Xinghuo Yu

A fuzzy neural network approximator with fast terminal sliding mode and its applications

This paper presents a novel training method for fuzzy neural network (FNN) systems to approximate unknown nonlinear continuous functions. The training algorithm uses the principle of the fast terminal sliding mode (TSM) into the conventional gradient descent (GD) learning algorithm. It guarantees that the approximation is stable and converges to the optimal approximation function with improved speed. The proposed FNN approximator is then applied in the control of an unstable nonlinear system and the Duffing system. The simulation results demonstrate the effectiveness of the proposed method.

An intelligent system for automatic layout routing in aerospace design

This paper discusses the development of an intelligent routing system for automating design of electrical wiring harnesses and pipes in aircraft. The system employs knowledge based engineering (KBE) methods and technologies for capturing and implementing rules and engineering knowledge relating to the routing process. The system reads a mesh of three dimensional structure and obstacles falling within a given search space and connects source and target terminals satisfying a knowledge base of design rules and best practices. Routed paths are output as computer aided design (CAD) readable geometry, and a finite element (FE) mesh consisting of geometry, routed paths and a knowledge layer providing detail of the rules and knowledge implemented in the process. Use of this intelligent routing system provides structure to the routing design process and has potential to deliver significant savings in time and cost.

A multi-objective constraint-handling method with PSO algorithm for constrained engineering optimization problems

This paper presents a multi-objective constraint handling method incorporating the particle swarm optimization (PSO) algorithm. The proposed approach adopts a concept of Pareto domination from multi-objective optimization, and uses a few selection rules to determine particlespsila behaviors to guide the search direction. A goal-oriented programming concept is adopted to improve efficiency. Diversity is maintained by perturbing particles with a small probability. The simulation results on the three engineering benchmark problems demonstrate the proposed approach is highly competitive.

Power generation loading optimization using a multi-objective constraint-handling method via PSO algorithm

Power generation loading optimization problem will be of practical importance in the coming carbon constrained power industry. A major objective for the coal-fired power generation loading optimization is to minimize fuel consumption to achieve output demand and to maintain NOx emissions within the environmental license limit. This paper presents a multi-objective constraint-handling method incorporating the particle swarm optimization (PSO) algorithm for the power generation loading optimization application. The proposed approach adopts the concept of Pareto dominance from multi-objective optimization, and uses several selection rules to determine particlespsila behaviors to guide the search direction. The simulation results of the power generation loading optimization based on a coal-fired power plant demonstrates the capability, effectiveness and efficiency of using a multi-objective constraint-handling method with PSO algorithm in solving significant industrial problems.

A new sliding mode-based learning control scheme

A new sliding mode-based learning control scheme for a class of SISO dynamic systems is developed in this paper. It is seen that, based on the most recent information on the closed-loop stability, a recursive learning chattering-free sliding mode controller can be designed to drive the closed-loop dynamics to reach the sliding mode surface in a finite time, on which the desired closed-loop dynamics with the zero-error convergence can be achieved.

Stability analysis of time-delayed single-input sliding mode control systems

This paper presents stability analysis of the time-delayed equivalent control based single input sliding mode control systems using an LMI approach. Conditions to guarantee the boundedness of the control system trajectories under the time-delayed equivalent control based sliding mode control are obtained. Maximum upper bound of the delay time to guarantee stability is estimated. Digital simulations are conducted to verify the theoretical results.

Modeling-error based adaptive fuzzy sliding mode control for trajectory-tracking of nonlinear systems

A novel adaptive fuzzy sliding mode control design is developed for trajectory tracking of a class of nonlinear systems in this paper. This control design uses the modelling error to adaptively estimate the deterministic uncertainties as well as the control gain based on the fuzzy systems approach. By this design, the bounds of the uncertainties are not required to be known in advance, and the robust stability of closed loop systems is analysed in the Lyapunov sense. Simulation results are given to demonstrate the improved performance.

Analysis of cascaded failures in power networks using maximum flow based complex network approach

Power networks can be modeled as networked structures with nodes representing the bus bars (connected to generator, loads and transformers) and links representing the transmission lines. In this manuscript we study cascaded failures in power networks. As network structures we consider IEEE 118 bus network and a random spatial model network with similar properties to IEEE 118 bus network. A maximum flow based model is used to find the central edges. We study cascaded failures triggered by both random and targeted attacks to the edges. In the targeted attack the edge with the maximum centrality value is disconnected from the network. A number of metrics including the size of the largest connected component, the number of failed edges, the average maximum flow and the global efficiency are studied as a function of capacity parameter (edge critical load is proportional to its capacity parameter and nominal centrality value). For each case we identify the critical capacity parameter by which the network shows resilient behavior against failures. The experiments show that one should further protect the network for a targeted attack as compared to a random failure.

Fault location in power networks using graph theory

This paper proposes a method for analyzing the vulnerability of a power system using network theory. It locates fault by combining the travelling waves methodology with the network topology to isolate the faulty link first and then locate the fault distance. The algorithm is verified on a test power network using Alternate Transients Program/Electromagnetic Transients Program (ATP/EMTP) and Matlab. The time stamps recorded are combined with the network topology to isolate the faulty link and calculate the fault distance.

Time-delay effect on equivalent control based single-input sliding mode control systems

This paper studies the time-delay effect on the equivalent control based sliding mode control. Conditions to guarantee the boundedness of the control system steady states under the time delayed equivalent control based sliding mode control are obtained. Maximum upper bound of the delay time to guarantee boundedness is estimated. Simulations are conducted to verify the theoretical results.