A hybrid particle swarm optimization and bacterial foraging for power system stability enhancement

A novel hybrid approach involving particle swarm optimization (PSO) and bacterial foraging optimization algorithm (BFOA) called bacterial swarm optimization (BSO) is illustrated for designing static var compensator (SVC) in a multimachine power system. In BSO, the search directions of tumble behavior for each bacterium are oriented by the individual's best location and the global best location of PSO. The proposed hybrid algorithm has been extensively compared with the original BFOA algorithm and the PSO algorithm. Simulation results have shown the validity of the proposed BSO in tuning SVC compared with BFOA and PSO. Moreover, the results are presented to demonstrate the effectiveness of the proposed controller to improve the power system stability over a wide range of loading conditions. © 2014 Wiley Periodicals, Inc. Complexity 21: 245–255, 2015     

Optimal location and optimized parameters for robust power system stabilizer using honeybee mating optimization

A honeybee mating optimization technique is used to tune the power system stabilizer (PSS) parameters and find optimal location of PSSs in this article. The PSS parameters and placement are computed to assure maximum damping performance under different operating conditions. One of the main advantages of the proposed approach is its robustness to the initial parameter settings. The effectiveness of the proposed method is demonstrated on two case studies as; 10‐machine 39‐buses New England (NE) power system in comparison with Tabu Search (TS) and 16 machines and 68 buses‐modified reduced order model of the NE New York interconnected system by genetic algorithm through some performance indices under different operating condition. The proposed method of tuning the PSS is an attractive alternative to conventional fixed gain stabilizer design as it retains the simplicity of the conventional PSS and at the same time guarantees a robust acceptable performance over a wide range of operating and system condition. © 2014 Wiley Periodicals, Inc. Complexity 21: 242–258, 2015     

A new hybrid algorithm based on optimal fuzzy controller in multimachine power system

In this article, a new methodology based on fuzzy proportional‐integral‐derivative (PID) controller is proposed to damp low frequency oscillation in multimachine power system where the parameters of proposed controller are optimized offline automatically by hybrid genetic algorithm (GA) and particle swarm optimization (PSO) techniques. This newly proposed method is more efficient because it cope with oscillations and different operating points. In this strategy, the controller is tuned online from the knowledge base and fuzzy interference. In the proposed method, for achieving the desired level of robust performance exact tuning of rule base and membership functions (MF) are very important. The motivation for using the GA and PSO as a hybrid method are to reduce fuzzy effort and take large parametric uncertainties in to account. This newly developed control strategy mixed the advantage of GA and PSO techniques to optimally tune the rule base and MF parameters of fuzzy controller that leads to a flexible controller with simple structure while is easy to implement. The proposed method is tested on three machine nine buses and 16 machine power systems with different operating conditions in present of disturbance and nonlinearity. The effectiveness of proposed controller is compared with robust PSS that tune using PSO and the fuzzy controller which is optimized rule base by GA through figure of demerit and integral of the time multiplied absolute value of the error performance indices. The results evaluation shows that the proposed method achieves good robust performance for a wide range of load change in the presents of disturbance and system nonlinearities and is superior to the other controllers. © 2014 Wiley Periodicals, Inc. Complexity 21: 78–93, 2015     

PI controller design using artificial bee colony algorithm for MPPT of photovoltaic system supplied DC motor‐pump load

Maximum Power Point Tracking (MPPT) is used in Photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV‐DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by Artificial Bee Colony (ABC) to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with Genetic Algorithm for various disturbances to prove its robustness. © 2015 Wiley Periodicals, Inc. Complexity 21: 99–111, 2016     

Close formation flight of swarm unmanned aerial vehicles via metric-distance brain storm optimization

Close formation flight of swarm unmanned aerial vehicles (UAVs) has drawn much attention from scholars due to its significant importance in many aspects. In this paper, we focus on an advanced controller design for swarm UAV close formation based on a novel bio-inspired algorithm, i.e., metric-distance brain storm optimization (MDBSO). The proposed method utilizes the brain storm optimization (BSO) which has been extensively adopted in complicated systems with great performances and modifies its basic operators to formulate the formation flight controller design. The original clustering operator in BSO is replaced by a fresh clustering method based on metric distances, while the individual updating operator utilizes Lévy distribution to extend search steps to fit into the metric searching regions. Then the proposed algorithm is applied to optimize the benchmark controller in swarm UAV close formation to enhance the tracking performances under complicated circumstances. Simulation results demonstrate that our approach is more superior in stable configuration of swarm UAV close formations by comparing with several generic methods.     

Reliable gain‐scheduled control design for networked control systems

In this article, the problem of reliable gain‐scheduled H_∞ performance optimization and controller design for a class of discrete‐time networked control system (NCS) is discussed. The main aim of this work is to design a gain‐scheduled controller, which consists of not only the constant parameters but also the time‐varying parameter such that NCS is asymptotically stable. In particular, the proposed gain‐scheduled controller is not only based on fixed gains but also the measured time‐varying parameter. Further, the result is extended to obtain a robust reliable gain‐scheduled H_∞ control by considering both unknown disturbances and linear fractional transformation parametric uncertainties in the system model. By constructing a parameter‐dependent Lyapunov–Krasovskii functional, a new set of sufficient conditions are obtained in terms of linear matrix inequalities (LMIs). The existence conditions for controllers are formulated in the form of LMIs, and the controller design is cast into a convex optimization problem subject to LMI constraints. Finally, a numerical example based on a station‐keeping satellite system is given to demonstrate the effectiveness and applicability of the proposed reliable control law. © 2014 Wiley Periodicals, Inc. Complexity 21: 214–228, 2015     

Fuzzy guaranteed cost controller for trajectory tracking in nonlinear systems

In this paper, we propose a fuzzy logic based guaranteed cost controller for trajectory tracking in nonlinear systems. Takagi–Sugeno (T–S) fuzzy model is used to represent the dynamics of a nonlinear system and the controller design is carried out using this fuzzy model. State feedback law is used for building the fuzzy controller whose performance is evaluated using a quadratic cost function. For designing the fuzzy logic based controller which satisfies guaranteed performance, linear matrix inequality (LMI) approach is used. Sufficient conditions are derived in terms of matrix inequalities for minimizing the performance function of the controller. The performance function minimization problem with polynomial matrix inequalities is then transformed into a problem of minimizing a convex performance function involving standard LMIs. This minimization problem can be solved easily and efficiently using the LMI optimization techniques. Our controller design method also ensures that the closed-loop system is asymptotically stable. Simulation study is carried out on a two-link robotic manipulator tracking a reference trajectory. From the results of the simulation study, it is observed that our proposed controller tracks the reference trajectory closely while maintaining a guaranteed minimum cost.     

A novel bee swarm optimization algorithm for numerical function optimization

The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSO–RP and BSO–RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.     

Multimachine power system stabilizer based on optimal multistage fuzzy PID attendant honey bee mating optimization

This article presents a new strategy based on multistage fuzzy PID controller for damping power system stabilizer in multimachine environment using Honey Bee Mating Optimization (HBMO). The proposed technique is a new metaheuristic algorithm which is inspired by mating procedure of the honey bee. Actually, the mentioned algorithm is used recently in power systems which demonstrate the good reflex of this algorithm. Also, finding the parameters of PID controller in power system has direct effect for damping oscillation. Hence, to reduce the design effort and find a better fuzzy system control, the parameters of proposed controller is obtained by HBMO that leads to design controller with simple structure that is easy to implement. The effectiveness of the proposed technique is applied to single machine connected to infinite bus and IEEE 3–9 bus power system. The proposed technique is compared with other techniques through integral of the time multiplied absolute value of the error and figure of demerit. © 2015 Wiley Periodicals, Inc. Complexity 21: 234–245, 2016     

Global optimization for the Biaffine Matrix Inequality problem

It has recently been shown that an extremely wide array of robust controller design problems may be reduced to the problem of finding a feasible point under a Biaffine Matrix Inequality (BMI) constraint. The BMI feasibility problem is the bilinear version of the Linear (Affine) Matrix Inequality (LMI) feasibility problem, and may also be viewed as a bilinear extension to the Semidefinite Programming (SDP) problem. The BMI problem may be approached as a biconvex global optimization problem of minimizing the maximum eigenvalue of a biaffine combination of symmetric matrices. This paper presents a branch and bound global optimization algorithm for the BMI. A simple numerical example is included. The robust control problem, i.e., the synthesis of a controller for a dynamic physical system which guarantees stability and performance in the face of significant modelling error and worst-case disturbance inputs, is frequently encountered in a variety of complex engineering applications including the design of aircraft, satellites, chemical plants, and other precision positioning and tracking systems.     

CBSO: a memetic brain storm optimization with chaotic local search

Brain storm optimization (BSO) is a newly proposed optimization algorithm inspired by human being brainstorming process. After its appearance, much attention has been paid on and many attempts to improve its performance have been made. The search ability of BSO has been enhanced, but it still suffers from sticking into stagnation during exploitation phase. This paper proposes a novel method which incorporates BSO with chaotic local search (CLS) with the purpose of alleviating this situation. Chaos has properties of randomicity and ergodicity. These properties ensure CLS can explore every state of the search space if the search time duration is long enough. The incorporation of CLS can make BSO break the stagnation and keep the population’s diversity simultaneously, thus realizing a better balance between exploration and exploitation. Twelve chaotic maps are randomly selected for increasing the diversity of the search mechanism. Experimental and statistical results based on 25 benchmark functions demonstrate the superiority of the proposed method.     

基于非线性规划的社会系统协调发展优化模型及其应用

社会系统协调发展优化问题是优化理论运用较少的领域.基于非线性规划方法,依据社会系统协调发展内涵,对社会系统协调发展目标函数做了具体改进,构建了社会系统协调发展优化模型,并根据协调发展类型分为超前型优化模型与滞后型优化模型.这两种优化模型的区别主要是约束条件的不同.依据优化模型的优化解,可以为某地区社会系统的协调发展提供清晰的调节与控制路径.最后利用上述方法对徐州地区物流基础系统、经济基础系统的协调发展进行了具体优化与调控.     

离散非线性时滞系统的鲁棒耗散控制

针对一类基于T-S模型表示的具有范数有界不确定性离散非线性时滞系统,研究了鲁棒耗散模糊控制问题.对可用T-S模糊模型表示的非线性时滞系统,考虑系统具有范数有界参数不确定性时,应用并行分布式控制方法,得到使得系统稳定且严格耗散的模糊耗散控制器存在的充分性条件.进而通过建立和求解LMI(线性矩阵不等式)约束的凸优化问题,给出了耗散控制律的设计方法.数值算例表明了此方法的可行性和有效性.     

Optimization of Fuzzy Controller for an Uninhabited Airplane Using Genetic Algorithms

The design of a fuzzy logic based controller for an uninhabited airplane using genetic algorithms for parameter optimization is illustrated. The airvehicle mission requires that a prescribed trajectory be followed with a satisfactory accuracy. Fuzzy control modules are present in each of the four control channels. Inputs are position and velocity errors. The parameters of the fuzzy controller are: trapezoidal membership functions, five linguistic values, and height defuzzification method associated with peak value. The scaling factors of the fuzzy controller are optimized by means of a genetic algorithm such that, a performance index, based on errors from a stationary flight path, is minimized. The genetic algorithm is based on binary genetic representation, an elitist roulette wheel selection technique and two genetic operators: mutation and crossover. The performance of the resulting optimal fuzzy controller is assessed through numerical simulation.     

Efficient Computation of Optimal Controls for the Exchange Processes During the Dialysis Therapy

In order to reduce the frequency of acute complications during the dialysis therapy the exchange processes of water and different solutes within the patient as well as across the dialyzer membrane shall be optimally controlled. With regard to a clinical application, this task requires the efficient treatment of a large-scale control problem, formulated in terms of a dynamical optimization problem. Equality and inequality conditions are given by the system describing the exchange processes and by the consideration of technical and medical constraints, respectively. Above all the complexity of the describing system prevents the application of standard optimization techniques as well as the construction of closed loop control laws and implies the construction of a control procedure which is specially adapted to the problem. The presented optimization method—denoted as controller PSEUDYGALG—represents a numerical iterative descent procedure, based on the approach of admissible direction. The procedure assumes an appropriate parameterization of the control problem as well as the availability of information about the input-output structure of the underlying describing system. In order to achieve the required efficiency, adaptive penalization strategies for the performance criterion and update modules for the descent information of each iterative step are presented. The controller allows both the treatment of badly and well conditioned control problems which are characterized by the occurrence and the absence of contradictional requirements for the performance criterion, respectively. PSEUDYGALG represents an off-line control method, but due to the achieved efficiency an on-line deployment by receding horizon approaches is in principle possible. Even though the controller has been developed for the dialysis problem it can be applied to a wide range of comparable control problems if the two assumptions—appropriate parameterization and knowledge about the input-output structure of the underlying system—are met.     

Chaotic artificial immune approach applied to economic dispatch of electric energy using thermal units

The economic dispatch problem (EDP) is an optimization problem useful in power systems operation. The objective of the EDP of electric power generation, whose characteristics are complex and highly non-linear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying system constraints. Recently, as an alternative to the conventional mathematical approaches, modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. As special mechanism to avoid being trapped in local minimum, the ergodicity property of chaotic sequences has been used as optimization technique in EDPs. Based on the chaos theory, this paper discusses the design and validation of an optimization procedure based on a chaotic artificial immune network approach based on Zaslavsky’s map. The optimization approach based on chaotic artificial immune network is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. Simulation results and comparisons show that the chaotic artificial immune network approach is competitive in performance with other optimization approaches presented in literature and is also an attractive tool to be used on applications in the power systems field.     

A novel of fuzzy PSS based on new objective function in multimachine power system

This article proposed a new control strategy based on Takagi–Sugeno fuzzy model for deceasing the power system oscillation. This controller is based on the parallel distributed compensation structure, the stability of the whole closed‐loop model is provided using a general Lyapunov‐Krasovski functional. Also, in this article, a new objective function has been considered to test the proposed Fuzzy Power System Stabilizer in different load conditions which increase the system damping after the system undergoes a disturbance. So, for testing the effectiveness of the proposed controller, the damping factor, damping ratio, and a combination of the damping factor and damping ratio were analyzed and compared with the proposed objective function. The effectiveness of the proposed strategy has been used over 16 machine 68 bus power system. The eigenvalue analysis and nonlinear time domain simulation results proof the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Complexity 21: 288–298, 2016     

Integrated design and optimization of a complex nonlinear hybrid electric powertrain including simulations in time domain

This paper deals with the design and optimization of hybrid electric powertrains. Therefore basic relations of the behavior of hybrid electric powertrain systems and the controller design are introduced. Based on models of typical hybrid electric system components principal optimization approaches with respect to performance parameters like efficiency, availability, lifetime, etc. are shown. Hereby an optimization algorithm based on a global optimization technique is applied. Using the example of a fuel cell based hybrid electric powertrain system the approaches are introduced and compared to each using time-domain simulations integrated in optimization algorithms. The results show that both approaches are appropriate to design the system as well as the controllers. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Dynamic output guaranteed cost controller for neutral systems with input delay

In this paper, we consider a design problem of dynamic output guaranteed cost controller (GCC) of a class of neutral systems with input delay. A quadratic cost function is considered as a performance measure for the closed-loop system. Based on the Lyapunov second method, two stability criteria for existence of the controller are derived in terms of matrix inequalities. The solutions of the matrix inequalities can be easily obtained using existing efficient convex optimization techniques. A numerical example is given to illustrate the proposed design method.     

Hybrid design of PID controller for four DoF lower limb exoskeleton

In this paper, a method of tuning a proportional-integral-derivative controller for a four degree-of-freedom lower limb exoskeleton using hybrid of genetic algorithm and particle swarm optimization is presented. Transfer function of each link of the lower limb exoskeleton acquired from a pendulum model, was used in a closed-loop proportional-integral-derivative control system, while each link was assumed as one degree-of-freedom linkage. In the control system, the hybrid algorithm was applied to acquire the parameters of the controller for each joint for minimizing the error. The algorithm started with genetic algorithm and continued via particle swarm optimization. Furthermore, a 3-dimensional model of the lower limb exoskeleton was simulated to validate the proposed controller. The trajectory of the control system with optimized proportional-integral-derivative controller via hybrid precisely follows the input signal of the desired. The result of the hybrid optimized controller was compared with genetic algorithm and particle swarm optimization based on statistics. The average error of the proposed algorithm showed the optimized results in comparison with genetic algorithm and particle swarm optimization. Furthermore, the advantages of the hybrid algorithm have been indicated by numerical analysis.