Muhammad Bilal Kadri

The control of uncertain non-linear plants is a challenging task. Adaptive Fuzzy Controllers have been applied widely for the control of such processes. The book discusses one particular approach to the fuzzy controllers. Adaptive Model Free Fuzzy Control is a goal oriented approach which tries to control the plant based on the information available without specifically modeling the plant. The prime objective of the controller is to reduce the control error. The book discusses two different model free control structures and their relative merits and de-merits. Four different fuzzy identification schemes have been discussed which can be used to train the controller parameters. The simulation results from both the controllers have been included to get a better understanding of the control performance. The cooling coil of an air handling unit is used for the realistic testing of the controllers.

Non-linear uncertain systems are difficult to model and control especially when the system has significant amount of disturbances. Achieving tight control performance in the face of poorly measured disturbance is a difficult objective. To reject the disturbances different methodologies have been proposed. The advantages and disadvantages of Internal model Control (IMC) are discussed. Hybrid control schemes has been proposed which incorporate the feedforward of the measured disturbance along with IMC. The benefits of Fuzzy Relational Model in the representation of the measurement uncertainty constitute a major portion of the book. A novel approach to defuzzification is explored which can significantly reduce the control activity. A novel two-stage approach is proposed which is able to provide a better representation of the measurement uncertainty in the fuzzy control signal and consequently less control activity. Artificial Neural Network model of the laboratory air handling unit has been developed. Results from the simulation as well as from the real system are included.

The paper considers the usefulness of a control strategy based on a fuzzy relational model of the controller to counteract uncertainties caused by measurement noise and unmeasured disturbances. The fuzzy relational model is identified using a combination of feedback error learning and fuzzy identification. An important feature of the resulting fuzzy relational model is that it will generate a fuzzy output in the presence of uncertainties. Two causes of uncertainty are considered separately, the first cause of uncertainty is due to the noise on the sensor measuring the controlled variable and the second one is an unmeasured input disturbance. Results are presented that show that the fuzzy control signal is representative of the uncertainties and that conditional defuzzification can then be used to improve the control performance by reducing the control activity.

Model Free Adaptive Control guarantees good control performance without having any substantial information about the plant to be controlled. The particular type of model free controller discussed in the paper is based on the inverse plant model. The controller effectively maintains the temperature by adjusting the water flow rate through the cooling coil in an experimental test rig. The controller is shown to be robust and the particular structure complements the learning scheme. The fuzzy learning scheme, which is based on the ...

Disturbance rejection is one of the most challenging issues when the system under control is nonlinear and little a priori information is available about the system. Internal model control (IMC) has been extensively used for disturbance rejection but has certain drawbacks. Most of the work reported in literature deals with additive output disturbance. The main focus of this study is on multiplicative input disturbance. In this work, fuzzy model-free adaptive control (FMAC) is used to reject the disturbance in an uncertain nonlinear plant. Different schemes have been investigated for rejecting the disturbance. It is demonstrated that the particular type of disturbance cannot be completely rejected using the IMC. The second methodology used to reject the disturbance is feedforward of the measured disturbance. Feedforward of the input disturbance is used which is able to counteract the effect of the disturbances but resulting in an increase in the control activity. The control activity is related to the noise on the sensor measuring the input disturbance. The FMAC is modelled as a fuzzy relational model (FRM) which is able to represent the noise level in the fuzzy control signal. Conditional defuzzification is applied on the resulting fuzzy control signal; which is able to reduce the control activity while maintaining the controlled output at the desired level. FMAC is tested with a modified version of the Hammerstein Model. The control performance demonstrates the effectiveness of the proposed novel methodology in rejecting the input multiplicative disturbance while reducing the control activity.

Abstract Controlling complex systems, containing nonlinearities and constraints, is always a domain of interest for researchers. Different schemes such as Nonlinear Model Predictive Control (NMPC) have been proposed but these are usually computationally demanding and complex by themselves.

Designing controllers to reject disturbances, which cannot be accurately measured or estimated, is a challenging problem in real applications where the behaviour of the system is both non-linear and uncertain. Control schemes based on internal model control (IMC) can only be used to reject disturbances in non-linear systems if they do not affect the relationship between the controlled variable and the control signal i.e. they are additive input disturbances or output disturbances. An accurate model of the relationship between the disturbance and the controlled variable is required in feedforward control schemes. In this paper, an adaptive model-free fuzzy controller is designed to offer good control performance for non-linear uncertain plants in the face of input disturbances which affect the relationship between the controlled variable and the control signal. The controller is a T-S fuzzy model whose output parameters are updated on-line using feedback error learning. Three ways of incorporating the inaccurate measurements of the disturbance are considered :(1) The measured disturbance is one of the inputs of the fuzzy controller (2) The fuzzy controller is used in an IMC scheme, in which the measured disturbance is an input of the internal model. (3) The measured disturbance is fed into both the fuzzy controller and the internal model of the IMC scheme. A computer simulation, which is based on a modified Hammerstein model of the behaviour of the cooling coil in an air-conditioning system, is used to test the performance of each version of the control scheme. Results are presented, which show that feedforward of the disturbance provides the best control.

In this study, a genetic algorithm is employed to minimize the entropy generation rate in microchannel heat sinks. The entropy generation rate allows the combined effects of thermal performance and pressure drop to be assessed simultaneously as the heat sink interacts with the surrounding flow field. Previously developed models for the heat transfer, pressure drop and entropy generation rate are used in the optimization procedure. The results of optimization are compared with existing results obtained by the Newton–Raphson method. It is observed that the GA gives better overall performance of the microchannel heat sinks.

Model Free Adaptive Control (MFAC) has been used to reject the sensor noise measuring the controlled output. The MFAC is trained using feedback error learning law. The measurement noise makes the control error (used by feedback error learning) very noisy which results in increased control activity as well as corruption of the past learning. The MFAC is modeled as a Fuzzy Relational Model (FRM). FRM can be trained to represent the sensor noise occurring at the plant output in the fuzzy control signal. This representation of the sensor noise can be effectively utilized to reduce the control activity; hence increasing the life span of an actuator and contributing towards enhanced control performance. The control activity can be reduced by using conditional defuzzification which defuzzifies the fuzzy control signal by taking into consideration the uncertainty representation. By employing conditional defuzzification the controller will not respond to the noisy measurement of the plant output. The superiority of the conditional defuzzification used in conjunction with MFAC is demonstrated by the fact that with increased sensor noise the control activity reduces while maintaining the same control performance.