An improved search space resizing method for model identification by standard genetic algorithm

In this paper, a new improved search space boundary resizing method for an optimal model’s parameter identification for continuous real time transfer function by standard genetic algorithms (SGAs) is proposed and demonstrated. Premature convergence to local minima, as a result of search space boundary constraints, is a key consideration in the application of SGAs. The new method improves the convergence to global optima by resizing or extending the upper and lower search boundaries. The resizing of the search space boundaries involves two processes, first, an identification of initial value by approximating the dynamic response period and desired settling time. Second, a boundary resizing method derived from the initial search space value. These processes brought the elite groups within feasible boundary regions by consecutive execution and enhanced the SGAs in locating the optimal model’s parameters for the identified transfer function. This new method is applied and examined on two processes, a third-order transfer function model with and without random disturbance and raw data of excess oxygen. The simulation results assured the new improved search space resizing method’s efficiency and flexibility in assisting SGAs to locate optimal transfer function model parameters in their explorations

An improved search space resizing method for model identification by Standard Genetic Algorithm

.In this paper, a new improved search space boundary resizing method for an optimal model's parameter identification by Standard Genetic Algorithms (SGAs) is proposed and demonstrated. The premature convergence to local minima, as a result of search space boundary constraints, is a key consideration in the application of SGAs. The new method improves the convergence to global optima by resizing or extending the upper and lower search boundaries. The resizing of search space boundaries involves two processes, first, an identification of initial value by approximating the dynamic response period and desired settling time. Second, a boundary resizing method derived from the initial search space value. These processes brought the elite groups within feasible boundary regions by consecutive execution and enhanced the SGAs in locating the optimal model's parameters for the identified transfer function. This new method is applied and examined on two processes, a third order transfer function model with and without random disturbance and raw data of excess oxygen. The simulation results assured the new improved search space resizing method's efficiency and flexibility in assisting SGAs to locate optimal transfer function model parameters in their explorations. © 2015 Chinese Automation and Computing Society in the UK - CAC

PID controller tuning for a multivariable glass furnace process by genetic algorithm

Standard genetic algorithms (SGAs) are investigated to optimise discrete-time proportional-integral-derivative (PID) controller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms (SGAs) are used to identify control oriented models of the plant which are subsequently used for controller optimisation. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction. © 2015 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelber

Predetermined time constant approximation method for optimising search space boundary by standard genetic algorithm

In this paper, a new predetermined time constant approximation (Tsp) method for optimising the search space boundaries to improve SGAs convergence is proposed. This method is demonstrated on parameter identification of higher order models. Using the dynamic response period and desired settling time of the transfer function coefficients offered a better suggestion for initial Tsp values. Furthermore, an extension on boundaries derived from the initial Tsp values and the consecutive execution, brought the elite groups within feasible boundary regions for better exploration. This enhanced the process of locating of the optimal values of coefficients for the transfer function. The Tsp method is investigated on two processes; excess oxygen and a third order continuous model with and without random disturbance. The simulation results assured the Tsp method's effectiveness and flexibility in assisting SGAs to locate optimal transfer function coefficients. Copyright © 2015 ACM

Decentralised Control Optimisation for a Glass Furnace by SGA’s

In this paper, the potential of standard genetic algorithms (SGAs) are presented to optimise the discrete PID parameters for multivariable glass furnace. Control oriented models of each multivariable glass furnace; glass temperature and excess oxygen are used to optimise the discrete controller with personalised cost function and adjusted boundaries by SGAs, individually. Well optimised discrete PID parameters by control oriented model are applied to realistic multivariable model by decentralised method

Robust fault diagnosis for an exothermic semi-batch polymerization reactor under open-loop

An independent radial basis function neural network (RBFNN) is developed and employed here for an online diagnosis of actuator and sensor faults. In this research, a robust fault detection and isolation scheme is developed for an open-loop exothermic semi-batch polymerization reactor described by Chylla–Haase. The independent RBFNN is employed here for online diagnosis of faults when the system is subjected to system uncertainties and disturbances. Two different techniques to employ RBFNNs are investigated. Firstly, an independent neural network (NN) is used to model the reactor dynamics and generate residuals. Secondly, an additional RBFNN is developed as a classifier to isolate faults from the generated residuals. Three sensor faults and one actuator fault are simulated on the reactor. Moreover, many practical disturbances and system uncertainties, such as monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise, are modelled. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method

Fault tolerant control for nonlinear systems using sliding mode and adaptive neural network estimator

This paper proposes a new fault tolerant control scheme for a class of nonlinear systems including robotic systems and aeronautical systems. In this method, a sliding mode control is applied to maintain system stability under the post-fault dynamics. A neural network is used as on-line estimator to reconstruct the change rate of the fault and compensate for the impact of the fault on the system performance. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimator is guaranteed to converge to the fault change rate, while the entire closed-loop system stability and tracking control is guaranteed. Compared with the existing methods, the proposed method achieved fault tolerant control for time-varying fault, rather than just constant fault. This greatly expands the industrial applications of the developed method to enhance system reliability. The main contribution and novelty of the developed method is that the system stability is guaranteed and the fault estimation is also guaranteed for convergence when the system subject to a time-varying fault. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results demonstrated that the post-fault is stable and the performance is maintained

Active and Reactive Power Sharing Between Three-Phase Winding Sets of a Multiphase Induction Machine

This paper introduces a method which can accurately transfer active and reactive power between sets of three-phase windings of a multiphase induction machine (IM). The machine has to have a multiple of three phases (3, 6, 9 etc.), with each three phases representing one set. The paper demonstrates first that the sets do not share the power in the same way as they share a torque. Therefore, the paper proposes a method that is capable of achieving accurate power sharing. This is ensured by considering the whole power that is transferred through the air-gap to/from a set, instead of considering only a power that is transferred between that set and a rotor shaft. A complete control algorithm of the proposed method is given. Experiments and simulations verify theoretical considerations and the proposed control

Fault Tolerant Control of Electronic Throttles with Friction Changes

To enhance the reliability of the electronic throttle and consequently the vehicles driven by the internal combustion engines, a fault tolerant control strategy is developed in this paper. The proposed method employs a full-order terminal sliding mode control in conjunction with an adaptive radial basis function network to estimate change rate of the fault. Fault tolerant control to abrupt and incipient changes in the throttle viscous friction torque coefficient and the throttle coulomb friction torque coefficient is achieved. Whilst the throttle position is driven to track the reference signal, the post-fault dynamics are guaranteed to converge to the equilibrium point in finite time, and the control is smooth without chattering. A nonlinear Simulink model of an electronic throttle is developed with real physical parameters and is used for evaluation of the developed method. A significant change of the throttle friction torque is simulated, and the fault tolerant control system keeps system stability and tracking the reference signal in the presence of the fault