In this paper a combination of Fuzzy Logic Control (FLC) and Model 1 Reference Adaptive Control (MRAC) will be developed to stabilize and control a 2 fixed-wing unmanned aerial vehicle (UAV). The control must be able to direct the 3 airplane towards different waypoints while at the same time maintaining the UAV 4 stable. Also, the control should be transferable to similar UAV models with little...

A Recurrent Trainable Neural Network (RTNN) with a two layer canonical architecture and a dynamic Backpropagation leamng method are applied for identification and control of complex nonlinear mechanical plants. The paper uses a Fuzzy-Neural Hierarchical Multi-Model (FNHMM), which merge the fuzzy model flexibility with the learning abilities of the RNNs. The paper proposed the application of two...

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controlle...

Adaptive control for nonlinear time-varying systems is of both theoretical and practical importance. In this paper, we propose an adaptive control methodology for a class of nonlinear systems with a time-varying structure. This class of systems is composed of interpolations of nonlinear subsystems which are input–output feedback linearizable. Both indirect and direct adaptive control methods ar...

In this study, an adaptive fuzzy sliding-mode control (AFSMC) system is adopted to control the position of an induction servo motor. The AFSMC system comprises the fuzzy control design and the robust control design. In the fuzzy control design a fuzzy controller is designed to mimic an ideal control law. In the robust control design a robust controller is designed to compensate the approximatio...

This paper presents a stable new algorithm for force/position control in robot manipulators. In this algorithm, position vectors are measured by sensors and then used in the control law. Since using force sensor has some issues such as high costs and technical problems, an approach is presented to overcome these issues. In this respect, force sensor is replaced by an adaptive fuzzy estimator to...

This paper proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlling nonlinear dynamic systems. The RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules. Temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network (FNN). The RFNN...

This paper proposes an adaptive fuzzy control application to support a vendor managed inventory (VMI). The methodology applies fuzzy control to generate an adaptive smoothing constant in the forecast method, production and delivery plan to eliminate, for example, the rationing and gaming or the Houlihan effect and the order batching effect or the Burbidge effects and finally the Bullwhip effect...

The objective of the model reference adaptive fuzzy control (MRAFC) is to change the rules definition in the direct fuzzy logic controller (FLC) and rule base table according to the comparison between the reference model output signal and system output. The MRAFC is composed by the fuzzy inverse model and a knowledge base modifier. Because of its improved algorithm, the MRAFC has fast learning ...

A “learning system” possesses the capability to improve its performance over time by interaction with its environment. A learning control system is designed so that its “learning controller” has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant. In this brief paper, we introduce a learning...