Modelling of an hydraulic excavator using simplified refined instrumental variable (SRIV) algorithm

Instead of establishing mathematical hydraulic system models from physical laws usually done with the problems of complex modelling processes, low reliability and practicality caused by large uncertainties, a novel modelling method for a highly nonlinear system of a hydraulic excavator is presented. Based on the data collected in the excavator's arms driving experiments, a data-based excavator dynamic model using Simplified Refined Instrumental Variable (SRIV) identification and estimation algorithms is established. The validity of the proposed data-based model is indirectly demonstrated by the performance of computer simulation and the real machine motion control experiments.     

Multiple model approach to nonlinear system identification with an uncertain scheduling variable using EM algorithm

This paper deals with system identification of general nonlinear dynamical systems with an uncertain scheduling variable. A multi model approach is developed; wherein, a set of local auto regressive exogenous (ARX) models are first identified at different process operating points, and are then combined to describe the complete dynamics of a nonlinear system. An expectation-maximization (EM) algorithm is used for simultaneous identification of local ARX models, and for computing the probability associated with each of the local ARX models taking effect. A smoothing algorithm is used to estimate the distribution of the hidden scheduling variables in the EM algorithm. If the dynamics of the scheduling variables are linear, Kalman smoother is used; whereas, if the dynamics are nonlinear, sequential Monte-Carlo (SMC) method is used. Several simulation examples, including a continuous stirred tank reactor (CSTR) and a distillation column, are considered to illustrate the efficacy of the proposed method. Furthermore, to highlight the practical utility of the developed identification method, an experimental study on a pilot-scale hybrid tank system is also provided.     

遗传算法在水电机组调速器PID参数优化中的应用

水轮发电控制是一个复杂的动态过程,特别是对大型机组,控制策略和优化算法的选取对机组的性能尤为重要.为了获取不同工况下水轮发电机组调速器的优化参数,首先建立了一种机组的非线性全数字仿真模型.结合调节系统的特点,提出了一种新的反映系统综合性能指标的适应度函数,利用遗传算法对自适应变参数PID调速器的3个参数b_t,T_d,T_n进行了优化.具体实施优化过程中,采用了C++ Builder编制遗传算法主程序;使用了Matlab中的Simulink工具箱建立机组仿真模型,交互接口通过Matlab中的引擎Engine来实现.仿真实验表明:与正交实验法寻优结果相比较,在机组的不同运行状态、不同工况下遗传算法都能获得更为理想的寻优效果.同时,该优化方法已经应用于某巨型电站的调速器中.     

Design optimization of fuzzy controllers in building structures using the crystal structure algorithm (CryStAl)

The current study uses a recently-developed metaheuristic method called Crystal Structure Algorithm (CryStAl) to achieve optimized vibration control in structural engineering. More specifically, this algorithm, which is inspired by the well-established crystallographic principles underlying the formation of crystalline solids in nature, is applied to the optimization of fuzzy logic controllers in building structures. To demonstrate the capability of this method in solving real engineering problems, two real-size building structures, one with three and the other with twenty stories, are considered. The fuzzy controllers are implemented through an active control system to control the seismically-induced vibrations of the structures intelligently. The evaluation criteria utilized to assess the overall performance of the optimization method applied to the fuzzy control system are presented and discussed. Through nonlinear structural analyses, the ductility, energy dissipation, and other nonlinear characteristics of the structures are also considered as the structural responses to be controlled. The computational results obtained from this novel metaheuristic algorithm are compared with those of the other expert systems from the optimization literature. The findings of this paper demonstrate that the Crystal Structure Algorithm is capable of outranking the other methods in the majority of considered cases.     

Modelling and control of Hammerstein system using B-spline approximation and the inverse of De Boor algorithm

In this article a simple and effective controller design is introduced for the Hammerstein systems that are identified based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The controller is composed by computing the inverse of the B-spline approximated nonlinear static function, and a linear pole assignment controller. The contribution of this article is the inverse of De Boor algorithm that computes the inverse efficiently. Mathematical analysis is provided to prove the convergence of the proposed algorithm. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.     

Design of a fuzzy-PID controller for a nonlinear hydraulic turbine governing system by using a novel gravitational search algorithm based on Cauchy mutation and mass weighting

Hydraulic turbine governing system (HTGS) is a complicated nonlinear system, which regulates frequency and power of hydropower generating unit. In previous study, control model of HTGS is always overly simplified and the elastic water hammer model is seldom considered. In this paper, a nonlinear HTGS model with elastic water hammer effect has been studied and a fuzzy-PID controller is designed to improve control quality of this system. In order to optimize the fuzzy-PID controller, a novel gravitational search algorithm based on Cauchy mutation and mass weighing (GSA-CW) has been proposed with two improvements: a weighting strategy is designed to accelerate the convergence by assigning weights to agents in mass calculation; a combined mutation strategy based on Cauchy and Gaussian distribution is proposed to balance the exploration and exploitation ability of the proposed algorithm. At first, the searching ability of the GSA-CW has been verified on a set of 13 complex benchmark functions by statistical analysis. And then, the GSA-CW is applied to optimize the fuzzy-PID controller, while different meta-heuristics and different PID controllers are employed for comparison. Experimental results indicate that the fuzzy-PID controller optimized by the GSA-CW is more effective to improve the control quality of the nonlinear HTGS.     

Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model

We investigated the possibility of applying a hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID controller to a nonlinear pneumatic artificial muscle (PAM) robot arm to improve its joint angle position output performance. The proposed hybrid inverse NARX fuzzy-PID controller is implemented to control a PAM robot arm that is subjected to nonlinear systematic features and load variations in real time. First the inverse NARX fuzzy model is modeled and identified by a modified genetic algorithm (MGA) based on input/output training data gathered experimentally from the PAM system. Second the performance of the optimized inverse NARX fuzzy model is experimentally demonstrated in a novel hybrid inverse NARX fuzzy-PID position controller of the PAM robot arm. The results of these experiments demonstrate the feasibility and benefits of the proposed control approach compared to traditional PID control strategies. Consequently, the good performance of the MGA-based inverse NARX fuzzy model in the proposed hybrid inverse NARX fuzzy-PID position control of the PAM robot arm is demonstrated. These results are also applied to model and to control other highly nonlinear systems.     

Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm

Centrality is one of the most important fields of social network research. To date, some centrality measures based on topological features of nodes in social networks have been proposed in which the importance of nodes is investigated from a certain point of view. Such measures are one dimensional and thus not feasible for measuring sociological features of nodes. Given that the main basis of Social Network Analysis (SNA) is related to social issues and interactions, a novel procedure is hereby proposed for developing a new centrality measure, named Sociability Centrality, based on the TOPSIS method and Genetic Algorithm (GA). This new centrality is not only based on topological features of nodes, but also a representation of their psychological and sociological features that is calculable for large size networks (e.g. online social networks) and has high correlation with the nodes' social skill questionnaire scores. Finally, efficiency of the proposed procedure for developing sociability centrality was tested via implementation on the Abrar Dataset. Our results show that this centrality measure outperforms its existing counterparts in terms of representing the social skills of nodes in a social network.     

Application of Wiener model predictive control (WMPC) to an industrial C2-splitter

Dual composition control of a high-purity distillation column is recognized as an industrially important, yet notoriously difficult control problem. It is proposed, however, that Wiener models, consisting of a linear dynamic element followed in series by a static nonlinear element, are ideal for representing this and several other nonlinear processes. They are relatively simple models requiring little more effort in development than a standard linear step response model, yet offer superior characterization of systems with highly nonlinear gains. Wiener models may be incorporated into MPC schemes in a unique way that effectively removes the nonlinearity from the control problem, preserving many of the favorable properties of linear MPC, especially in the analysis of stability. In this paper, Wiener model predictive control is applied to an industrial C2-splitter at the Orica Olefines plant with promising results.