A component-oriented programming framework for developing embedded mobile robot software using PECOS model

A practical framework for component-based software engineering of embedded real-time systems, particularly for autonomous mobile robot embedded software development using PECOS component model is proposed The main features of this framework are: (1) use graphical representation for components definition and composition; (2) target C language for optimal code generation with small micro-controller; and (3) does not requires run-time support except for real-time kernel. Real-time implementation indicates that, the PECOS component model together with the proposed framework is suitable for resource constrained embedded systems

Improved Event-Based Pi Controller for Limit Cycles Avoidance

One of the issues in designing event-based proportional-integral (PI) controller using aggressive tuning rules is the possible occurrence of limit cycles. To date, there is no adequate simple event-based PI controller technique able to explicitly use aggressive tuning rule. In this paper an improved simple event-based PI controller is proposed to address this issue. By analysing the discrete PI algorithm, an improved triggering condition is introduced. To test the effectiveness of the approach, extensive simulations are carried out by introducing the proposed method to process control under various sampling period, triggering limit, and different tuning rules namely, AMIGO, SIMC and One-Third tuning rules. The performances are evaluated based on two standard criteria: ability to imitate the time-triggered system and computation load reduction.  The results show that the performance of the proposed method able to surpass others simple event-based PI controller approaches by giving a closest response to the time-triggered system, lowest computational load and able to avoid the limit cycles occurrences. It is envisaged that the proposed method can be useful in designing a simple event-based PI controller that compatible with any type of tuning rules

Modelling and Simulation for Industrial DC Motor Using Intelligent Control

AbstractThis paper presents an overview of Proportional Integral control (PI) and Artificial Intelligent control (AI) algorithms. AI and PI controller are analyzed using Matlab [Simulink] software. The DC motor is an attractive piece of equipment in many industrial applications requiring variable speed and load characteristics due to its ease of controllability. The main objective of this paper illustrates how the speed of the DC motor can be controlled using different controllers. The simulation results demonstrate that the responses of DC motor with an AI control which is Fuzzy Logic Control shows satisfactory well damped control performance. The results shows that Industrial DC Motor model develop using its physical parameters and controlled with an AI controller give better response, it means it can used as a controller to the real time DC Moto

The Effectiveness of Pole Placement Method in Control System Design for an Autonomous Helicopter Model in Hovering Flight

This paper presents the results of attitude, velocity, heave and yaw controller design for an autonomous model scaled helicopter using identified model of vehicle dynamic from parameterized state-space model with quasi-steady attitude dynamic approximation (6 Degree of Freedom model). Multivariable state-space control methodology such as pole placement was used to design the linear state-space feedback for the stabilization of helicopter because of its simple controller architecture. The design specification for controller design was selected according to Military Handling Qualities Specification ADS-33C. Results indicate that acceptable controller can be designed using pole placement method with quasi-steady attitude approximation and it has been shown that the controller design was compliance with design criteria of hover requirement in ADS-33C

Robust human detection with occlusion handling by fusion of thermal and depth images from mobile robot

In this paper, a robust surveillance system to enable robots to detect humans in indoor environments is proposed. The proposed method is based on fusing information from thermal and depth images which allows the detection of human even under occlusion. The proposed method consists of three stages, pre-processing, ROI generation and object classification. A new dataset was developed to evaluate the performance of the proposed method. The experimental results show that the proposed method is able to detect multiple humans under occlusions and illumination variations

Dynamic characterisation of a two-link flexible manipulator: theory and experiments

This paper presents theoretical and experimental investigations into the dynamic modelling and characterisation of a two-link flexible manipulator incorporating payload. A planar two-link flexible manipulator that moves in a horizontal plane is considered. A dynamic model of the system is developed using a combined Euler-Lagrange and assumed mode methods, and simulated using Matlab. Experiments are performed on a lab-scaled two-link flexible manipulator for validation of the dynamic model and characterisation of the system. Two system responses namely hub angular position and deflection responses at both links are obtained and analysed in time and frequency domains. The effects of payload on the dynamic characteristics of the flexible manipulator are also studied and discussed. The results show that a close agreement between simulation and experiments is achieved demonstrating an acceptable accuracy of the developed model

Modified Predictive Control for a Class of Electro-Hydraulic Actuator

Many model predictive control (MPC) algorithms have been proposed in the literature depending on the conditionality of the system matrix and the tuning control parameters. A modified predictive control method is proposed in this paper. The modified predictive method is based on the control matrix formulation combined with optimized move suppression coefficient. Poor dynamics and high nonlinearities are parts of the difficulties in the control of the Electro-Hydraulic Actuator (EHA) functions, which make the proposed matrix an attractive solution. The developed controller is designed based on simulation model of a position control EHA to reduce the overshoot of the system and to achieve better and smoother tracking. The performance of the designed controller achieved quick response and accurate behavior of the tracking compared to the previous study

Non-parametric identification techniques for intelligent pneumatic actuator

The aim of this paper is to present experimental, empirical and analytic identification techniques, known as non-parametric techniques. Poor dynamics and high nonlinearities are parts of the difficulties in the control of pneumatic actuator functions, which make the identification technique very challenging. Firstly, the step response experimental data is collected to obtain real-time force model of the intelligent pneumatic actuator (IPA). The IPA plant and Personal Computer (PC) communicate through Data Acquisition (DAQ) card over MATLAB software. The second method is approximating the process by curve reaction of a first-order plus delay process, and the third method uses the equivalent n order process with PTn model parameters. The obtained results have been compared with the previous study, achieved based on force system identification of IPA obtained by the (Auto-Regressive model with eXogenous) ARX model. The models developed using non-parameters identification techniques have good responses and their responses are close to the model identified using the ARX system identification model. The controller approved the success of the identification technique with good performance. This means the Non-Parametric techniques are strongly recommended, suitable, and feasible to use to analyze and design the force controller of IPA system. The techniques are thus very suitable to identify the real IPA plant and achieve widespread industrial acceptance