VIBRATION CONTROL OF A GANTRY CRANE SYSTEM USING DYNAMIC FEEDBACK SWING CONTROLLER

The use of gantry crane system for transporting payload is very common in industrial application. However, moving the payload using the crane is not easy task especially when strict specifications on the swing angle and on the transfer time need to be satisfied. To overcome this problem a dynamic feedback swing controller is designed for the gantry position and speed, as well as the load angle and angular velocity using PID controller. Simulated responses of the position of the trolley and sway angle of the mass are presented using MATLAB. The performance of the Bangbang torque input function and the feedback swing controller are compared. From the simulation results, satisfactory vibration reduction of a crane system has been achieved using the proposed method

Fuzzy logic control of centralized chilled water system

Fuzzy logic controller has been proven to control nonlinear process system and HVAC is a type of nonlinear process systems. This paper studies the performance of fuzzy logic controller with three and five term membership function in centralized chilled water system. Three different cases are simulated and analyzed for both type of controllers. Results show that the performances between both controllers are almost similar with no significant difference. It is also encountered that in certain cases, 3-mf fuzzy logic controller outperformed 5-mf fuzzy logic controller

Load Dependence Behavior Of The Thermoelectric Module Energy Harvesting System By Inverse Dynamic Analysis–Maximum Power Point Tracking

This paper presents the characterization behavior of a thermoelectric module (TEM) analysis by the implementation of a new inverse dynamic analysis–maximum power point tracking (IDA-MPPT), with different load resistance values to the common maximum power point circuit (MPPC) controller. In this study, a conventional TEM test unit measurement is performed consists of a hotplate and a TEM module. The two MPPC boards are fed with the harvested voltage from the TEM module and a supercapacitor is linked with it. The load resistor is then fed with the boosted voltage of the second board. The three phases experimental analysis is introduced in a closed loop manner. There are, TEM system analysis without the IDA-MPPT implementation, TEM system analysis with IDA-MPPT implementation, and a newly developed IDA-MPPT unit experimental analysis. Results have shown that the TEM system with IDA-MPPT implementation with 1kΩ resistor is capable of achieving 5V output stable line voltages with 83.33% efficiency. In conclusion, with the input stable voltage of the MPPC board from the new input –shaping process, a satisfactory load behavior is achievable by its voltage readings

Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful enough to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge of data driven FDD is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralised chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data- driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data- driven FDD: the rooms' temperature, TTCx, supplied chilled water temperature, TCHWS, supplied chilled water flow rate, VCHWS, and supplied cooled water temperature, TCWS. Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6% - 3.4% differed fromthe original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy

Data-driven fault detection and diagnosis for centralised chilled water air conditioning system

The air conditioning system is complex and consumes the most energy in the building. Due to its complexity, it is difficult to identify faults in the system immediately. In this project, fault detection and diagnosis system using decision tree classifier model was developed to detect and diagnose faults in a chilled water air conditioning system. The developed model successfully classified normal condition and five common faults for more than 99% accuracy and precision. A graphical user interface of the system was also developed to ease the user

Solar energy harvester for pet GPS collar

The power source of wireless technology depends on the device’s battery life and need to be plugged in for recharge purpose. This problem can be solved using energy harvesting system which directly converts solar energy radiated from the sun into electricity. In this project a solar energy harvesting pet collar is developed to harness solar energy and charge the installed battery. The GPS system used a minimum of 2.2V up to 3.6V input voltage. The result obtained shows that the solar panel can give enough power to power up the GPS system as that energy harvester circuit and is able to deliver output up to 4.3V in direct sunlight with an input voltage as low as 3.25V

Pneumatic Positioning Control System Using Constrained Model Predictive Controller: Experimental Repeatability Test

Most of the controllers that were proposed to control the pneumatic positioning system did not consider the limitations or constraints of the system in their algorithms. Non-compliance with the prescribed system constraints may damage the pneumatic components and adversely affect its positioning accuracy, especially when the system is controlled in real-time environment. Model predictive controller (MPC) is one of the predictive controllers that is able to consider the constraint of the system in its algorithm. Therefore, constrained MPC (CMPC) was proposed in this study to improve the accuracy of pneumatic positioning system while considering the constraints of the system. The mathematical model of pneumatic system was determined by system identification technique and the control signal to the valves were considered as the constraints of the pneumatic system when developing the controller. In order to verify the accuracy and reliability of CMPC, repetitive experiments on the CMPC strategy was implemented. The existing predictive controller, that was used to control the pneumatic system such as predictive functional control (PFC), was also compared. The experimental results revealed that CMPC effectively improved the position accuracy of the pneumatic system compared to PFC strategy. However, CMPC not capable to provide a fast response as PF

A Framework For Chili Fruits Maturity Estimation Using Deep Convolutional Neural Network

An agriculture robot has been demanded in recent years. Inaccurate in estimating the maturity of the chili always happens since the human eyes are tend to prone to errors. Serving an effective, innovative, feasible chili recognition system would help farmers as economical alternative by reducing the workloads while increasing fruit yield. Hence, a comprehensive framework of chili maturity estimation using deep learning is carried out

Control of a non-holonomic mobile robot

Non-holonomic system is a mechanical system that is subject to non-holonomic constraints. They are the constraints on the velocity of the system which can not be integrated into position constraints that can be used to reduce the number of generalized coordinates. Mobile robots constitute a typical example of non-holonomic systems. In this project, the application of two different types of kinematics controller are examined and analysed. Both of the controllers are using Lyapunov method which is the simplest and successful method in kinematics stabilization. The first controller guarantees to be global asymptotically stable tracking control and the second controller guarantees to be asymptotically stable tracking control. Both controllers are able to control a non-holonomic mobile robot to track the desired trajectory. All simulations are performed using SIMULINK/MATLAB

Solving Tracking Problem of a Nonholonomic Wheel Mobile Robot Using Backstepping Tecnique

Abstract