Khairuddin Osman

Modelling and controller design for a cruise control system

This paper presents the performance of different control approaches that consist of conventional, modern and intelligent controller that employed in a cruise control system. The cruise control system is one of the most enduringly popular and important laboratory models for teaching control system engineering. The system is widely used because it is very simple to understand and yet the control techniques cover many important classical and modern design methods. In this paper, the mathematical modelling for linear and nonlinear dynamic model of the cruise control system is obtained. PID, state space and artificial intelligence controller (fuzzy logic) are designed for linear model. Meanwhile, PID with feedforward controller is proposed for nonlinear model with disturbance effect. Feedforward-type PD controller is proposed in this study in order to eliminate the gravitational and wind disturbance effect. Simulation will be carried out using. Finally, a comparative assessment of the impact of each controller on the basis of results acquired.

System Identification model for an Intelligent Pneumatic Actuator (IPA) system

Intelligent Pneumatic Actuators (IPA) is a new generation of actuator for Research and Development (R&D). The purpose of this paper is to present the System Identification (SI) technique and procedure for the IPA system. System identification is used to obtain the mathematical model (transfer function) of the IPA system from the measured experimental data. Flow for the SI procedure starts with experimental setup, model structure selection, model estimation, model validation and finally testing with basic controller to prove the operation of IPA system. Auto Regressive with Exogenous Input (ARX) model is chosen as model structure of the system. Based on the input and output data of the system (model validation), best fit criterion and correlation analysis of the residual is analyzed to determine the adequate model to represent the IPA system. The result from SI model shows linear discrete model in order to obtain a discrete transfer function for the IPA system.

Nonlinear mathematical model of an Intelligent Pneumatic Actuator (IPA) systems: Position and force controls

This paper presents a nonlinear mathematical modeling of an Intelligent Pneumatic Actuator (IPA) systems and comparison of simulation results with existing experimental results of position and force control data. The derivation method of mathematical equation is referred and reviewed from several literatures. From the mathematical modeling, a simulation analysis was done and compared with existing data from previous research. This paper proposed a reverse engineering method from existing real system on the intelligent actuator that focused more on development of hardware and experimental setup to simulation analysis for validation of the developed model. The simulation results show the affirmation of the developed model based on the mathematical derivation work. The simulation results demonstrate the open-loop and close-loop control systems of position and force tracking control. Finally, performance of this system are analyzed and compared. For future research, this simulation can be used for development of new controllers, validation process and can be applied to real system.

Real-time position control of intelligent pneumatic actuator (IPA) system using optical encoder and pressure sensor

Purpose – Intelligent pneumatic actuator (IPA) is a new generation of actuator developed for Research and Development (R&D) purposes in the academic and industrial fields. The purpose of this paper is to show the application of optical encoder and pressure sensor in IPA, to develop a real-time model similar to the existing devices, and to assess the position control performance using a proportional-integrative (PI) controller and a bang-bang controller in real-time. Design/methodology/approach – A micro optical encoder chip is used to detect cylinder rod position by reading constructed laser stripes on a guide rod, whereas a pressure sensor is used to detect the chamber pressure reading. To control the cylinder movements by manipulating pulse-width modulation (PWM) cycles, two unit valves of two ports and two positions were used. A PI controller and a bang-bang controller are used with suitable gain value to drive the valve using PWM to achieve the target actuator position. Findings – The results show the...

System identification and embedded controller design for pneumatic actuator with stiffness characteristic

This paper presents model and controller design applications to pneumatic actuator embedded system. Two model strategies of position and force are proposed to realize compliance control for stiffness characteristic. Model of the pneumatic actuator system (transfer function) is obtained from system identification (SI) method. Next, combination of predictive functional control with observer (PFC-O) design is selected as a new control strategy for pneumatic system. Performance assessment of the controller is performed in MATLAB and validated through real-time experiments using national instrument (NI) devices and programmable system on chip (PSoC) microcontroller. Result shows that the new controller is adapted to the system and able to successfully control both simulation and real-time experiments.

P-Adaptive Neuro-Fuzzy and PD-Fuzzy controller design for position control of a modified single acting pneumatic cylinder

Pneumatic systems are widely used in the automation industry and in the field of automatic control. This work proposes two control approaches, Proportional Adaptive Neuro-Fuzzy (P-ANFIS) controller and PD-Fuzzy (PDFLC) controller for a modified single acting pneumatic cylinder position control. The design steps of each controller implemented on MATLAB/Simulink are presented. A model based on position system identification is used for the controller design. Then, the simulation results are analyzed and compared to illustrate the performance of the proposed controllers. Finally, the controllers are tested with the real plant in realtime experiment to validate the results obtained by simulation. Results show that P-ANFIS controller offer better control compared to PDFLC.

Predictive Functional Controller design for pneumatic actuator with stiffness characteristic

This paper presents a model and controller design for a pneumatic actuator system. Three control strategies of position, force and compliance control are proposed. From the compliance control, stiffness characteristic of the pneumatic actuator will be presented in real-time system. Model of the pneumatic actuator system (transfer function) is obtained from System Identification (SI) method. Next, Predictive Functional Control (PFC) with observer design is selected as the control strategy for the pneumatic system. Performance assessment of the controller is performed in MATLAB and validated through real-time experiment. Result shows that the PFC controller is adapted to the system and able to control successfully in both simulation and experiment.

System identification and predictive functional control for electro-hydraulic actuator system

Nowadays, electro-hydraulic actuator (EHA) is widely applied in industries. This study presents the modeling and development of a predictive functional control (PFC) algorithm for position control of EHA. System identification (SI) approach is used to obtain the linear transfer function of the system in discrete form. PFC is proposed based on its ability to predict the future outputs of the actual plant over the predictive horizon and computes the control effort over the control horizon at every sampling instance. Numerical simulation and real-time experiment are conducted to study the PFC performance with respect to an optimized PID controller tuned by using particle swarm optimization (PID-PSO) for several position tracking inputs. The result shows that the PFC algorithm has better performance in term of overshoot and integral absolute error (IAE) as compared to the optimized PID.

Design of unconstrained and constrained model predictive control for pneumatic actuator system: Set-point tracking

This paper presents the performance analysis of Model Predictive Controller (MPC) for the controlling of position of cylinders stroke for pneumatic actuator system. The mathematical model of the system was obtained using System Identification (SI) technique and the linear approach using AutoRegressive with Exogenous Input (ARX) was chosen as a model structure to describe the dynamic behavior and characteristics of the system. The input constraints were applied to the MPC algorithm to make the controller operates closer to the limits. In this paper, the performance of MPC in controlling the position of the pneumatic actuator system as well as tracking the set-point was compared with Proportional-Integral (PI) controller. The performance of the controllers were assessed by taking into account the percentage overshoot (%OS), settling time (ts), rise time (tr), and steady-state error (SSE) of the responses. Simulation results show the effectiveness of the MPC with constrained input in producing better transient response and minimizing SSE.

Force Control for a Pneumatic Cylinder Using Generalized Predictive Controller Approach

Pneumatic cylinder is a well-known device because of its high power to weight ratio, easy use, and environmental safety. Pneumatic cylinder uses air as its power source and converts it to a possible movement such as linear and rotary movement. In order to control the pneumatic cylinder, controller algorithm is needed to control the on-off solenoid valve with encoder and pressure sensor as the feedback inputs. In this paper, generalized predictive controller (GPC) is proposed as the control strategy for the pneumatic cylinder force control. To validate and compare the performance, proportional-integral (PI) controller is also presented. Both controllers algorithms GPC and PI are developed using existing linear model of the cylinder from previous research. Results are presented in simulation and experimental approach using MATLAB-Simulink as the platform. The results show that the GPC is capable of fast response with low steady state error and percentage overshoot compared to PI.