Wai Lun Lo

Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

Research into adjusting the probabilities of crossover and mutation pm in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. px and pm greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of px and pm , this paper presents the use of fuzzy logic to adaptively adjust the values of px and pm in GA. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of px and pm. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulators performance, and the convergence rate in the training are favorably compared with the GA using fixed values of px and pm. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions

Identification and control of continuous-time nonlinear systems via dynamic neural networks

In this paper, we present an algorithm for the online identification and adaptive control of a class of continuous-time nonlinear systems via dynamic neural networks. The plant considered is an unknown multi-input/multi-output continuous-time higher order nonlinear system. The control scheme includes two parts: a dynamic neural network is employed to perform system identification and a controller based on the proposed dynamic neural network is developed to track a reference trajectory. Stability analysis for the identification and the tracking errors is performed by means of Lyapunov stability criterion. Finally, we illustrate the effectiveness of these methods by computer simulations of the Duffing chaotic system and one-link rigid robot manipulator. The simulation results demonstrate that the model-based dynamic neural network control scheme is appropriate for control of unknown continuous-time nonlinear systems with output disturbance noise.

Online identification of continuous-time systems with unknown time delay

In this note, we present a recursive algorithm for online identification of systems with unknown time delay. The proposed algorithm can be interpreted as an approximate nonlinear least-squares, corresponding to modified normalized or un-normalized least-squares when the normalizing factor /spl gamma/>0 or /spl gamma/=0, respectively. Both algorithms are essentially extensions to general least-squares methodology. Simulation studies demonstrate the characteristics and performance of these algorithms.

Implementation of a decoupled optimization technique for design of switching regulators using genetic algorithms

This paper presents an implementation of a decoupled optimization technique for design of switching regulators using genetic algorithms (GAs). The optimization process entails the selection of component values in a switching regulator, in order to meet the static and dynamic requirements. Although the proposed method inherits characteristics of evolutionary computations that involve randomness, recombination, and survival of the fittest, it does not perform a whole-circuit optimization. Thus, intensive computations, that are usually found in stochastic optimization techniques can be avoided. Similar to many design approaches for power electronics circuits, a regulator is decoupled into two components, namely the power conversion stage (PCS) and the feedback network (FN). The PCS is optimized with the required static characteristics, whilst the FN is optimized with the required static and dynamic behaviors of the whole system. Systematic optimization procedures are described and the technique is illustrated with the design of a buck regulator with overcurrent protection. The predicted results are compared with the published results available in the literature and are verified with experimental measurements.

Chaotic Time Series Prediction Using a Neuro-Fuzzy System with Time-Delay Coordinates

This paper presents an investigation into the use of the delay coordinate embedding technique in the multi-input- multioutput-adaptive-network-based fuzzy inference system (MANFIS) for chaotic time series prediction. The inputs to the MANFIS are embedded-phase-space (EPS) vectors preprocessed from the time series under test, while the output time series is extracted from the output EPS vectors from the MANFIS. A moving root-mean-square error is used to monitor the error over the prediction horizon and to tune the membership functions in the MANFIS. With the inclusion of the EPS preprocessing step, the prediction performance of the MANFIS is improved significantly. The proposed method has been tested with one periodic function and two chaotic functions including Mackey-Glass chaotic time series and Duffing forced-oscillation system. The prediction performances with and without EPS preprocessing are statistically compared by using the t-test method. The results show that EPS preprocessing can help improve the prediction performance of a MANFIS significantly.

State observer based indirect adaptive fuzzy tracking control

Abstract An observer based adaptive fuzzy controller for a certain class of unknown nonlinear systems is proposed in this paper. The proposed approach employs a fuzzy system to approximate the unknown nonlinear functions in designing the adaptive controller and an observer is designed to generate an error signal for the adaptive law. Moreover, a robust H ∞ control law is obtained by solving a modified Riccati-like equation in order to compensate the effect of the approximated error and external disturbance of the system. It is proved that the overall adaptive scheme guarantees the global asymptotic stability in the Lyapunov sense if all the signals involved are uniformly bounded. Simulation studies show that the proposed controller performs well and exhibits good performance.

Self-tuning PID controller using Newton-Raphson search method

A new algorithm for self tuning of proportional-integral-derivative (PID) controllers is proposed. A combined least-squares estimation and Newton-Raphson search technique is used to determine the ultimate gain and period of an unknown system for the purpose of automatic tuning of PID controllers based on Ziegler and Nichols (ZN) or refined Ziegler and Nichols (RZN) formulas. The proposed algorithm can be applied to systems with known time delay, as well as those with unknown dead time. Simulation studies are used to demonstrate the performance of this algorithm. The performance of this PID self tuner is also compared with a popular commercial auto-tuner for simulated systems and a laboratory-scale real plant.

Simultaneous online identification of rational dynamics and time delay: a correlation-based approach

A new algorithm for simultaneous identification of a rational dynamic and an unknown or varying time delay is suggested in this communication. The proposed method is based on integration of two interacting estimation modules: First, a pseudo-pure delay system is obtained by filtering the input and output of the system. The time-delay is then identified via correlation technique. The rational dynamics is identified via standard recursive least-squares identification. An experimental study is included to illustrate the merits of this algorithm.

Use of system oscillation to locate the MPP of PV panels

This letter proposes the use of system oscillation in a perturbation-based maximum power point (MPP) tracker to locate the MPP of photovoltaic (PV) panels. Instead of using an explicit perturbation source, the tracker controller is designed to make the overall system self-oscillate, so that the duty cycle of the main switch in the power conversion stage (PCS) is inherently modulated with a small-amplitude variation at a predefined frequency around the required steady-state value. The tracking mechanism is based on comparing the ac component (due to the variation of the duty cycle) and the average value of the input voltage of the PCS to determine the quiescent duty cycle. The proposed technique does not approximate the panel characteristics and can globally locate the MPP under wide insolation conditions. The tracking capability has been verified experimentally with a 10-W PV panel in a controlled setup. Performances at the steady state and during the large-signal change of the insolation level have been studied.

Predictive PI controller

A Predictive Proportional + Integral (PPI) controller with improved performance over standard PI and PID controllers is proposed. This controller consists of two parts: a standard PI controller and a predictive term with which its dynamics depend on the system time delay. The controller is realized by a continuous time implementation; i.e. a numerical solution of differential equations rather than a transformation to the Z-domain. The performance of this controller is compared with that of the PID controller tuned by Zeigler and Nichols and three other recent methods of tuning PID controllers. The conditions for stability are derived and the sensitivity of the new controller to process parameter changes is analysed by Monte-Carlo evaluation.