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.
Abstract In this study, a novel approach is described to the design of an interval type‐2 fuzzy neural system (IT2 FNS). It differs from the classical IT2 FNS in its use of parameterized conjunctors. In the optimization of the IT2 FNS, the membership functions are kept fixed and only the parameters of the conjunctors and the parameters in the consequent are tuned. In this study, the gradient based learning algorithm is used. The approach is tested for the modeling of a benchmark nonlinear function and for the wheel slip control of a quarter car model (QCM). In the stated applications, in the absence of any expert knowledge, some knowledge about the system is gained by the use of the interval type‐2 fuzzy c‐means (IT2 FCM) clustering algorithm. Nevertheless, this requires the number of classes to be known beforehand. To alleviate this problem, some validity indices that have been suggested in the literature and a novel validity index that carries less computational burden are considered to determine the number of classes and the number of fuzzy rules. Simulation studies are presented and compared with the results from the literature.
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.
In modern Battery Management Systems (BMSs), it is significant to obtain an accurate battery model to estimate the states of the battery such as State of Charge (SoC), State of Health (SoH), State of Power (SoP), State of Safety (SoS) etc. Traditional lithium-ion batteries (LIBs) have some drawbacks in terms of safety and energy density. To overcome these drawbacks, all-solid-state batteries (ASSBs) are being developed as an alternative solution for conventional lithium-ion batteries. The focus of this study is on all-solid-state batteries and their modeling based on the equivalent circuit model. On the other hand, the modeling of a cell needs an immense amount of data and long test duration time. Instead of cell characterization test data, the all-solid-state cell is modeled by using DC internal resistance (DC-IR) information. During this study, two different equivalent circuit models containing series-connected RC pairs with and without ohmic resistance are investigated. In addition, the equivalent circuit model parameters are derived via Genetic Algorithm. Moreover, measured and simulated resistance values are compared with Mean Absolute Error (MAE) criteria for two different equivalent circuit models. Finally, the plausibility of the obtained models are analyzed and compared with experimental Hybrid Pulse Power Characterization (HPPC) test results.
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.
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.
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