In this study, Fuzzy PID Control scheme is designed for an active suspension system. The main goal of an active suspension system for using in a vehicle model is reducing body deflections and handling high comfort for a passenger car. The present system was modelled as a two-degree-of-freedom (2-DOF) nonlinear vehicle model. Keywords—Active suspension system, Fuzzy PID controller, a nonlinear q...

An approach is proposed to design the intelligent electrode position controller for UHP by using nonlinear scaling and fuzzy self-tuning PID control algorithm. First, nonlinear scaling of controlled variable that compensate the nonlinearity of the object is proposed. Second, a fuzzy self-tuning PID electrode position control algorithm is designed and the parameters of the fuzzy inference are op...

There is a growing interest in the modeling and control of model helicopters using nonlinear dynamic models and nonlinear control. Application of a new intelligent control approach called Brain Emotional Learning Based Intelligent Controller (BELBIC) to design autopilot for an autonomous helicopter is addressed in this paper. This controller is applied to a nonlinear model of a helicopter. This...

The polymerization of resin adhesives, which is highly nonlinear, is chosen as a control process in this paper. When phenol and formaldehyde are mixed, a sudden and unpredictable heat is produced due to an exothermic reaction, which could cause the deviation of process temperature and hence diminish the product quality. Therefore, temperature control is necessitated for exothermic batch reactor...

This paper addresses analysis and design issues in adaptive PID control for linear second order minimal phase processes using the backstepping algorithm. The first step consists in adding an integral action to the basic backstepping algorithm to obtain a zero static error. An integrator is therefore added to the plant model and is then slid back to the controller equation at the end of the desi...

Abstract—This paper presents modeling and control of a highly nonlinear system including, non-interacting two spherical tanks using iterative learning control (ILC). Consequently, the objective of the paper is to control the liquid levels in the nonlinear tanks. First, a proportional-integral-derivative (PID) controller is applied to the plant model as a suitable benchmark for comparison. Then,...

Recent progress in the theory of neural networks played a major role in the development of new tools and techniques for modelling, identification and control of complex nonlinear dynamic systems. Intelligent control, with a special focus on neuro-control has been used successfully to solve difficult real control problems which are nonlinear, noisy and relatively complex. This is due to the fact...

Optimization of PID controller parameters has been a hot issue in the fields of Automatic control. In the automatic control process, the controlled object has nonlinear and uncertainty characteristics. Traditional PID parameters methods are often time-consuming and difficult to obtain control effect, causing the control accuracy not high. In order to solve the optimization problem of PID contro...

The problem of robust multiloop proportional-integral-derivative (PID) controller tuning for multivariable processes is addressed in this paper. The problem is formulated in the H∞ control framework, and the controller parameters are determined based on both user-specified performance and robust stability. The PID settings are computed by solving a nonlinear optimization problem with matrix ine...

This paper presents a new improved nonlinear tracking differentiator (INTD) with hyperbolic tangent function in the state-space system. The stability and convergence of the INTD are thoroughly investigated and proved. Through the error analysis, the proposed INTD can extract differentiation of any piecewise smooth nonlinear signal to reach a high accuracy. The improved tracking differentiator (...