17,476 research outputs found
Perancangan Dan Implementasi Sistem Pengaturan Kecepatan Motor BLDC Menggunakan Kontroler Pi Berbasiskan Neural Fuzzy Hibrida Adaptif
Mobil listrik menjadi inovasi terbaru dengan tujuan utama untuk
melepaskan ketergantungan pada bahan bakar minyak. Penelitian yang
telah ada memaparkan bahwa motor listrik yang sesuai untuk
menggerakkan mobil listrik adalah motor Brushless Direct Current
(BLDC). Beberapa keunggulan motor BLDC antara lain adalah suara
halus, ukuran kompak, torsi besar, efisiensi tinggi, memiliki umur pakai
yang panjang, dan mudah dikontrol. Performa dan kecepatan motor
BLDC dapat terganggu apabila bekerja pada kondisi berbeban. Oleh
karena itu, dibutuhkan pengaturan kecepatan menggunakan sebuah
kontroler yang dapat menjaga kecepatan motor BLDC sesuai set-point
meskipun sedang beroperasi pada kondisi berbeban.
Kontroler yang digunakan untuk mengatur kecepatan motor BLDC
adalah kontroler Proposional Integral (PI) berbasiskan Neural-Fuzzy
Hibrida Adaptif. Kontroler PI dipilih karena dapat mengeliminasi steadystate
error. Sedangkan Neural-Fuzzy Hibrida Adaptif merupakan
kombinasi antara Fuzzy dan Neural-Network. Fuzzy digunakan untuk
penentuan parameter kontroler PI. Parameter kontroler PI didapatkan dari
Neural-Network. Karakteristik respon terhadap hasil implementasi
memiliki settling time 20 detik, overshoot sebesar 1,1%, dan time constant
7,7 detik.
==================================================================================================================Electric cars become the latest innovations with the main objective
to release the dependence on fossil fuels. Research that has been there
explained that the electric motor is suitable to drive an electric car is a
Brushless Direct Current (BLDC) motor. Some of the advantages of
BLDC motor is smooth sound, compact size, large torque, high efficiency,
has a long lifespan, and easy to control. Performance and speed of the
BLDC motor can be disturbed when working on load condition.
Therefore, it takes the speed setting using a controller that can keep
BLDC motor speed suit to set-point even when operating at load
condition.
The controller used to control the speed of the BLDC motor is a
Proportional Integral (PI) controller based Hybrid Adaptive Neural-
Fuzzy. PI controller is chosen because it can eliminate the steady-state
error. While Hybrid Adaptive Neural-Fuzzy is a combination of Fuzzy
Logic and Neural-Network. Fuzzy Logic is used to determine parameters
PI controller. Parameters PI Controller obtained from Neural-Network.
The response characteristics of the results of the implementation have 20
seconds settling time, overshoot of 1.1%, and the time constant of 7.7
seconds
To develop an efficient variable speed compressor motor system
This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque
is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce
dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy
optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization
control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained
clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment
A neuro-fuzzy architecture for real-time applications
Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach
Intelligent methods for complex systems control engineering
This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems.
The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning).
Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions
A Novel Mobile Robot Navigation System Using Neuro-Fuzzy Rule-Based Optimization Technique
Abstract: A new novel approach to control the autonomous mobile robot that moved along a collision free trajectory until it reaches its target is proposed in this study. The approach taken here utilizes a hybrid neuro-fuzzy method where the neural network effectively chooses the optimum number of activation rules in order to reduce computational time for real-time applications. Initially, a classical fuzzy logic controller has been constructed for the path planning problem. The inference engine required 625 if-then rules for its implementation. Then the neural network is implemented to choose the optimum number of the activation rules based on the input crisp values. Simulation experiments were conducted to test the performance of the developed controller and the results proved that the approach to be practical for real time applications. The proposed neuro-fuzzy optimization controller is evaluated subjectively and objectively with other fuzzy approaches and also the processing time is taken in consideration
Validation and Verification of Aircraft Control Software for Control Improvement
Validation and Verification are important processes used to ensure software safety and reliability. The Cooper-Harper Aircraft Handling Qualities Rating is one of the techniques developed and used by NASA researchers to verify and validate control systems for aircrafts. Using the Validation and Verification result of controller software to improve controller\u27s performance will be one of the main objectives of this process. Real user feedback will be used to tune PI controller in order for it to perform better. The Cooper-Harper Aircraft Handling Qualities Rating can be used to justify the performance of the improved system
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Fuzzy logic control of telerobot manipulators
Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems
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