Yinggan Tang

Fractional order sliding mode controller design for antilock braking systems

Antilock braking system (ABS) is a highly nonlinear system including variation and uncertainties in the parameters due to changes in vehicle loadings, road condition, etc. It is a difficult task to design an ideal controller for ABS. In this paper, a novel robust controller named fractional order sliding mode controller (FOSMC) is proposed for ABS to regulate the slip to a desired value. The proposed FOSMC combines sliding mode controller (SMC) with fractional order dynamics, in which fractional order proportional-derivative (FOPD) sliding surface is adopted. FOSMC can not only deal with the uncertainties in ABS system but also track the desired slip faster than conventional integer order SMC with proportional or proportional-derivative sliding surface. Experimental results demonstrate the effectiveness of the proposed method.

An improved krill herd algorithm

A deficiency of KH is analyzed.Why KH cannot achieve the excellent balance between exploration and exploitation in optimization processing is explained.To overcome the defect, an improved KH with linear decreasing step (KHLD) is proposed. Krill herd (KH) inspired by the herding behavior of the krill individuals is a new swarm intelligent algorithm which is proved to perform better than other swarm intelligent algorithms. However, there are some weak points yet. In this paper, we analyze a deficiency of KH which cannot achieve the excellent balance between exploration and exploitation in optimization processing and proposed an improved KH-krill herd with linear decreasing step (KHLD). Twenty benchmark functions are used to verify the effectiveness of these improvements and it is illustrated that, in most cases, the performance of KHLD is superior to the standard KH.

Identification of Hammerstein model using functional link artificial neural network

In this paper, a novel algorithm is developed for identifying Hammerstein model. The static nonlinear function is characterized by function link artificial neural network (FLANN) and the linear dynamic subsystem by an ARMA model. The utilization of FLANN can not only result in a simple and effective representation of static nonlinearity but also simplify the learning algorithm. A two-step procedure is adopted to identify Hammerstein model by using a specially designed input signal, which separates the identification of linear part from that of nonlinear part. Levenberg-Marquart algorithm is used to learn the weights of FLANN. Simulation examples demonstrate the effectiveness of the proposed method.

Parameter identification of fractional order systems using block pulse functions

In this paper, a novel method is proposed to identify the parameters of fractional-order systems. The proposed method converts the fractional differential equation to an algebraic one through a generalized operational matrix of block pulse functions. And thus, the output of the fractional system to be identified is represented by a matrix equation. The parameter identification of the fractional order system is converted to a multi-dimensional optimization problem, whose goal is to minimize the error between the output of the actual fractional order system and that of the identified system. The proposed method can simultaneously identify the parameters and the fractional differential orders of the fractional order system and avoid the drawbacks in the literature that the fractional differential orders should be known or commensurate. Furthermore, the proposed method avoids complex calculations of the fractional derivative of input and output signals. Illustrative examples covering both fractional and integer systems are given to demonstrate the validity of the proposed method. HighlightsA new method for fractional system identification is proposed.The fractional integral operator is converted to an algebraic equation.The proposed method can simultaneously identify the parameters and orders.The proposed method does not need the orders of the system to be known or commensurate.

Optimum Design of Fractional Order PID Controller for an AVR System Using an Improved Artificial Bee Colony Algorithm

Abstract Fractional order proportional-integral-derivative (FOPID) controller generalizes the standard PID controller. Compared to PID controller, FOPID controller has more parameters and the tuning of parameters is more complex. In this paper, an improved artificial bee colony algorithm, which combines cyclic exchange neighborhood with chaos (CNC-ABC), is proposed for the sake of tuning the parameters of FOPID controller. The characteristic of the proposed CNC-ABC exists in two folds: one is that it enlarges the search scope of the solution by utilizing cyclic exchange neighborhood techniques, speeds up the convergence of artificial bee colony algorithm (ABC). The other is that it has potential to get out of local optima by exploiting the ergodicity of chaos. The proposed CNC-ABC algorithm is used to optimize the parameters of the FOPID controller for an automatic voltage regulator (AVR) system. Numerical simulations show that the CNC-ABC FOPID controller has better performance than other FOPID and PID controllers.

Optimal gray PID controller design for automatic voltage regulator system via imperialist competitive algorithm

Automatic voltage regulator (AVR) is an equipment maintaining the terminal voltage of generators to a specific level all the time and under any load conditions. Many controllers for AVR system are designed based on a linearized normal model of AVR system and they are not robust enough against uncertainties such as parameter variation and load change in the system. In this paper, a gray PID (GPID) controller is designed for AVR system. The GPID controller consists of two parts, i.e. a conventional PID controller together with a gray compensation controller. In GPID controller, gray GM (0, N) model is used to estimate the uncertainties online, and a gray compensation controller is constructed according the estimation results to eliminate the effect of uncertainties. To further improve its performance, the GPID controller’s parameters are optimized through a new evolutionary algorithm, i.e., imperialist competitive algorithm (ICA). The proposed GPID controller can effectively deal with the uncertainties in AVR system. Simulation results illustrate its effectiveness of the proposed control scheme.

Nonlinear system identification using least squares support vector machine tuned by an adaptive particle swarm optimization

AbstractIn this paper, we present a method for nonlinear system identification. The proposed method adopts least squares support vector machine (LSSVM) to approximate a nonlinear autoregressive model with eXogeneous (NARX). First, the orders of NARX model are determined from input–output data via Lipschitz quotient criterion. Then, an LSSVM model is used to approximate the NARX model. To obtain an efficient LSSVM model, a novel particle swarm optimization with adaptive inertia weight is proposed to tune the hyper-parameters of LSSVM. Two experimental results are given to illustrate the effectiveness of the proposed method.

Ill-posed Echo State Network based on L-curve Method for Prediction of Blast Furnace Gas Flow

Blast furnace gas is a significant energy resource in steel industry. Keeping stable of blast furnace gas flow is an important task for the furnace itself and the application of byproduct gas. However, owing to fluctuation and noisy of gas flow, echo state network is usually ill-posed in the prediction and it is very difficult to accurately predict the amount of gas. In this paper, in order to increase the accuracy of prediction in ill-posed echo state network model, L-curve method is used to compute the regularization parameter, which can alleviate the influence of ill-condition for ESN. Finally, to verify the effectiveness of the proposed method, the real data from blast furnace is employed in the experiments. Compared with two parameter regularization methods and four types of prediction methods, the results demonstrate that the proposed method exhibits a higher prediction accuracy for gas prediction.

Optimal fractional order PID controller design for automatic voltage regulator system based on reference model using particle swarm optimization

Automatic voltage regulator (AVR) system is an important equipment in power system for maintaining the terminal voltage of the generator at a specific level. Recently, fractional order PID controller has been designed for AVR system. However, many fractional order PID controller designing methods need to calculate various performance indices in time domain and frequency domain in the process of parameter tuning, which is a tedious and complex process and satisfactory performance can not be obtained. In this paper, a new fractional order PID controller designing method is proposed AVR system based on Bodes reference model. The optimal parameters of FOPID controller is obtained through minimizing the integrated absolute error (IAE) between the output of the Bodes ideal reference model and that of the plant. Particle swarm optimization (PSO) is responsible to search the solution of the IAE criterion, i.e., the parameters of FOPID controller. Extensive simulations and comparisons show that the designed FOPID controller has more excellent performance. Meanwhile, PSO algorithm is effective for searching the optimal FOPID controller parameters.

A fast training algorithm for extreme learning machine based on matrix decomposition

Extreme Learning Machine (ELM), a competitive machine learning technique for single-hidden-layer feedforward neural networks (SLFNNs), has proven to be efficient and effective algorithm for regression and classification problems. However, traditional ELM involves a large number of hidden nodes for complex real world regression and classification problems which increasing the computation burden. In this paper, a decomposition based fast ELM (DFELM) algorithm is proposed to effectively reduce the computational burden for large number of hidden nodes condition. Compared with ELM algorithm, DFELM algorithm has faster training time with a large number of hidden nodes maintaining the same accuracy performance. Experiment on three regression problems, six classification problems and a complex blast furnace modeling problem are carried out to verify the performance of DFELM algorithm. Moreover, the decomposition method can be extended to other modified ELM algorithms to further reduce the training time.