The internal model control (IMC) scheme has been widely applied in the field of process control. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of linear design procedures can be exploited. The IMC scheme results in controllers that are comparable to gain-sched...

This paper presents the development of a Genetic Algorithm Supervisory Loop (GASL) to enhance the performance of Adaptive Controllers in the Self Tuning Regulator (STR) framework. The GASL is designed to tune the identification model and control parameters to achieve an optimum performance of the process. The STR controller used to demonstrate the technique effectiveness is a real time Proporti...

With the rapid advance of digital control hardware, it is time to take the simple but effective proportional-integral-derivative (PID) control technology to the next level of performance and robustness. For this purpose, a nonlinear PID and active disturbance rejection framework are introduced in this paper. It complements the existing theory in that (1) it actively and systematically explores ...

The problem of output regulation for nonlinear time-varying control systems under uncertainties is one of particular interest for real-time control system design. There is a broad set of practical problems in the control of aircraft, robotics, mechatronics, chemical industry, electrical and electro-mechanical systems where control systems are designed to provide the following objectives: (i) ro...

This paper proposes an adaptive proportional integral-derivative (PID) speed control scheme for permanent magnet synchronous motor (PMSM) drives. The proposed controller consists of three control terms: a decoupling term, a PID term, and a supervisory term. The first control term is employed to compensate for the nonlinear factors, the second term is made to automatically adjust the control gai...

The paper deals with a controller design for the nonlinear processes using genetic algorithm and neural model. The aim was to improve the control performance using genetic algorithm for optimal PID controller tuning. The plant model has been identified via an artificial neural network from measured data. The genetic algorithm represents an optimisation procedure, where the cost function to be m...

A class of adaptive disturbance tracking controllers (ADTCs) is augmented with disturbance and state estimation and adaptive state feedback, in which a controller and estimator, which are designed on the basis of a lower-order model, are used to control a higher-order nonlinear plant. The ADTC requires that the plant be almost strict positive real (ASPR) to ensure stability. In this paper, we s...

Proportional-integral-derivative (PID) control is a control strategy that has been successfully used over many years. Simplicity, robustness, a wide range of applicability and near-optimal performance are some of the reasons that have made PID control popular in the academic institutes and industries. This paper presents a new development of the proportional control method for stable tracking c...

This paper presents multi-layer feedforward neural network-based identification and approximate predictive controller (NNAPC) for a two degree-of-freedom (DOF), quarter-car servohydraulic vehicle suspension system. The nonlinear dynamics of the servo-hydraulic actuator is incorporated in the suspension model. A suspension travel controller is developed to improve the ride comfort and handling q...

Abstract: In this paper, rotor speed and position of a Switched Reluctance Motor (SRM) are controlled using an intelligent control algorithm. The controller is working based on a PID signal while its gain is permanently tuned by means of an Emotional Learning Algorithm to achieve a better control performance. Here, nonlinear characteristic of SRM is identified using an efficient training algori...