This paper proposes a fuzzy approximation-based adaptive sliding-mode controller for uncertain nonlinear perturbed underactuated systems. The fuzzy logic system is used for approximating the unknown nonlinear functions. For estimating the parameters of the fuzzy systems and some unknown bounds, a set of adaptation laws are appropriately designed. The boundedness of all signals of the closed-loo...

This paper presents two optimal approaches of two-stage fuzzy controller for traffic signals at isolated intersections. Firstly, in the light that traffic status variables in two-stage controller leads to the inefficiency of traffic status weakening under low traffic flow, a two-stage combination fuzzy controller is designed from the perspective of structural optimization; this controller intro...

According to the character of the volume-lag of the controlled process of three-tank-system, Smith predictor was adopted to compensate three-tank-system fuzzy adaptive control system. Fuzzy adaptive Smith predictive control system is composed with Smith predictor and fuzzy adaptive controller. The PID parameters were setting on line. This algorithm uses fuzzy adaptive PID control to improve the...

This paper deals with control design and stability analysis for a class of nonlinear systems with system uncertainties as well as input constraint when using the T-S fuzzy model. As a result, it achieves an adaptive fuzzy controller, which consists of two components: one is corresponding with the regular state feedback controller based on the LMI approach in order to deal with the known part in...

This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON), constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which has a feedforward multilayer network and is developed for the realization of a fuzzy controller. One FALCON performs as a critic network (fuzzy predictor), the other as an action network (fuzzy controller). Using t...

This study presents an adaptation method for Sugeno fuzzy inference systems that maintain the readability and interpretability of the fuzzy model during and after the learning process. This approach can be used for modelling of dynamical systems and for building adaptive model-based control algorithms for chemical processes. The gradient-descent based learning algorithm can be used on-line to f...

This paper proposes a fuzzy-identification-based adaptive control scheme for the chaotic dynamic systems using backstepping control approach, which is referenced as adaptive fuzzy backstepping control (AFBC). The proposed AFBC offers a design approach to drive the chaotic trajectory to track a desired trajectory, and it is comprised of a fuzzy backstepping controller and a robust controller. Th...

This paper investigates the effectiveness of two adaptive control strategies for modulating control force of variable friction dampers (VFDs) that are employed as semi-active devices in combination with laminated rubber bearings for seismic protection of buildings. The first controller developed in this study is an adaptive fuzzy neural controller (AFNC). It consists of a direct fuzzy controlle...

As demand for high quality transmission increases, improving spectrum efficiency and error performance in wireless communication systems are important. OFDM is a multi-carrier modulation technique with densely spaced sub-carriers that has gained a lot of popularity among the broadband community in the last few years. One of the promising approaches to next generation communication systems are a...

This paper proposes a direct adaptive fuzzy-model-based control algorithm. The controller is based on an inverse semi-linguistic fuzzy process model, identified and adapted via inputmatching technique. For the adaptation of the fuzzy model a general learning rule has been developed employing gradient-descent algorithm. The on-line learning ability of the fuzzy model allows the controller to be ...