-learning. Simulation results demonstrate that the players are able to learn their optimal strategies.

 In this paper a method to reject periodic biased disturbances of unknown frequency is presented. The entire set of unknown disturbance parameters is estimated via a third-order generalized integrator (TOGI). An interesting property of the method is that internal signals of TOGI are combined linearly to produce the control signal, resulting in a very simple algorithm. Only a single adaptive parameter permits to govern the system. It is shown that, within the assumptions of an averaging analysis, the adaptive system is stable and completely rejects the disturbance, even if a rough estimation of the plant is available. Bounds on the uncertainty of the plant are given in terms of the bounds on the input disturbance. Simulations demonstrate the properties of the algorithm in a variety of conditions.

 One approach to a recursive identification of continuous time systems was implemented in self tuning control of a system of interconnected tanks. Since derivatives of input and output variables of continuous time systems can not be directly measured, differential filters and filtered variables are established to substitute primary variables. The filtered variables are then used in a recursive identification procedure where the classical recursive least squares method is used to identify the system. Results of real time experiments are compared to results obtained with an analogical discrete controller.

 A new Adaptive Feed-Forward strategy is presented, that identifies the periodic disturbances of known frequencies. These disturbance sources can be external or internal. Internal disturbances are also called self-excited vibrations that come from the load or its parameter variations. These variations depend periodically on the angle of rotation particularly in the case of torsion machines. The identified parameters of the disturbance model are used to design feed forward controller to counteract the disturbance effect on the system output, this is to be with a minimum interaction with an already existing set point tracking feedback controller.

 This paper adresses a Lyapunov stability analysis of nonlinear systems control. We consider an adaptive control scheme based on recurrent neural networks emulator and controller with decoupled adaptive rates. Lyapunov sufficient stability conditions for decoupled adaptive rates of the emulator and controller are proposed. In order to guarantee the fast convergence, the optimal adaptive rate of controller is derived in the sense of Lyapunov exponential stability taking into account the stability of the emulator. The good performances of the proposed stable control design are shown with numerical simulations results.

 The design of a robust position control scheme for an induction motor drive using the field oriented control theory is proposed. The proposed sliding-mode control law incorporates an adaptive sliding gain in order to adjust the sliding gain to the system uncertainties. Moreover, the sliding gain adaptation avoids having to calculate the upper limit for the system uncertainties. The design also incorporates a load torque observer in order to obtain the load torque applied to the induction motor without the use of the load torque sensor. The proposed observer is based on the system dynamical equation and uses the rotor speed and the stator current in order to obtain the load torque.

The stability analysis of the proposed controller under parameter uncertainties and load torque variations is provided using the Lyapunov stability theory. Finally experimental results show that the proposed controller with the proposed observer provides high-performance dynamic characteristics and that this scheme is robust with respect to plant parameter uncertainties and load torque variations.