Entity

Time filter

Source Type


Tang Y.,Yanshan University | Tang Y.,National Engineering Research Center for Equipment and Technology of Cold Strip Rolling | Cui M.,Yanshan University | Hua C.,Yanshan University | And 4 more authors.
Expert Systems with Applications | Year: 2012

Fractional-order PID (FOPID) controller is a generalization of standard PID controller using fractional calculus. Compared to PID controller, the tuning of FOPID is more complex and remains a challenge problem. This paper focuses on the design of FOPID controller using chaotic ant swarm (CAS) optimization method. The tuning of FOPID controller is formulated as a nonlinear optimization problem, in which the objective function is composed of overshoot, steady-state error, raising time and settling time. CAS algorithm, a newly developed evolutionary algorithm inspired by the chaotic behavior of individual ant and the self-organization of ant swarm, is used as the optimizer to search the best parameters of FOPID controller. The designed CAS-FOPID controller is applied to an automatic regulator voltage (AVR) system. Numerous numerical simulations and comparisons with other FOPID/PID controllers show that the CAS-FOPID controller can not only ensure good control performance with respect to reference input but also improve the system robustness with respect to model uncertainties. © 2011 Elsevier Ltd. All rights reserved. Source


Li J.-X.,Yanshan University | Fang Y.-M.,Yanshan University | Fang Y.-M.,National Engineering Research Center for Equipment and Technology of Cold Strip Rolling | Shi S.-L.,Yanshan University
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | Year: 2012

A robust output-feedback control algorithm with unknown input observer is presented for the hydraulic servo-position system in a cold-strip rolling mill with uncertain parameters, immeasurable states and unknown external load forces. The disturbance term containing the unknown external load forces is regarded as an unknown input, for which we build an observer. A robust output-feedback controller is then designed with this observer. Theoretical analysis shows that the resulting closed-loop system is uniformly bounded stable, and has robust H-infinity performance. A simulation is carried out on the hydraulic servo position system of a 650mm reversing cold-strip mill, results show the validity of the proposed algorithm. Source


Li G.,Yanshan University | Niu P.,Yanshan University | Niu P.,National Engineering Research Center for Equipment and Technology of Cold Strip Rolling | Ma Y.,Yanshan University | And 2 more authors.
Knowledge-Based Systems | Year: 2014

In this paper, a novel optimization technique based on artificial bee colony algorithm (ABC), which is called as PS-ABCII, is presented. In PS-ABCII, there are three major differences from other ABC-based techniques: (1) the opposition-based learning is applied to the population initialization; (2) the greedy selection mechanism is not adopted; (3) the mode that employed bees become scouts is modified. In order to illustrate the superiority of the proposed modified technique over other ABC-based techniques, ten classical benchmark functions are employed to test. In addition, a hybrid model called PS-ABCII-ELM is also proposed in this paper, which is combined of the PS-ABCII and Extreme Learning Machine (ELM). In PS-ABCII-ELM, the PS-ABCII is applied to tune input weights and biases of ELM in order to improve the generalization performance of ELM. And then it is applied to model and optimize the thermal efficiency of a 300 MW coal-fired boiler. The experimental results show that the proposed model is very convenient, direct and accurate, and it can give a general and suitable way to predict and improve the boiler efficiency of a coal-fired boiler under various operating conditions. © 2014 Elsevier B.V. All rights reserved. Source


Li J.-X.,Yanshan University | Fang Y.-M.,Yanshan University | Fang Y.-M.,National Engineering Research Center for Equipment and Technology of Cold Strip Rolling | Shi S.-L.,Yanshan University
Kongzhi yu Juece/Control and Decision | Year: 2013

An anti-windup-based robust dynamic output feedback control algorithm is presented for hydraulic servo position system with unmeasurable states, uncertain parameters and input saturation in a rolling mill. Firstly, a sufficient condition of stability can be transformed into a linear matrix inequality(LMI) condition by using Finsler's lemma, and the controller parameter matrices are obtained by solving LMIs; Secondly, by compromising the disturbance attenuation and stability region, the anti-windup matrix is obtained by solving a convex optimization problem. It can be proved that the proposed method can guarantee the closed-loop system is uniformly bounded stable and possesses robust H∞ performance. Finally, a simulation is carried out on the hydraulic servo position system of 650mm reversing cold-strip rolling mill, the simulation results show the validity of the proposed algorithm. Source


Li G.,Yanshan University | Li G.,National Engineering Research Center for Equipment and Technology of Cold Strip Rolling | Niu P.,Yanshan University | Niu P.,National Engineering Research Center for Equipment and Technology of Cold Strip Rolling
Neural Computing and Applications | Year: 2013

The extreme learning machine (ELM) is a novel single hidden layer feedforward neural network, which has the superiority in many aspects, especially in the training speed; however, there are still some shortages that restrict the further development of ELM, such as the perturbation and multicollinearity in the linear model. To the adverse effects caused by the perturbation or the multicollinearity, this paper proposes an enhanced ELM based on ridge regression (RR-ELM) for regression, which replaces the least square method to calculate output weights. With an additional adjustment of ridge regression, all the characteristics become even better. Simulative results show that the RR-ELM, compared with ELM, has better stability and generalization performance. © 2011 Springer-Verlag London Limited. Source

Discover hidden collaborations