In this paper, an adaptive backstepping control system with a fuzzy integral action is proposed to control the mover position of a linear induction motor (LIM) drive. First, the indirect field oriented control LIM is derived. Then, an integral backstepping design for indirect field oriented control of LIM is proposed to compensate the uncertainties which occur in the control. Finally, the adapt...

In this paper an adaptive neuro fuzzy inference system based on interval Gaussian type-2 fuzzy sets in the antecedent part and Gaussian type-1 fuzzy sets as coefficients of linear combination of input variables in the consequent part is presented. The capability of the proposed method (we named ANFIS2) to function approximation and dynamical system identification is shown. The ANFIS2 structure ...

This paper presents an adaptive neuro-fuzzy controller ANFIS (Adaptive Neuro-Fuzzy Inference System) for a bilateral teleoperation system based on FPGA (Field Programmable Gate Array). The proposed controller combines the learning capabilities of neural networks with the inference capabilities of fuzzy logic, to adapt with dynamic variations in master and slave robots and to guarantee good prac...

In this paper, a new adaptive fuzzy reasoning method using compensatory fuzzy operators is proposed to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is proved to be a universal approximator. The compensatory neural fuzzy networks built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons cannot only adaptively adjust fuz...

A novel control scheme, fuzzy immune adaptive model inversion control, was put forward, aiming at the large flight envelope curve control problem of helicopter. The proposed scheme was designed based on model inversion theory, biology immune response mechanism and fuzzy control method. It could achieve effective control throughout large flight envelope curve via the design of fuzzy immune onlin...

This paper presents a practical direct adaptive fuzzy H tracking control (AFHC) approach for a class of uncertain nonlinear systems with unknown control gain functions and external disturbances. A modified output tracking error is defined to eliminate high gain at the control input and to improve transient performance. An ideal control law is developed to eliminate the restriction that the cont...

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

A fuzzy adaptive control algorithm is presented in the paper. Its hallmarks are simplicity and global stability, i.e. boundedness of all the signals in the system. The control can be successfully applied to nonlinear plants that are predominantly of the first order. Thus, this approach covers quite broad class of plants that are often encountered in process industries. The proposed algorithm wa...

In this paper, an adaptive fuzzy robust output feedback control approach is proposed for a class of single input single output (SISO) strict-feedback nonlinear systems without measurements of states. The nonlinear systems addressed in this paper are assumed to possess unstructured uncertainties, unmodeled dynamics and dynamic disturbances, where the unstructured uncertainties are not linearly p...

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Chapter . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  Working memory studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  Adaptive system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  Fuzzy system . . . . . . . . . . . . ...