Keywords: Optimization, Modeling of complex systems
Abstract: This paper presents a new four-view-based framework to survey optimization approaches and the pioneering studies of optimization. The new four-view-based framework allows us to mainly understand optimization. Moreover, every proposed view explores a specific fundamental aspect of optimization. Therefore, classifying, organizing and comparing proposed works is much easier. The proposed four-view-based framework allow us to depict the lacks of the proposed frameworks and tools. Moreover, it allows us to raise many challenges to develop our new tool for combinatorial optimization problem with optimization property.

Keywords: Optimization, Power systems
Abstract: The conventional economic dispatch (ED) cannot satisfy the environmental protection requirements, since it only considers minimizing the total fuel cost. The gaseous pollutants emitted by the power stations cause harmful effects with the human beings and the environment like the sulphur dioxide (SO2), nitrogen oxide (NOx) and the carbon dioxide (CO2), etc. Thus, the optimization of production cost should not be the only objective but the reduction of emission must also be taken into account. The limiting levels of emissions over a schedule horizon represent additional operational constraints that are to be satisfied when finding the optimal solution for the economic dispatch problem. The characteristics of emissions of different pollutants are different and are usually highly non-linear. This increases the complexity and non-monotony city of the emission constrained economic dispatch problem This paper presents an application of quadratic programming (QP) for solving dynamic economic load dispatch (DELD) problem with minimum gas emission. This problem determines optimum power generation schedule while minimizing gas emission. The proposed QP method takes care of different unit and system constraints to find optimal solution. To validate the effectiveness and the feasibility of the approach, it has been examined on 2 different standard test cases(i) a 3-unit and (ii) a 10-unit test system. Simulation results obtained are also compared with other reported methodology. The comparison confirms the superiority, fast convergence and proficiency of the algorithm.

Keywords: Optimization, Signal processing
Abstract: This paper proposes an application of the simulated annealing to optimize the detection threshold in an ordered statistics constant false alarm rate (OS-CFAR) system. Using conventional optimization methods, such as the conjugate gradient, can lead to a local optimum and lose the global optimum. Also for a system with a number of sensors that is greater than or equal to three, it is difficult or impossible to find this optimum; Hence, the need to use other methods, such as meta-heuristics. From a variety of meta-heuristic techniques, we can find the Simulated Annealing (SA) method, inspired from a process used in metallurgy. This technique is based on the selection of an initial solution and the generation of a near solution randomly, in order to improve the criterion to optimize. In this work, two parameters will be subject to such optimisation and which are the statistical order (k) and the scaling factor (t). Two fusion rules; “AND” and “OR” were considered in the case where the signals are independent from sensor to sensor. The results showed that the application of the proposed method to the problem of optimisation in a distributed system is efficiency to resolve such problems. The advantage of this method is that it allows to browse the entire solutions space and to avoid theoretically the stagnation of the optimization process in an area of local minimum.

Keywords: Power systems, Optimization
Abstract: This paper presents a multi-objective Simulated Annealing Optimization to solve a Dynamic Generation Dispatch problem. In this work, the problem is formulated as a multi-objective one with two competing functions, namely economic cost and emission functions, subject to different constraints. The inequality constraints considered are the generating unit capacity limits while the equality constraint is generation-demand balance. To show the advantages of the proposed algorithm, it has been applied for solving multi-objective EELD problems in a 3-generator system with NOx and SO2 emission, and in a 6-generators system considering NOx, SO2, and CO2 emission. This technique is compared with other techniques which reveals the superiority of the proposed approach and confirms its potential for solving other power systems problems.

Keywords: Robotics, Optimization, Modeling of complex systems
Abstract: Further progress in the development, innovation and optimization of microrobot-based applications leads to the requirement of new modeling tools and hypothesis, for enhancing the performances and the computational time of microrobots simulators. Recently, the evolutionary techniques, artificial bee colony (ABC), genetic algorithms (GA) and particle swarm optimization (PSO), have drawn great attention among several heuristic optimization techniques. The aim of this work is to compare the accuracy and computational efficiency of ABC, GA and PSO evolutionary techniques for optimizes and improves the performances of the microrobot design. Moreover, the performance of all optimization methods in terms of convergence rate and computational time is presented and compared. Besides, our obtained results for all techniques (PSO, GA and ABC) are presented and compared with design without optimization to demonstrate the accuracy of our simulations, where a good improvement of the electromechanical performances of the design has been founded by the entire techniques.

Keywords: Control algorithms implementation, Embedded Systems, Fuzzy and neural systems
Abstract: the performances of drive in the case of variable reluctance linear actuators depend on the machine, the topology of the power converter and the kind of excitation sequence. The windings of a variable reluctance machine represent highly inductive circuits. The supply of the windings permits to inject them a given current in a given position. This current must be promptly established to obtain the significant effort that maintains the machine to the desired value during normal operating conditions. In addition, this current must be extinguished quickly when switching is chained up. In this article, we consider a four-phase variable reluctance linear step by step actuator to apply a developed PWM technique to determine the switching instants of the power stage transistor.

Keywords: Control applications, Linear and nonlinear systems, Modeling and simulation
Abstract: In this paper, a linear robust controller is proposed for systems comprising a hysteresis operator preceding a linear stable system. The hysteresis operator is modeled with piecewise linear characteristics with uncertainties, and a nominal inverse operator is included to mitigate the hysteresis effect. An H_infinity control design is proposed to handle the remaining uncertainties in a two-degree-of freedom (2DOF) framework.

Usually, the H_infinity control is designed for a nominal plant while hysteresis is treated as uncertainty. The hysteresis nonlinearity is not modeled or inverted in those methods. In our work we reduce the effect of hysteresis by inversion and only the remaining inversion error determines the size of the uncertainty. We compared the proposed H_infinity method with a PI controller when 2DOF is applied to both system. The PI controller usually has good performance at low frequencies, however, it does not take advantage of knowing the dynamics of the system as in H_infinity design, where we can improve the performance at high frequency. Simulation results on a model of piezoelectric actuator-based nanopositioner are presented to illustrate the design and analysis, where the hysteresis nonlinearity is represented by a Prandtal-Ishlinskii operato

Keywords: Control applications, Power systems, Transportation systems
Abstract: The hybridization of batteries and super-capacitors (SCs) is prevalent in the study of electrical powered vehicles. SCs, having high specific power, are able to afford large amount of energy in a short period, and thus have been used to satisfy transient load power demands and capture the braking power. In this paper, the SC, also able to afford large number of charge/discharge cycles, is used to absorb persistent sinusoidal disturbances in hybrid electric vehicles. The disturbances stem from the engine torque ripples compensation by the electric machine, which is elaborated in the paper. The disturbances lead to oscillations in the DC bus voltage and in the battery current. The objective is then to attenuate the oscillations by absorbing the disturbances in the SC. Passivity-based control method and error output regulation method for nonlinear systems are exploited to control the DC-DC converters.

Keywords: Control of telecommunications systems, Modeling and simulation, Signal processing
Abstract: In a wireless communication channel, there can be more than one way in which the signal can travel between the transmitter and the receiver. The presence of multipath components is due to reflection, refraction and / or because of other interfering reflections which limit error performance digital channels objects. The M-ary Phase Shift Keying and M-ary Quadrature Amplitude Modulation are used in wireless communication to compare the performance of fading channels. In this paper, we evaluate the digital modulations (MPSK, MQAM) in terms of bit error rate (BER) based on the fading channels (Rayleigh and Rice) and Additive White Gaussian Noise (AWGN). Index Terms—Fading; AWGN; Rayleigh; Rician; MPSK; MQAM.

Keywords: Control applications, Optimization, Linear and nonlinear systems
Abstract: This paper describes the development of a method to optimally tune constrained MPC algorithms for a nonlinear process. The T-S model is firstly established for nonlinear systems and its sequence parameters of fuzzy rules are identified by local recursive least square method. The proposed method is obtained by minimizing performance criteria in the worst-case conditions to control the process system, thus assuring robustness to the set of optimum tuning parameters. The resulting constrained mixed-integer nonlinear optimization problem is solved on the basis of a version of the particle swarm optimization technique. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results.