International Journal of Industrial Electronics, Control and Optimization
Volume:2 Issue: 2, Spring 2019

Capital portfolio management is considered an important issue in the field of economics and its main subject is about the scientific management of combination choice of assets that meet the specific investment objectives. Maximizing returns and minimizing asset risk are the most important goals in the management of the portfolio of capital. This paper proposes two novel risk measures based on the MLP neural networks and prediction intervals (PI). The MLP based risk is constant and assumes that the uncertainty is uniform in the dataset. The second one is a time-varying risk measure that doesn’t assume uniformity condition. After introducing two novel risk measures, a new cost function is presented to consider the expected returns and the involving risk at the same time. Finally, the covariance matrix adaptation evolution strategy (CMA-ES) algorithm is used to obtain the optimal portfolio. The validity of the proposed selection process (including risk measures, cost function, and the optimization method) is tested using the dataset of the 18 shares of the Tehran Stock Exchange, and the results are compared with the obtained portfolio using the conditional value at risk (CVaR) criterion as a well-known benchmark.

In this paper buffer dynamic modeling for wireless sensor networks (WSNs) as a highly nonlinear system is accomplished in discrete time and the overall model is gained by blending subsystems obtained based on delay. Based on queue utilization and channel estimation algorithm, congestion is detected and a suitable rate is selected by an adaptive back-off interval selection. In this paper, a new approach is proposed for controller synthesis of our system based on non-quadratic Lyapunov functions, and a controller is designed to stabilize each subsystem. The controller synthesis results are expressed as a set of Linear Matrix Inequalities (LMIs). Moreover, the performance is considered and decay rate is guaranteed. Finally, a set of new LMI-based congestion control schemes (LCC) is obtained for WSNs. The closed-loop systems are globally asymptotically stable (GAS) in case of delay changes resulted from queue size changes. The simulation results using MATLAB and OPNET simulator confirm the effectiveness of our proposed schemes.

Random based inventive algorithms are being widely used for optimization. An important category of these algorithms comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed algorithm for optimizing the orientation game is a series of optimization algorithms that are formed with the idea of an old game and search operators are an arrangement of players. These players are displaced in a certain space, under the influence of the game referee's orders. The best position is achieved by the laws are there in this game .In this paper, the real version of the algorithm is presented. The results of optimization of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed algorithm over the genetic algorithm and the particle swarm optimization algorithm.

Double-objective optimization is a wide class of multi-objective optimization problems in different scientific and industrial applications. This paper proposes a method for the problem of constrained double-objective optimization that is called gravitational charged particles optimization (GChPO). The presented algorithm is based on the movement dynamics of charged particles in the electric field. The mass and electric charge of particles vary according to the value of the first and second objective function, respectively. Usually, in multi-objective optimization algorithms, the dominant and non-dominant solutions should be determined in every iteration, which increases the computation cost of the algorithm. In the proposed method, there is no need of determining the dominant and non-dominant solutions in every iteration that decreases the computation time of the algorithm, significantly. Performance of GChPO is evaluated by seven double-objective and four single-objective benchmark problems. The obtained results are compared with the recent multi-objective and well-known single-objective optimization algorithms that indicate not only the presented algorithm can find the Pareto solutions in the double-objective functions but also it performs better than other algorithms, generally.

In this paper, a sliding mode control of LLC resonant dc-dc converter in low voltage and high current application is presented. The proposed controller operates at two fixed switching frequencies to the battery charger of electric vehicles. In resonant converter, because of the soft switching process, switching loss is negligible and converters can be designed at high frequencies to reduce the size, weight and price. To analyze this convertor the harmonic approximation method with state space equation is used. Taking advantage of the approximations used in order to avoid uncertainty, sliding mode control method is used. The mentioned convertor with the proposed controlled is exploited within three switching frequency bands that are lower than resonant frequency, resonant frequency and above resonant frequency. Due to the robustness of the sliding mode control, the output voltage regulation occurs with minimum distortion under a wide variation of input voltage, output voltage and output current. As a result, the battery life increases. In order to verify the results and effectiveness of the proposed converters, the prototype of LLC resonant converters Simulation and experimental results for the input voltage 300-420 V dc, output voltage 36-72 V dc and current output (0-11 A) is presented. The prototype achieves a peak efficiency value of 96%.

DC–DC boost converters are unable to provide high step-up voltage gains due to the effect of power switches, rectifier diodes, and the equivalent series resistance of inductors and capacitors. A high step-up DC-DC converter based on the modified SEPIC converter is presented in this paper. Step up non-isolated converters generally suffer from problems such as high voltage stress and low efficiency. In this study, non-isolated boost converter structures, SEPIC, SEPIC modified circuit, and a proposed converter is studied. Then compare the performance of these typologies is located and presented as a chart. The operation analysis, design procedure for proposed converter is obtaied from 15V input voltage and 150V output voltage and with 100 watts output power. Using the proposed converter, the input inrush current and the invading voltage of the output have decreased. The time response analysis states that the proposed converter acts faster with deployment time than other converters.

the main challenge in associate islanded Micro grid (MG) is the frequency stability due to the inherent low-inertia feature of distributed energy resources. That is why, energy storage devices, are utilized in MGs as the promising sources for grid short-term frequency regulation. Though energy storage devices, improve the dynamic response of the load frequency control system, these devices increase system costs. Moreover, the modification or uncertainty of the system parameters will significantly degrade the performance of the conventional load-frequency control system. This article proposes the implementation of rotating-mass-based virtual inertia in Double-Fed Induction Generator (DFIG) to support the primary frequency control associated an adaptive Neuro-Fuzzy Inference System (ANFIS) controller, as the secondary frequency control. The simulation results illustrate that the suggested control scheme ameliorate the dynamic response and performance of the load frequency control system and also the studied islanded MG remains stable, despite severe load variation and parametric uncertainties.

In this paper, a novel centralized controller is presented to control multi-input multi-output industrial processes with heavy interactions and significant time-delays. The system model equations are represented in a non-minimal stochastic state-space form. Also, the state and measurement equations respectively use smoothed random walk model and finite impulse response model of the plant. To design the controller, a quadratic cost function is considered. A standard Kalman filter algorithm is used to estimate the state vector of the controller and solve to the discrete algebraic Riccati equation simultaneously. By using the smoothing parameter, the controller behavior can be changed between the Kalman filter random walk controller and the Kalman filter integrated random walk controller. To evaluate the effect of the smoothing parameter the proposed controller is first applied to a single input single output system. Then an industrial-scale polymerization reactor which has the two-input and two-output system is used to investigate the performance of the designed controller. The simulation results indicate that the controller has a good performance in tracking the set point and robust due to changing the system parameters.