Ayse Cisel Aras

A servo system control with time-varying and nonlinear load conditions using type-2 TSK fuzzy neural system

Abstract: A type-2 Takagi-Sugeno-Kang fuzzy neural system is proposed and its parameter update rules are derived using fuzzy clustering and gradient learning algorithms. The proposed type-2 fuzzy neural system is used for the control and the identification of a real-time servo system. Fuzzy c-means clustering algorithm is used to determine the initial places of the membership functions to ensure that the gradient descent algorithm used afterwards converges in a shorter time. A number of different load conditions including nonlinear and time-varying ones are used to investigate the performance of the proposed control algorithm. The control structure has the ability to regulate the servo system with reduced oscillations when compared with the results of its type-1 counterpart around the set point signal in the presence of load disturbances.

Trajectory tracking of a 2-DOF helicopter system using neuro-fuzzy system with parameterized conjunctors

In this study, the aim is to track the desired pitch and yaw axis trajectories of a 2-DOF helicopter system. For this purpose, neuro-fuzzy system with parameterized conjunctors is used and its performance is compared with a conventional control approach, namely a PID controller. In neuro-fuzzy methods, in order to obtain an optimal fuzzy model, the most commonly used approach is to tune the parameters of the membership functions at the antecedent part of the fuzzy rules. This adaptation process may lead to loss or distortion of the knowledge that is carried by these membership functions. To alleviate this problem, the parameters of the parameterized conjunctors are tuned instead of the parameters of these membership functions.

An adaptive neuro-fuzzy architecture for intelligent control of a servo system and its experimental evaluation

In this paper the development of an adaptive neuro-fuzzy architecture for the speed control of a servo system with nonlinear load is presented. The synthesis of the structure is described and a learning algorithm for the neuro-fuzzy control system is derived. The supervised learning algorithm is used to train the unknown coefficients of the system, and then the fuzzy rules of the neuro-fuzzy system are generated. A number of simulation studies are carried out, and the results are compared with those obtained with a PI controller tuned using desired time response characteristics. These and the experimental studies presented show that the neuro-fuzzy control system has a better control performance than the conventional PI controller.

A comparison of fuzzy methods for modeling

The paper has the goal of comparing the performance of four different approaches to fuzzy modeling, using parameterized conjunctions, a novel concept named constrained fuzzy sets (CFSs), CFSs with parameterized conjunctions, and unnormalized interval type-2 Takagi Sugeno Kang (IT2 TSK). The theoretical and mathematical backgrounds of the four approaches are briefly described and their performances are compared in approximating a nonlinear function.

Slip control of a quarter car model based on type-1 fuzzy neural system with parameterized conjunctions

In conventional fuzzy modeling and control, to obtain an optimal fuzzy system, a commonly used approach is to tune the parameters of the membership functions. However, if the membership functions carry significant expert knowledge about the system, this may be lost or distorted during the optimization process. In order to prevent such a loss of valuable information, parameterized conjunction operators may be used and their parameters can be tuned instead. In this paper such an approach is adopted to optimize a type-1 fuzzy neural system (FNS), used for slip control of a Quarter Car Model (QCM). The simulation results presented indicate the efficacy of the approach in meeting the desired objectives even under noisy conditions.

Nonlinear function approximation based on fuzzy algorithms with parameterized conjunctors

In this study, two fuzzy algorithms, type-1 fuzzy algorithm with parameterized conjunctors and a novel approach interval type-2 fuzzy algorithm with parameterized conjunctors are used in the modeling application for nonlinear functions. The aim of using parameterized conjunctors as fuzzy operators in these algorithms is not to lose or distort the expert knowledge about the system during the optimization process. In this study, this linguistic information about the system is obtained by using fuzzy c-means clustering algorithms. Then, the designed fuzzy algorithms are tested on two benchmark nonlinear functions in modeling application.

Experimental evaluation of a type-2 fuzzy control algorithm on an Anti-Lock Braking System

Control of nonlinear systems is a challenging task in control engineering and the use of type-2 fuzzy logic controllers (FLCs) has been proposed as a promising approach, as they can perform adequately in dynamically unstructured environments that include large amount of uncertainties. In this paper, an Anti-Lock Braking System (ABS) is controlled by an interval type-2 fuzzy logic controller. The control algorithm is used on a quarter vehicle model and it is seen through simulation studies that it results in a good performance with fast convergence. These results are experimentally validated on a laboratory setup.

A servo system control with time-varying load using type-2 fuzzy neural system

A type-2 fuzzy neural system (T2FNS) is proposed in this paper for process control. The structure of the system is presented and the rules for updating its parameters are derived using the gradient algorithm. The effectiveness of the proposed approach is evaluated on a laboratory setup that regulates the speed of a DC motor and the experimental results are compared with those obtained with the use of a type-1 fuzzy neural system (T1FNS). It is seen that T2FNS results in reduced oscillations around the set point in the presence of load disturbances.

Parameter Identification of an Electrical Battery Model using DC-IR Data.

Parameter identification of an electrical battery model is significant for the analysis of the performance of a battery. In order to obtain an accurate electrical battery model, a series of cell characterization tests should be conducted which will take a considerable amount of time. In this study, in order to identify the parameters of the electrical battery model in a short amount of time with an acceptable accuracy, DC-IR data is used. DC-IR test will take less time compared to the cell characterization tests. For the parameter identification, one of the most commonly used evolutionary algorithm (EA), Genetic Algorithm (GA) is used for the curve fitting problem and its performance is compared with the Levenberg-Marquardt algorithm.

Control of Antilock Braking System using Spiking Neural Networks

Model-free approaches such as Artificial Neural Networks and Fuzzy Controllers are widely used in the control of Antilock Braking System (ABS) due to its strongly nonlinear structure and uncertainties involved. In this paper the design of a Spiking Neural Network (SNN) controller is considered for the regulation of the wheel slip value at its optimum value. For the training of the network a gradient descent based approach is followed. To formulate the generation of a new spike train from the incoming spikes, the Spike Response Model (SRM) is used. Delay coding is utilized to convert real numbers into spike times. The control algorithm is applied to a quarter vehicle model, and it is verified through simulations indicating fast convergence and good performance of the designed controller.