Xiangjie Liu

Adaptive fuzzy sliding mode control for flexible satellite

The adaptive fuzzy sliding mode control is applied to the attitude stabilization of flexible satellite. The detailed design procedure of the fuzzy sliding mode control system is presented. The adaptive fuzzy control is utilized to approach the equivalent control of sliding mode control and the adaptive law is derived. The hitting control, which guarantees the stability of the control system, is developed. In order to attenuate the chattering phenomena, fuzzy rules are employed to smooth the hitting control. Simulation results show that precise attitude control is accomplished based on the proposed method.

Nonlinear Multivariable Power Plant Coordinate Control by Constrained Predictive Scheme

A coordinated control strategy is often used to ensure a thermal power plant to have a higher rate of load change, but without violating the thermal constraints. Although model predictive control has been widely used for controlling power plant, handling input constraints is a major problem especially as these plants are nonlinear. Two alternative methods of exploiting the nonlinear predictive control are presented in this paper. One is the input-output feedback linearization technique based on a suitably chosen approximated linear model. The other is based on neuro-fuzzy networks to represent a nonlinear dynamic process using a set of local models. From the criteria based on the integral absolute errors and the relative optimization time for completing the simulation, it is shown that the performance of the coordinated control of a steam-boiler generation plant using these two nonlinear predictive methods are better than the conventional predictive method.

H-Infinity State Feedback Control for a Class of Networked Cascade Control Systems With Uncertain Delay

Based on practical industrial process control, a typical configuration for networked cascade control systems (NCCSs) is analyzed. This kind of NCCSs with state feedback controllers, in which the network-induced delay is uncertain and less than a sampling period, is studied. The sufficient condition for the stabilizability of the NCCSs without disturbances is proposed, and the state feedback stabilization control laws are derived via Lyapunov stability theory and linear matrix inequality (LMI) approach. For the NCCSs with disturbances, the criterion of its robust asymptotically stability is derived and the ¿ -suboptimal state feedback H ¿ control laws are designed. The ¿-optimal state feedback H ¿ control laws are also put forward by optimizing a set of LMIs. A simulation example of a NCCS for the main steam temperature in a power plant is given to demonstrate the effectiveness of the proposed approaches.

Nonlinear Model Predictive Control for DFIG-Based Wind Power Generation

Reliable control and optimal operation of the doubly fed induction generator (DFIG) is necessary to ensure high efficiency and high load-following capability in modern wind power plants. This is often difficult to achieve using conventional linear controllers, as wind power plants are nonlinear and contain many uncertainties. Furthermore, unbalanced conditions often exist on the power network, which can degrade DFIG system performance. Considering the nonlinear DFIG dynamics, this paper proposes a nonlinear modeling technique for DFIG, meanwhile taking into account unbalanced grid conditions. Then, a nonlinear model predictive controller is derived for power control of DFIG. The prediction is calculated based on the input-output feedback linearization (IOFL) scheme. The control is derived by optimization of an objective function that considers both economic and tracking factors under realistic constraints. The simulation results show that the proposed controller can effectively reduce wear and tear of generating units under normal grid conditions, and reduce the rotor over-current under unbalanced grid conditions, thereby improving the ability of grid-connected wind turbines to withstand grid voltage faults.

Wind speed prediction using reduced support vector machines with feature selection

Abstract Accurate prediction of wind speed is one of the most effective ways to solve the problems of relaibility, security, stability and quality, which are caused by wind energy production in power systems. This paper presents a wind speed prediction concept with high efficiency convex optimization support vector machine for data regression (SVR). Based on the SVR, a reduced support vector machine (RSVM) is proposed, which preselects a subset of data as support vectors and solves a smaller optimization problem. The principal component analysis is utilized to determine the outcome of the major factors affecting the wind speed. With increasing number of the input variables in RSVM for regression structure, particle swarm optimization (PSO) is incorporated to optimize the parameters. Detailed analysis and simulations using the real time wind power plant data demonstrate the effectiveness of the RSVM-based forecasting approach.

Model-reference adaptive control based on neurofuzzy networks

Model reference adaptive control (MRAC) is a popular approach to control linear systems, as it is relatively simple to implement. However, the performance of the linear MRAC deteriorates rapidly when the system becomes nonlinear. In this paper, a nonlinear MRAC based on neurofuzzy networks is derived. Neurofuzzy networks are chosen not only because they can approximate nonlinear functions with arbitrary accuracy, but also they are compact in their supports, and the weights of the network can be readily updated on-line. The implementation of the neurofuzzy network-based MRAC is discussed, and the local stability of the system controlled by the proposed controller is established. The performance of the neurofuzzy network-based MRAC is illustrated by examples involving both linear and nonlinear systems.

Load frequency control by neural-network-based integral sliding mode for nonlinear power systems with wind turbines

Load frequency control (LFC) plays an important role in maintaining constant frequency in order to ensure the reliability of power systems. With the large-scale development of sustainable but intermittent sources such as wind and solar, such intermittency challenges the LFC problem. Moreover, the generation rate constraint (GRC) of power systems also complexes the LFC problem. Concerning the constraint, this paper addresses an integral sliding mode control (I-SMC) method for power systems with wind turbines. Since the intermittency of wind farms and the linearization of GRC deteriorate the uncertainties of power systems, sliding-mode-based neural networks are designed to approximate the uncertainties. Weight update formulas of the neural networks are derived from the Lyapunov direct method. The neural-network-based integral sliding mode controller is employed to achieve the LFC problem. By this scheme, not only are the update formulas obtained, but also the control system possesses the asymptotic stability. The simulation results by an interconnected power system illustrate the feasibility and validity of the presented method.

Robust MPC for the constrained system with polytopic uncertainty

An improved method for synthesising the constrained robust model predictive controller is proposed in this study. It constructs a continuum of terminal constraint sets off-line, and achieves robust stability with a variable control horizon on-line from the very beginning and a time-varying terminal constraint set, by solving the min–max optimisation problem, which can be formulated as a linear matrix inequality problem. This algorithm not only dramatically reduces the on-line computation burden, but also guarantees the control performance by reserving at least one free control move in the whole process. Simulation results for the three-tank system with uncertain dynamic behaviour on flux coefficients are given.

Load frequency control considering generation rate constraints

Constrained generalized predictive algorithm is employed to load frequency control in this paper. Generation rate constraint (GRC) has been considered. Using the linearization modeling technique, this paper deals with load frequency control by multivariable generalized predictive control method to build Controlled Auto-Regressive Integrated Moving Average model (CARIMA) and obtain generalized predictive control algorithm for load frequency control of the two-area reheat power system. Results demonstrate the effectiveness of the proposed generalized predictive control algorithm.

Distributed Model Predictive Load Frequency Control of the Multi-Area Power System After Deregulation

This paper proposes a distributed model predictive control scheme for the load frequency control (LFC) problem of the deregulated multi-area interconnected power system with contracted and uncontracted load demands. The traditional LFC of the interconnected power system is modified to take into account the effect of bilateral contracts on the dynamics. The concept of the distribution company participation matrix and area participation matrix are introduced to simulate these bilateral contracts and reflected in the multi-area block diagram. The distributed model predictive controller is designed by posing the LFC problem as a tracking control problem in the presence of both external disturbances and constraints that represent generation rate constraint and load reference setpoint constraint, respectively. Analysis and simulation results for a deregulated three-area interconnected power system show possible improvements on closed-loop performance and computational burden, while respecting the physical hard constraints.