This paper presents a methodology based on direct adaptive fuzzy controller to control the pitch attitude of Satellite Launch Vehicle (SLV). To generate reference trajectories of a four-stage Launch Vehicle, Trajectory Optimization has been performed offline using Genetic Algorithms. The optimization routine takes into account the maximum angle of attack along with normal and lateral overload constraints. To efficiently follow the reference pitch profile, a fuzzy based controller is proposed in which an adaptation law is devised which automatically adjusts the tunable parameter. This tunable parameter is the consequent part of fuzzy IF-THEN rules. Lyapunov principle is incorporated in the adaptation law for ensuring systems stability. It is observed that although the Lyapunov approach make sure that the system is asymptotically stable, it is very critical to select appropriately the value of semi positive definite matrix. To analyze the performance of proposed control scheme, 6DOF simulation model is developed in Simulink.

 This paper explores the application of a multi-step predictive adaptive controller to control the level of neuromuscular relaxation during a general anaesthesia. The problem of neuromuscular relaxation in humans is characterized by a large variability of the pharmacokinetics (PK) and pharmacodynamics (PD) among patients. Because the administrating potent drugs may cause side effects it is important to obtain the adequate level of neuromuscular blockade but avoiding the administration of drug overdoses. To tackle the problem of the variability among patients, the data obtained with the response to the first bolus can be analyzed to extract information of the patient/drug pharmacokinetics/pharmacodynamics. However this approach does not provide a complete solution for the problem, therefore an online model identification based on a nonlinear transformation is explored with the multi-step predictive adaptive controller. Computer simulations are used to evaluate the performance and the main difficulties of its applicability.

 This paper addresses the problem of controlling the temperature of an air heating fan with an unknown flow input rate. This time-varying uncertainty in the dynamics of the plant significantly reduces the performance of the closed-loop system, if a single (fixed) non-adaptive controller is used. Moreover, the average temperature of the air flowing through the system, that can be seen as an offset on the corresponding dynamics, is also (slowly) time-varying and highly dependent on the ambient temperature. Therefore, an alternative approach to this problem is proposed, by resorting to a novel multiple-model adaptive control methodology that relies on set-valued observers to identify the operating region of the plant. The suggested method is evaluated experimentally, demonstrating a loss of performance of about 2% when compared to the (unrealizable) perfect model identification scenario. As a shortcoming, the computational requirements due to the use of SVOs are considerably larger than the ones needed for an LTI non-adaptive controller.

 We have recently proposed a locally homogeneous stabilizing controller that guarantees a convergence rate for input affine nonlinear control systems. However, adaptive control problem has not been discussed. In this research, we propose an adaptive homogeneous controller for homogeneous control systems. Consecutively, we propose an adaptive locally homogeneous controller with a convergence rate guarantee for input affine nonlinear systems. We apply our proposed controller to a stabilization problem of a magnetic levitation system. We confirm the effectiveness of the proposed method by an experiment.

 Using Monte-Carlo simulations, the performance of three recently developed multiple-controller robust adaptive control methods is compared for different time-varying uncertain parameter waveforms, using a mass-spring-dashpot benchmark example. We further examine the performance of these different adaptive methods with that of the best possible robust nonadaptive design using the same physical example. Whenever possible, we also compare the adaptive methods performance with that of the (unrealizable) Perfect Model Identification (PM.ID) introduced in [1]. In order to have a fair comparison of the performance obtained with the different control laws we used the same bank of local controllers and tuned each approach for its best performance. The Monte-Carlo simulations highlight the strength and aptitude of each method as well as its shortcomings and drawbacks.

 A novel and simple observer scheme is proposed for rotor speed and position reconstruction for Permanent Magnet Synchronous Machines. The reference frame adopted in the observer is pushed toward the synchronous one by forcing it to be intrinsically aligned with the estimated back-emf vector and by designing suitable adaptations law for its speed and angle along with the back-emf amplitude. Stator flux dynamics are not used in this approach, leading to an improved robustness with respect to voltage and current measurement uncertainties. Stability analysis is carried out by using singular perturbation approach. Effective tuning rules are drawn exploiting insightful linearization of the proposed nonlinear adaptive observer. Simulations show the properties of the presented method.