Sharatul Izah Samsudin

Nonlinear PI Control with Adaptive Interaction Algorithm for Multivariable Wastewater Treatment Process

The wastewater treatment plant (WWTP) is highly known with the nonlinearity of the control parameters, thus it is difficult to be controlled. In this paper, the enhancement of nonlinear PI controller (ENon-PI) to compensate the nonlinearity of the activated sludge WWTP is proposed. The ENon-PI controller is designed by cascading a sector-bounded nonlinear gain to linear PI controller. The rate variation of the nonlinear gain is automatically updated based on adaptive interaction algorithm. Initiative to simplify the ENon-PI control structure by adapting has been proved by significant improvement under various dynamic influents. More than 30% of integral square error and 14% of integral absolute error are reduced compared to benchmark PI for DO control and nitrate in nitrogen removal control. Better average effluent qualities, less number of effluent violations, and lower aeration energy consumption resulted.

Tracking performance and disturbance rejection of pneumatic actuator system

This paper investigates several control strategies that potential to perform well in regulating and tracking set point of pneumatic actuator system and able to reject disturbance. The system consists of 5-port proportional valve with the dead-band flow and double rod cylinders that exhibit significant friction. Two control strategies of PID and NPID controllers with four different configurations with and without dead-zone compensators (DZC) are simulated. Three different input signals including step, sinusoidal and random waveforms are used to evaluate the performance of the proposed techniques. The effectiveness of NPID+DZC has been successfully demonstrated and proved through simulation and experimental studies.

Application of optimization technique for PID controller tuning in position tracking of pneumatic actuator system

In this paper, two optimization techniques of Particle Swarm Optimization (PSO) and Firefly Algorithm (FA) is used to obtain the optimal PID control parameters. To represent the model of the system, system identification with ARX model structure is developed. The results are determined by analysis the step response characteristic of the system. It was observed that the performances of PID controller with PSO optimized parameters perform well in position tracking of the pneumatic actuator system.

Comparison of NARX neural network and classical modelling approaches

Classical optimization tools are effective when precise mechanistic models exist to support their design and implementation. However, most of the real-world processes are complex due to either nonlinearities or uncertainties (or both) and environmental variations, thus making realizing accurate mathematical models for such processes quite difficult or often impossible. Black box approach tends to present a better alternative in such situations. This paper presents a comparison of nonlinear autoregressive with eXogenous (NARX) neural network and traditional modelling techniques [autoregressive with exogenous input (ARX) and autoregressive moving average with exogenous input (ARMAX)]. The models were validated using experimental data from full-scale plants. Simulation results revealed that the performance of the NARX neural network is better compared to the ARMAX and ARX. The NARX neural network may serve as a valuable forecasting tool for the plants.

PWM controller design of a hexapod robot using FPGA

Motor control mechanism on a robotic platform has been dominated by microcontroller-based system for the past decade. With the advancement of the reconfigurable hardware platforms such as field programmable gate array (FPGA), such control mechanism is being ported to the platform in order to improve efficiency and achieves higher performance. This paper presents the FPGA implementation of the servomotor control technique which is applied for a six-legged robot platform. The hexapod robot consisted of 18 continuous servomotors with 3 servos on each leg. Each of the servomotors is assigned with single pulse width modulation (PWM) output and could be individually controlled by the main controller. To enable smooth movement of the hexapod, all the PWM output are synchronized based on a simple tripod gait movement. The servo motor controller is implemented in the Spartan-3 FPGA chip and the hardware design is described in Verilog Hardware Description Language (HDL). The controller design is simulated and verified using Xilinxs ISE Simulator (ISim). Initial hardware implementation also has been conducted on several basic movements on the hexapod such as standing-up, forward and backward movement. Overall, the implementation of the servomotor controller in an FPGA has offered several advantages in terms of circuit design flexibility and simultaneous command executions when compared to conventional microcontroller-based system.

Two-time scales matrix decomposition for wastewater treatment plant

The purpose of this work is to identify two-time scales matrix decomposition of wastewater treatment plant. Wastewater plant is naturally aim to remove suspended substances, organic material and phosphate before releasing to recipients. Initially, the MIMO system is excited with generalized binary noise signals and estimated with robust numerical subspace state-space system identification. The performance of identified models is then validated by variance accounted for. Next, the block diagonalisation procedures are implemented in decoupling the system with two-time property into slow and fast subsystem. It was observed that the identified model possess two-time scales behaviors presented by separated clusters of eigenvalues. Besides, similar dynamic responses were obtained by decoupled systems compared to actual full model at lower frequency that is highly demanded in control design application. The study leads to future improvement on wastewater control strategies.

Development of fuzzy logic controller on a magnetic bearing system

A magnetic bearing system is a device that uses electromagnetic forces to support a rotor without mechanical contact. The force exerted on the rotor by an active magnetic bearing is determined by the current flow in the magnet coil. 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 forces produced by the electromagnets at the end of the shaft will be developed. Here, a rotor is assumed as a rigid body. A fuzzy logic controller as an alternative control strategy using Sugenos inference method will be designed in controlling and stabilizing the position of the rotor for the system.

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.

Application of adaptive decentralized PI controller for activated sludge process

This paper presents the application of adaptive decentralized PI controller to nonlinear activated sludge wastewater treatment plant (WWTP). Tuning of WWTP is a challenging task due to the variation and the high uncertainty of the parameters. Thus, a simple tuning method is applied in satisfying straighten effluent quality and hence optimizing the nitrogen removal of the plant. The PI controller parameters are adjusted directly by updating algorithm developed based on adaptive interaction algorithm theory. It was observed that the decentralized PI with approximate Frechet tuning algorithm offers an attractive tuning task for multivariable WWTP with improvement in energy saving and effluent violations of Benchmark Simulation Model No. 1.

Multivariable PID controllers for dynamic process

This paper is concerned with the design of a dynamic multivariable PID control for multi input multi output (MIMO) process. Four multivariable PID control schemes using Davison, Penttinen-Koivo, Maciejowski and a combined method were applied. The controller parameters for all control strategies were designed based on dynamic condition using singularly perturbed system. The purpose of the study is to investigate the effectiveness in the performance of dynamic control based on different multivariable PID control strategies. To attain the best result, numerous tuning parameters were tested. The simulation results show the significance of the study whereby the proposed dynamic MPID control scheme shows better improvement in control tuning of nonlinear system.