In this paper, an adaptive fuzzy controller based on fuzzy neural network is proposed for uncertain nonlinear systems. The main advantages are the simple design, no requirement of system model, and release of fixed universal range of fuzzy output. A fuzzy neural network is applied to on-line identify the control system and provide sufficient information of the adaptive laws for the proposed fuz...

In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is sol...

In this paper, we design an adaptive controller to compensate the nonlinear friction model when the output is the position. First, we present an adaptive differential filter to estimate the velocity. Secondly, the dynamic friction force is compensated by a fuzzy adaptive controller with position measurements. Finally, a simulation result for the proposed controller is demonstrated.

In this paper, a fuzzy adaptive neural-network model-following speed controller for permanent-magnet synchronous motor (PMSM) drives is proposed. The fuzzy neuralnetwork model-following controller (FNNMFC) consist of a proportional plus integral (PI) like-fuzzy controller in addition to an on-line trained neural-network model-following controller (NNMFC). This controller, FNNMFC, combines the m...

This paper introduces an optimal fuzzy proportional–integral–derivative (PID) controller. The fuzzy PID controller is a discrete-time version of the conventional PID controller, which preserves the same linear structure of the proportional, integral, and derivative parts but has constant coefficient yet self-tuned control gains. Fuzzy logic is employed only for the design; the resulting control...

This paper proposes a novel intelligent control scheme using type-2 fuzzy neural network type-2 FNN system. The control scheme is developed using a type-2 FNN controller and an adaptive compensator. The type-2 FNN combines the type-2 fuzzy logic system FLS , neural network, and its learning algorithm using the optimal learning algorithm. The properties of type-1 FNN system parallel computation ...

in recent years, underactuated nonlinear dynamic systems trajectory tracking, such as space robots and manipulators with structural flexibility, has become a major field of interest due to the complexity and high computational load of these systems. hierarchical sliding mode control has been investigated recently for these systems; however, the instability phenomena will possibly occur, especia...

The paper discusses the features of the Biomass Boiler drum water level. Conventional PID Control System can not reach a satisfaction result in nonlinearity and time different from Biomass Boiler Drum Water Control System. In this study, a kind of fuzzy self-adaptive PID controller is described and this controller is used in biomass boiler’s drum water level control system. Using the simulink t...

The ship main engine speed control system is a typical nonlinear system and it is uncertain characteristics, It is influenced by the wind, wave, flow etc. So it is difficulted to designed the diesel engine mathematical model. This paper presents a nonlinear mathematical model of ship main engine. Because the active disturbance rejection controller does not depend on the mathematical model of th...

In this paper, the principle of sliding mode control is used as a basis to develop an adaptive fuzzy controller for uncertain dynamic time-delayed systems with series nonlinearities. The control method provides a simple way to achieve asymptotic stability of the uncertain time-delayed system. Other attractive features of the method include a minimal realization of the adaptive fuzzy controller ...