多智能体粒子群优化的SVR 模型预测控制

参数的优化选择对支持向量回归机的预测精度和泛化能力影响显著,鉴于此,提出一种多智能体粒子群算法(MAPSO)寻优其参数的方法,并建立MAPSO支持向量回归模型,用于非线性系统的模型预测控制,推导出最优控制率.采用该算法对非线性系统进行仿真,并与基于粒子群算法、基于遗传算法优化支持向量回归机的模型预测控制方法和RBF神经网络的预测控制方法进行比较,结果表明,所提出的算法具有更好的控制性能,可以有效应用于非线性系统控制中.     

Speed control of SRM supplied by photovoltaic system via ant colony optimization algorithm

This paper proposes a speed control of switched reluctance motor supplied by photovoltaic system. The proposed design of the speed controller is formulated as an optimization problem. Ant colony optimization (ACO) algorithm is employed to search for the optimal proportional integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of genetic algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.

     

基于控制角度的无线传感器网络时钟同步优化算法

针对无线传感器网络(WSN)时间同步过程中易受干扰,易发生通信延迟所造成的同步精度不高、收敛速度不快的问题,从控制的角度提出一种时钟同步优化算法。该算法首先建立时钟同步状态模型,然后通过现代控制理论的思想,引入中心控制策略,建立基于控制的时间同步状态模型。该控制策略是通过全局的时钟状态信息进行设计,在卡尔曼滤波最优估计前提下,使控制满足使性能指标函数最小的条件下,得到最优控制。仿真结果表明,所提出的时钟同步优化算法和无线传感器时钟同步协议(TPSN)相比,从第6步时钟同步开始,前者的同步误差逐渐比后者的同步误差小;在实现同一较高精度的同步需求时,前者需要的同步次数是后者所用的同步次数的20%左右;由时钟同步误差收敛均值的方差对比值显示,前者比后者的同步误差均方差小了两个数量级,因此所提出的时钟同步优化算法比时钟同步协议同步精度高、收敛速度快、网络通信负荷低。     

Adaptive synchronization control design for flexible telerobotics with actuator fault and input saturation

This paper addresses the synchronization control problem of flexible telerobotics with actuator fault, input saturation, and asymmetrical time‐varying delays. A new adaptive antisaturation nonlinear fractional power proportional+damping fault‐tolerant control scheme is designed. With the new control algorithm, faster convergence rate and higher convergence precision can be guaranteed, as compared with the general proportion+damping control method. By choosing Lyapunov‐Krasovskii functional, it shows that the teleoperation system is stable under specific linear matrix inequality conditions. Thus, the allowable maximal transmission delay can be computed with the given controller‐design parameters. To validate the effectiveness of the proposed method, simulations on synchronization control system composed of 2 manipulators (master is rigid, and slave is flexible) are developed. Experiments on the PHANToM Premium 1.5A manipulators are also conducted and numerous experimental results are presented to show the superior performance of the proposed control scheme.     

A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel

Non-conventional machining processes always suffer due to their low productivity and high cost. However, a suitable machining process should improve its productivity without compromising product quality. This implies the necessity to use efficient multi-objective optimization algorithm in non-conventional machining processes. In this present paper, an effective standard deviation based multi-objective fire-fly algorithm is proposed to predict various process parameters for maximum productivity (without affecting product quality) during WEDM of Indian RAFM steel. The process parameters of WEDM considered for this study are: pulse current (I), pulse-on time (T _on), pulse-off time (T _off) and wire tension (WT).While, cutting speed (CS) and surface roughness (SR) were considered as machining performance parameters. Mathematical models relating the process and response parameters had been developed by linear regression analysis and standard deviation method was used to convert this multi objective into single objective by unifying the responses. The model was then implemented in firefly algorithm in order to optimize the process parameters. The computational results depict that the proposed method is well capable of giving optimal results in WEDM process and is fairly superior to the two most popular evolutionary algorithms (particle swarm optimization algorithm and differential evolution algorithm) available in the literature.

     

Modeling and optimization of cold extrusion process by using response surface methodology and metaheuristic approaches

Obtaining the optimal extrusion process parameters by integration of optimization techniques was crucial and continuous engineering task in which it attempted to minimize the tool load. The tool load should be minimized as higher extrusion forces required greater capacity and energy. It may lead to increase the chance of part defects, die wear and die breakage. Besides, optimization may help to save the time and cost of producing the final product, in addition to produce better formability of work material and better quality of the finishing product. In this regard, this study aimed to determine the optimal extrusion process parameters. The minimization of punch load was the main concern, in such a way that the structurally sound product at minimum load can be achieved. Minimization of punch load during the extrusion process was first formulated as a nonlinear programming model using response surface methodology in this study. The established extrusion force model was then taken as the fitness function. Subsequently, the analytical approach and metaheuristic algorithms, specifically the particle swarm optimization, cuckoo search algorithm (CSA) and flower pollination algorithm, were applied to optimize the extrusion process parameters. Performance assessment demonstrated the promising results of all presented techniques in minimizing the tool loading. The CSA, however, gave more persistent optimization results, which was validated through statistical analysis.

     

Time-optimal and Smooth Trajectory Planning for Robot Manipulators

This paper presents a practical time-optimal and smooth trajectory planning algorithm and then applies it to robot manipulators. The proposed algorithm uses the time-optimal theory based on the dynamics model to plan the robot’s motion trajectory, constructs the trajectory optimization model under the constraints of the geometric path and joint torque, and dynamically selects the optimal trajectory parameters during the solving process to prominently improve the robot’s motion speed. Moreover, the proposed algorithm utilizes the input shaping algorithm instead of the jerk constraint in the trajectory optimization model to achieve a smooth trajectory. The input shaping of trajectory parameters during postprocessing not only suppresses the residual vibration of the robot but also takes the signal delay caused by traditional input shaping into account. The combination of these algorithms makes the proposed time-optimal and smooth trajectory planning algorithm ensure absolute time optimality and achieve a smooth trajectory. The results of an experiment on a six-degree-of-freedom industrial robot indicate the validity of the proposed algorithm.

     

一种改进的鲁棒多目标优化方法

针对在解决某些复杂多目标优化问题过程中,所得到的Pareto最优解易受设计参数或环境参数扰动的影响,引入了鲁棒的概念并提出一种改进的鲁棒多目标优化方法,它利用了经典的基于适应度函数期望和方差方法各自的优势,有效地将两种方法结合在一起。为了实现该方法,给出一种基于粒子群优化算法的多目标优化算法。仿真实例结果表明,所给出的方法能够得到更为鲁棒的Pareto最优解。     

存在未知时延和Markov 丢包的网络控制系统故障检测与优化

将网络控制系统(NCSs) 的未知短时延处理成范数有界不确定性, 结合Markov 丢包影响将NCSs 建模为不确定Markov 跳变系统, 设计模态依赖的鲁棒故障检测滤波器. 为了提高检测系统性能, 采用后置滤波器对残差信号进行时域优化, 并以Moore-Penrose 逆形式给出其最优解. 同时, 设计自适应检测阈值, 并给出时变参数阵的迭代方法,降低了计算量. 数值仿真表明, 所提出的方法能够有效地抑制时延和丢包影响, 提高故障检测系统的检测能力和检测速度.

     

双线性时滞系统的最优控制及其在精馏塔的应用

本文利用块脉冲函数(BPFs)和参数优化方法,提出了解决具有任意状态时滞、控制时滞及其任意初始条件的多变量双线性系统最优控制问题的具体算法,该算法被成功地用于一类可用双线性时滞模型描述的精馏塔的温度最优控制,同时,通过其它例子仿真,也表明这个算法是令人满意的。     

Optimal design of fractional order PID controller for time-delay systems: an IWLQR technique

ABSTRACT

In this paper, an optimal design based state feedback gain of fractional order proportional integral derivative (PID) controller for time delay system is proposed. The proposed optimal design is called as IWLQR, which will be the joined execution of both the invasive weed optimization (IWO) and linear quadratic regulator (LQR). The proposed technique modifies a fractional order proportional integral derivative (FOPID) regulator among a high order time delay scheme that achieves an elevated performance for a wide area. In the proposed methodology, the gain of the FOPID controller is tuned to achieve the desired responses which are determined using the LQR theory and the weight matrices of the LQR is anticipated with the assistance of IWO technique. The uniqueness of the projected technique is to reduce the fault in a PID regulator among the higher order time delay scheme by the aid of the increase limits of the regulator. The objective of the proposed control method is chosen in view of the set point parameters and the accomplished parameters from the time delay system. The projected method is employed to achieve the avoidance of high order time delay and the dependability restrictions such as tiny overrun, resolving time and fixed condition defect. This technique is carried out in MATLAB/Simulink platform and the results are separated by the earlier regulator junction representation like Z-N system, Wang technique, curve fitting technique, regression technique which illustrates the superior presentation of the anticipated abstaining in the existing work.     

Recursive least-squares temporal difference learning for adaptive traffic signal control at intersection

This paper presents a new method to solve the scheduling problem of adaptive traffic signal control at intersection. The method involves recursive least-squares temporal difference (RLS-TD(λ)) learning that is integrated into approximate dynamic programming. The learning mechanism of RLS-TD(λ) is to make an adaptation of linear function approximation by updating its parameters based on environmental feedback. This study investigates the method implementation after modeling a traffic dynamic system at intersection in discrete time. In the model, different traffic control schemes regarding signal phase sequence are considered, especially the defined adaptive phase sequence (APS). By simulating traffic scenarios, RLS-TD(λ) is superior to TD(λ) for updating functional parameters in the approximation, and APS outperforms other conventional control schemes on reducing traffic delay. By comparing with other traffic signal control algorithms, the proposed algorithm yields satisfying results in terms of traffic delay and computation time.

     

On tuning and complexity of an adaptive model predictive control scheduler

In this paper, adaptive model predictive control is applied to schedule differentiated buffers in routers. The proposed algorithm, adaptive model predictive control scheduler (AMPCS), dynamically regulates the service rates of aggregated traffic classes. This algorithm guarantees some required constraints on proportional or absolute delay. The control parameters and the way they are adjusted as well as the problems of implementing the controller at high data rates are investigated. Theoretical analysis and numerical simulations demonstrate stability of AMPCS and its acceptable quality of service differentiations at core routers while maintaining end to end delay constraints.     

一个无线传感网络时间同步模拟器

Time synchronization is a critical middleware service of wireless sensor networks. Researchers have already proposed some time synchronization algorithms. However, due to the demands for various synchronization precision, existing time synchronization algorithms often need to be adapted. So it is necessary to evaluate these adapted algorithms before use. Software simulation is a valid and quick way to do it. In this paper, we present a time synchronization simulator, Simsync, for wireless sensor networks. We decompose the packet delay into 6 delay components and model them separately. The frequency of crystal oscillator is modeled as Gaussian. To testify its effectiveness, we simulate the reference broadcast synchronization algorithm (RBS) and the timing-sync synchronization algorithm (TPSN) on Simsync. Simulated results are also presented and analyzed.     

Comparison of genetic programming and radial basis function neural network for open-channel junction velocity field prediction

In this paper, a new fuzzy adaptive artificial physics optimization (FAAPO) algorithm is used to solve security-constrained optimal power flow (SCOPF) problem with wind and thermal power generators. The stochastic nature of wind speed is modeled as a Weibull probability density function. The production cost is modeled with the overestimation and underestimation of available wind energy and included in the conventional SCOPF. Wind generation cost model comprises two components, viz. reserve capacity cost for wind power surplus and penalty cost for wind power shortage. The selection of optimal gravitational constant (G) is a tedious process in conventional artificial physics optimization (APO) method. To overcome this limitation, the gravitational constant (G) is fuzzified in this work. Therefore, based upon the requirement, the gravitational constant changes adaptively. Hence, production cost is reduced, settles at optimum point and takes less number of iterations. The proposed algorithm is tested on IEEE 30-bus system and Indian 75-bus practical system, including wind power in both the test systems. It is observed that FAAPO can outperform BAT algorithm and APO algorithm. Hence, the proposed algorithm can be used for integration of wind power with thermal power generators.

     

Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

Overbreak is an undesirable phenomenon in blasting operations. The causing factors of overbreak can be generally divided as blasting and geological parameters. Due to multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriated for blasting pattern design. In this research, artificial neural network (ANN) as a powerful tool for solving such complicated problems is developed to predict overbreak induced by blasting operations in the Gardaneh Rokh tunnel, Iran. To develop an ANN model, an established database comprising of 255 datasets has been utilized. A three-layer ANN was found as an optimum model for prediction of overbreak. The coefficient of determination (R²) and root mean square error (RMSE) values of the selected model were obtained as 0.921, 0.4820, 0.923 and 0.4277 for training and testing, respectively, which demonstrate a high capability of ANN in predicting overbreak. After selecting the best model, the selected model was used for optimization purpose using artificial bee colony (ABC) algorithm as one of the most powerful optimization algorithms. Considering this point that overbreak is one of the main problems in tunneling, reducing its amount causes to have a good tunneling operation. After making several models of optimization and variations in its weights, the optimum amount for the extra drilling was 1.63 m², which is 47% lower than the lowest value (3.055 m²). It can be concluded that ABC algorithm can be introduced as a new optimizing algorithm to minimize overbreak induced by tunneling.

     

Quadratic optimal neural fuzzy control for synchronization of uncertain chaotic systems

This paper presents a novel quadratic optimal neural fuzzy control for synchronization of uncertain chaotic systems via H^∞ approach. In the proposed algorithm, a self-constructing neural fuzzy network (SCNFN) is developed with both structure and parameter learning phases, so that the number of fuzzy rules and network parameters can be adaptively determined. Based on the SCNFN, an uncertainty observer is first introduced to watch compound system uncertainties. Subsequently, an optimal NFN-based controller is designed to overcome the effects of unstructured uncertainty and approximation error by integrating the NFN identifier, linear optimal control and H^∞ approach as a whole. The adaptive tuning laws of network parameters are derived in the sense of quadratic stability technique and Lyapunov synthesis approach to ensure the network convergence and H^∞ synchronization performance. The merits of the proposed control scheme are not only that the conservative estimation of NFN approximation error bound is avoided but also that a suitable-sized neural structure is found to sufficiently approximate the system uncertainties. Simulation results are provided to verify the effectiveness and robustness of the proposed control method.     

Event‐triggered dissipative synchronization for Markovian jump neural networks with general transition probabilities

In this paper, dissipative synchronization problem for the Markovian jump neural networks with time‐varying delay and general transition probabilities is investigated. An event‐triggered communication scheme is introduced to trigger the transmission only when the variation of the sampled vector exceeds a prescribed threshold condition. The transition probabilities of the Markovian jump delayed neural networks are allowed to be known, or uncertain, or unknown. By employing delay system approach, a new model of synchronization error system is proposed. Applying the Lyapunov‐Krasovskii functional and integral inequality combining with reciprocal convex technique, a delay‐dependent criterion is developed to guarantee the stochastic stability of the errors system and achieve strict (Q,S,R)?α dissipativity. The event‐triggered parameters can be derived by solving a set of linear matrix inequalities. A numerical example is presented to illustrate the effectiveness of the proposed design method.     

H ∞ synchronization of two different discrete-time chaotic systems via a unified model

This paper presents some novel synchronization methods for two discrete-time chaotic systems with different time delays, which are transformed into two unified models. First, the H _∞ performance of the synchronization error dynamical system between the drive unified model and the response one is analyzed using the linear matrix inequality (LMI) approach. Second, the novel state feedback controllers are established to guarantee H _∞ performance for the overall system. The parameters of these controllers are determined by solving the eigenvalue problem (EVP). Most discrete-time chaotic systems with or without time delays can be converted into this unified model, and H _∞ synchronization controllers are designed in a unified way. The effectiveness of the proposed design methods are demonstrated by three numerical examples.

     

一阶纯滞后智能非线性PI控制器优化设计

针对大惯性以及纯滞后严重的工业过程对象控制精度低的问题,采用专家控制与单神经元PI控制相结合的算法,并利用自适应变尺度混沌优化方法对其进行最优整定,从而实现了智能非线性PI控制器的优化设计．仿真结果表明,该智能非线性PI控制器具有较高的控制精度．