fuzzy controller

In this paper, a new hybrid direct fuzzy quantum control is designed for a class of quantum stochastic systems where the dynamics of the state variable are prescribed via a Quantum Stochastic Differential Equation (QSDE) with respect to a quantum Brownian motion on a quantum probability space. The presented control is comprised of two parts, an adaptive fuzzy control part that performs the main control action and a quantum-fuzzy control part that is implemented when the existence and uniqueness of the solution are not established. Thereby, the adjusted laws of the control parameters and the quantum-fuzzy rules are designed via the Lyapunov-based technique such that the stability of the system is guaranteed. One theorem for facilitating the Fuzzy controller design algorithm is presented and proved. The proposed control method enhances the applicability of the quantum stochastic control theory for many practical control problems such as portfolio management. Therefore, theoretical results are illustrated by simulating the pairs trading problem. According to simulation results, the performance of the pairs trading strategy is improved as an increasing return portfolio that is controlled by the proposed method.

Many dynamic processes are characterized by parametric or structural uncertainties due to internal and externaldisturbances. Existing deterministic models could not handle the uncertainties inherent in these processes. A valuablealternative to control these processes is the use of a type-3 fuzzy system. Since type-3 fuzzy systems use threedimensionalmembership functions, they have more capacity to model uncertainties. This paper introduces the designof a type-3 fuzzy logic system (FLS) for the control of dynamic plants. Utilizing type-3 fuzzy logic, the architectureof the type-3 fuzzy control system (T3FCS) is proposed. The knowledge base of the controller is constructed and itsdesign stages are presented. The inference mechanism of type-3 FLS is developed using α slices and interval type-3membership functions. The proposed type-3 FLS is utilized for controlling nonlinear dynamic plants. The modelingof the proposed T3FCS is performed and transient response characteristic is derived using different stepwise excitationsignals. A comparison of the designed system with the type-1 FLS-based system is provided. The obtained simulationresult demonstrates the efficiency of using the proposed type-3 FLS in the control of dynamic systems characterized byuncertainties.

In this paper‎, ‎we present a new approach for achieving leader-follower consensus in a network of nonlinear dynamic agents with an undirected graph topology‎, ‎using a fuzzy sliding mode controller (FSMC) for Multi-Agent Systems (MASs)‎. ‎Our proposed sliding mode controller is based on a separating hyperplane that effectively addresses the consensus problem in MASs‎. ‎Additionally‎, ‎we design a fuzzy controller to eliminate the chattering phenomenon‎. ‎According to the communication graph topology and the Lyapunov stability condition‎, ‎the proposed FSMC satisfies the consensus condition‎. ‎One significant advantage of our approach is that the system states converge to the sliding surface quickly and remain on the surface‎, ‎thereby ensuring better tracking performance‎. ‎We validate the effectiveness of our proposed approach through simulation results‎.

Today, computer network fault diagnosis is one of the key challenges experts are facing in the field of computer networks.  Therefore, achieving an automatic diagnosis system which is based on artificial intelligence methods and is able to diagnose faults with maximum accuracy and speed is of high importance. One of the methods which is studied and utilized up to now is artificial neural networks with a back propagation algorithm while using neural networks with a back propagation algorithm has two main challenges in front. The first challenge is related to the backpropagation learning type as it is a supervised learning requiring inductive knowledge driven from previous conditions. The second challenge is the long time required for training such a neural network. In this work, combining neural networks with a backpropagation algorithm and fuzzy logic is applied as a method for confronting these challenges. The result of this study shows that fuzzy clustering is able to provide the inductive knowledge required for backpropagation learning by determining the membership degree of training samples to different clusters of network faults. Also, according to the simulations taken place, implementing a fuzzy controller in determining the learning rate in each backpropagation iteration has resulted in successful outcomes. Thus, the learning speed of this algorithm has been increased in comparison to the constant learning rate mode resulted in reducing the training time duration of this neural networks.

Appropriate modeling of the coupled neurons helps us understand neurons' natural functions. In this paper fuzzy logic controller has been designed to synchronize two coupled neuron models. The fractional-order neurons are based on the FitzHugh-Nagumo (FHN) model. An optimized fuzzy controller is designed to synchronize the behavior of two neurons with each other in the presence of external disturbances. This controller overcomes the disturbance. The simulation example shows the performance of the proposed method.

Fuzzy control is one of the most important parts of fuzzy theory for which several approaches exist. Mamdani uses $\alpha$-cuts and builds the union of the membership functions which is called the aggregated consequence function. The resulting function is the starting point of the defuzzification process. In this article, we define a more natural way to calculate the aggregated consequence function via arithmetical operators. Defuzzification is the optimum value of the resultant membership function. The left and right hand sides of the membership function will be handled separately. Here, we present a new ABFC (Arithmetic Based Fuzzy Control) algorithm based on arithmetic operations which use a new defuzzification approach. The solution is much smoother, more accurate, and much faster than the classical Mamdani controller.

As a generalization of the triple I method, the universal triple I method is investigated from the viewpoints of both fuzzy reasoning and fuzzy controller. The universal triple I principle is put forward, which improves the previous triple I principle. Then, unified form of universal triple I method is established based on the (0,1)-implication or R-implication. Moreover, the reversibility property of universal triple I method is analyzed from expansion, reduction and other type operators, which demonstrate that its reversibility property seems fine, especially for the case employing the (0,1)-implication. Lastly, we analyze the response ability of fuzzy controllers based on universal triple I method, then the practicability of triple I method is improved.