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Showing 5 results for Subject:
Mr. Mostafa Rezayi, Dr. Hamid Farrokhi,
Volume 1, Issue 2 (1-2014)
Abstract
A new multiobjective power control algorithm is presented in this paper in which power update is accomplished using local information. The algorithm benefits a multiobjective optimization technique the objectives of which are designed in a way to not only minimize the users’ transmit power but also maintain the users’ SIR at an acceptable level, on one hand, and reduce as much possible the SIR fluctuations, on the other hand. The convergence characteristics of the proposed method are investigated both theoretically and with simulations. The results indicates that, compared to other existing power control algorithms, the proposed algorithm has a higher convergence speed while reducing user’s transmit power. Introducing the practical version of the proposed algorithm, it is then compared with two existing algorithms including B-BPSC (FSPC) and MOTDPC. Simulation results show significant improvements in the convergence speed and average consumed power.
Saeed Rahmati, Hussein Eliasi,
Volume 6, Issue 1 (1-2020)
Abstract
This paper presents a robust model predictive control scheme for a class of discrete-time nonlinear systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive uncertain non-linear time-varying function which satisfies a quadratic constraint. Using the dual-mode MPC stability theory, a sufficient condition is constructed for synthesizing the MPC’s stabilizing components; i.e. the local terminal cost function and the corresponding terminal set. The proposed control approach is applied to a CSTR. Simulation results show that the proposed robust MPC scheme is quite effective and it has a remarkable performance.
Mr. Kazem Shokoohi-Mehr, Dr. Mohsen Farshad, Dr. Ramazan Havangi, Dr. Nasser Mehrshad,
Volume 7, Issue 2 (3-2021)
Abstract
Due to the inefficiency of Kalman filter-based methods for combining low-cost inertial navigation system data and global satellite navigation systems when satellite signals are outage, the use of artificial intelligence techniques in integrated architecture has become a common issue. Therefore, in this paper, while presenting an effective hybrid architecture, the generalized regression neural network is used to predict the required observations of the Kalman filter at the event of long-term outage of satellite signals. In the proposed model, for training the neural network, the velocities and positions of the inertial system are considered as inputs and also the velocities and positions of the global positioning system are considered as network outputs. This approach, while being practical and operational, has reduced computational time and increased the accuracy and speed of training and network estimation. The simulation results show that due to the simple yet robust structure of the proposed architecture and of course the selection of an efficient multi-input-multi-output neural network with the ability to detect the effective relationship between inputs and specified outputs and consequently correct errors related to speeds and situations, inertial navigation system can be used for real-time navigation, self-reliant, with high reliability and accuracy.
Simin Hosseinzadeh, Dr Ramazan Havangi,
Volume 10, Issue 1 (3-2023)
Abstract
Disturbance and uncertaities exist in industrial systems and greatly affect the performance and stability of these systems. The robotic manipulator is one the most widely used devices in the industry that is highly affected by various disturbances. Hence establishing a proper control algorithm to estimate and eliminate disturbances seems crucial. Since the robotic manipulator is a highly nonlinear system, we need to design a nonlinear disturbance observer. In this thesis a nonlinear disturbance observer is proposed to estimate the constant and oscillatory disturbances in the studied system. On the other hand, since proportional-derivative controllers (PD) are widely used in industrial systems, so in this thesis, a suitable proportional derivative controller will be designed. This controller is not capable of dealing with disturbances and uncertainties, so a new supervisory controller structure has been proposed to estimate disturbances and stabilize the system. The core of proposed controller uses a new sliding model controller. Finally, some comparisions with PD and super twisting sliding mode controllers have been performed in several cases and the numerical results show the advantages of the proposed controller.
Dr Ramazan Havangi,
Volume 10, Issue 2 (9-2023)
Abstract
Estimating the state of charge of lithium- ion batteries is of great importance not only for optimal energy management, but also for ensuring safe operation, preventing charging and discharging, and as a result reducing the life of the battery. However, this parameter cannot be measured directly from the battery terminals. Therefore, there is a need to estimate it. In this paper an improved auxiliary marginal particle filter is presented to estimate the state of charge of lithium-ion batteries. In the proposed method, unlike the particle filter, sampling is done on the marginal distribution and the sampling dimensions do not increase with the passage of time. In addition, genetic operators and M-H algorithm have been used in the proposed method to increase diversity among particles. The use of genetic operators and the M-H algorithm causes the resampled particles to asymptotically approximate the samples from the posterior probability density function of the true state and increases the compatibility. The performance of the proposed method for estimating the state of charge of the battery has been compared with the estimation of the state of charge based on the developed particle filter and traceless particle filter. The results show the effective performance of the proposed method in comparison with other methods. The proposed method to obtain the same estimation accuracy as the particle filter requires far fewer particles and the amount of calculations is low. The root mean square error in the proposed method with different particles is close to 0.007, while in other methods, the root mean square error increases with the decrease of particles.
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