Nonlinear proportional integral controller with adaptive interaction algorithm for nonlinear activated sludge process

Wastewater Treatment Plant (WWTP) is highly complex with the nonlinearity of control parameters and difficult to be controlled. The need for simple but effective control strategy to handle the nonlinearities of the wastewater plant is obviously demanded. The thesis emphasizes on multivariable model identification and nonlinear proportional integral (PI) controller to improve the operation of wastewater plant. Good models were resulted by subspace method based on N4SID algorithm with generated multi-level input signal. The nonlinear PI controller (Non- PI) with adaptive rate variation was developed to accommodate the nonlinearity of the WWTP, and hence, improving the adaptability and robustness of the classical linear PI controller. The Non-PI was designed by cascading a sector-bounded nonlinear gain to linear PI while the rate variation is adapted based on adaptive interaction algorithm. The effectiveness of the Non-PI has been proven by significant improvement under various dynamic influents. In the process of activated sludge, better average effluent qualities, less number and percentage of effluent violations were resulted. Besides, more than 30% of integral squared error and 14% of integral absolute error were reduced by the Non-PI controller compared to the benchmark PI for dissolved oxygen control and nitrate in nitrogen removal control, respectively

Instruction Set Extension of a Low-End Reconfigurable Microcontroller in Bit-Sorting Implementation

The microcontroller-based system is currently having a tremendous boost with the revelation of platforms such as the Internet of Things. Low-end families of microcontroller architecture are still in demand albeit less technologically advanced due to its better I/O better application and control. However, there is clearly a lack of computational capability of the low-end architecture that will affect the pre-processing stage of the received data. The purpose of this research is to combine the best feature of an 8-bit microcontroller architecture together with the computationally complex operations without incurring extra resources. The modules’ integration is implemented using instruction set architecture (ISA) extension technique and is developed on the Field Programmable Gate Array (FPGA). Extensive simulations were performed with the and a comprehensive methodology is proposed. It was found that the ISA extension from 12-bit to 16-bit has produced a faster execution time with fewer resource utilization when implementing the bit-sorting algorithm. The overall development process used in this research is flexible enough for further investigation either by extending its module to more complex algorithms or evaluating other designs of its components

Application Specific Instruction Set Processor (ASIP) Design in an 8-bit Softcore Microcontroller

The microcontroller-based system is currently having a tremendous boost in demand in line with the Industrial Revolution 4.0. Although more applications seem to concentrate on software algorithms and wireless connectivity, the hardware side of the system is still occupied by microcontroller variants. With huge alternatives being offered to setup a microcontroller system, having a softcore microcontroller is extremely beneficial especially when considering the rapid advancement in computer technology. Although the 8-bit microcontroller has less computational capability compare to other high-end microcontroller families, it has an advantage in low code density for I/O application and control. The purpose of this research is to combine the best feature of the 8-bit architecture together with efficient arithmetic operations in the implementation of moving average filter. The modules’ integration is implemented using ASIP design without occurring extra board space and is developed using the Field Programmable Gate Array (FPGA) as the single chip solutions. It was found that the revised microcontroller architecture has produced a faster execution time and similar maximum frequency when benchmarked with its predecessor. The overall ASIP design procedures used in this research provides flexibility for further development, either by extending its module to incorporate more complex algorithms or by upgrading current designs of its components

Singularly perturbation method applied to multivariable PID controller design

Proportional integral derivative (PID) controllers are commonly used in process industries due to their simple structure and high reliability. Efficient tuning is one of the relevant issues of PID controller type. The tuning process always becomes a challenging matter especially for multivariable system and to obtain the best control tuning for different time scales system. This motivates the use of singularly perturbation method into the multivariable PID (MPID) controller designs. In this work, wastewater treatment plant and Newell and Lee evaporator were considered as system case studies. Four MPID control strategies, Davison, Penttinen-Koivo, Maciejowski, and Combined methods, were applied into the systems. The singularly perturbation method based on Naidu and Jian Niu algorithms was applied into MPID control design. It was found that the singularly perturbed system obtained by Naidu method was able to maintain the system characteristic and hence was applied into the design of MPID controllers. The closed loop performance and process interactions were analyzed. It is observed that less computation time is required for singularly perturbed MPID controller compared to the conventional MPID controller. The closed loop performance shows good transient responses, low steady state error, and less process interaction when using singularly perturbed MPID controller

The Effect of Overlapping Spread Value for Radial Basis Function Neural Network in Face Detection

In this paper, the effect of overlapping spread value for Radial Basis Function Neural Network (RBFNN) in face detection is presented. The reason for taking the overlapping factor into consideration is to optimize the results for using variance spread value. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. General preprocessing approach was used for normalizing the image and a Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face images. RBFNN offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and spread of the RBF. The performance of the RBFNN face detection system will be based on the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) criteri

A Smart Monitoring Of A Water Quality Detector System

The importance to monitor the water quality level is undeniable due to significant impact to human health and ecosystem. The project aims to develop a wireless water quality monitoring system that aids in continuous measurements of water conditions based on pH and turbidity measurements. These two sensors are connected to microprocessor and transmitted to the database by using a Wi-Fi module as a bridge. The developed system was successfully detect both the pH and turbidity values hence updating in IoT platform. Based on the results obtained, the test water sample can be classified to class IIB which is suitable for water recreational used body contact. Overall, the developed system offers fast and easy monitoring of pH and turbidity levels with IoT application for continuous maintenance of clean water. The work is just concern on the physical water parameters hence further extend to chemical parameter for verifying a better result in measuring the WQI value

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

Modern and intelligent controller for a magnetic bearing system

A magnetic bearing system is a device that uses electromagnetic forces to support a rotor without mechanical contact. The focus of this project will be on the stability and control of the MBC 500 system test bed constructed by Magnetic Moments Incorporated. The MBC 500 system contains a stainless steel shaft or rotor, which can be levitated using eight horseshoe electromagnets, four at each end of the rotor. A controller, which is able to stabilize the position of the rotor by varying the electromagnet force, f produced by the electromagnets at the end of the shaft, will be designed. For this purpose, the formulation of the mathematical dynamic model of magnetic bearing system is derived initially and it was followed by establishing the state space model of the system. Then, system model is linearized at the equilibrium point using a Taylor Series and the shaft is assumed as a rigid body. In addition, a state feedback controller using a pole placement technique and a fuzzy logic controller as an alternative control strategy are designed. This project will be implemented using MATLAB 6.