Parameter Identification of Nonlinear System on Combustion Engine Based MVEM using PEM

In four-stroke engine injection system, often called spark ignition (SI) engine, the air-fuel ratio (AFR) is taken from the measurement of lambda sensor in the exhaust. This sensor does not directly describe how much AFR in the combustion chamber due to the large transport delay. Therefore, the lambda sensor is used only as a feedback in AFR control "correction", not as the "main" control. The purpose of this research is to identify the parameters of the non-linear system in SI engines to produce AFR estimator. The AFR estimator is expected to be used as a feedback of the main "AFR" control system. The process of identifying the parameters using the Gauss-Newton method, due to its rapid computation to Achieve convergence, is based on prediction error minimization (PEM). The models of AFR estimator is an open-loop system without a universal exhaust gas oxygen (UEGO) sensors as feedback, called a virtual AFR sensor. The high price of UEGO sensors makes the virtual AFR sensor can be a practical solution to be applied in AFR control. The model in this research is based on the mean value engine models (MVEM) with some modifications. The research dataset was taken from a Hyundai Verna 2002 with the additional UEGO type of lambda sensors. The throttle opening angle (input) is played by stepping on the gas pedal and the signal to the injector (input) is set to a certain quantity to produce the AFR (output) value read by the UEGO sensor. This research produces an open loop estimator model or AFR virtual sensors with normalized root mean square error (NRMSE) = 0.06831 = 6.831%

CDM Based Servo State Feedback Controller with Feedback Linearization for Magnetic Levitation Ball System

This paper explains the design of Servo State Feedback Controller and Feedback Linearization for Magnetic Levitation Ball System (MLBS). The system uses feedback linearization to change the nonlinear model of magnetic levitation ball system to the linear system. Servo state feedback controller controls the position of the ball. An integrator eliminates the steady state error in servo state feedback controller. The parameter of integral gain and state feedback gains is achieved from the concept of Coefficient Diagram Method (CDM). The CDM requires the controllable canonical form, because of that Matrix Transformation is needed. Hence, feedback linearization is applied first to the MLBS then converted to a controllable form by a transformation matrix. The simulation shows the system can follow the desired position and robust from the position disturbance. The uncertainty parameter of mass, inductance, and resistance of MLBS also being investigated in the simulation. Comparing CDM with another method such as Linear Quadratic Regulator (LQR) and Pole Placement, CDM can give better response, that is no overshoot but a quite fast response. The main advantage of CDM is it has a standard parameter to obtain controller’s parameter hence it can avoid trial and error

Path Planning Based on Fuzzy Decision Trees and Potential Field

The fuzzy logic algorithm is an artificial intelligence algorithm that uses mathematical logic to solve to by the data value inputs which are not precise in order to reach an accurate conclusion. In this work, Fuzzy decision tree (FDT) has been designed to solve the path planning problem by considering all available information and make the most appropriate decision given by the inputs. The FDT is often used to make a path planning decision in graph theory. It has been applied in the previous researches in the field of robotics, but it still shows drawbacks in that the robot will stop at the local minima and is not able to find the shortest path. Hence, this paper combines the FDT algorithm with the potential field algorithm. The potential field algorithm provides weight to the FDT algorithm which enables the robot to successfully avoid the local minima and find the shortest path

Quadrotor Path Planning Based on Modified Fuzzy Cell Decomposition Algorithm

The purpose of this paper is to present an algorithm to determine the shortest path for quadrotor to be able to navigate in an unknown area. The problem in robot navigation is that a robot has incapability of finding the shortest path while moving to the goal position and avoiding obstacles. Hence, a modification of several algorithms are proposed to enable the robot to reach the goal position through the shortest path. The algorithms used are fuzzy logic and cell decomposition algorithms, in which the fuzzy algorithm which is an artificial intelligence algorithm is used for robot path planning and cell decomposition algorithm is used to create a map for the robot path, but the merger of these two algorithms is still incapable of finding the shortest distance. Therefore, this paper describes a modification of the both algorithms by adding potential field algorithm that is used to provide weight values on the map in order for the quadrotor to move to its goal position and find the shortest path. The modification of the algorithms have shown that quadrotor is able to avoid various obstacles and find the shortest path so that the time required to get to the goal position is more rapid

Hover Position of Quadrotor Based on PD-like Fuzzy Linear Programming

The purpose of this paper is to present the altitude control algorithm for quadrotor to be able to fly at a particular altitude. Several previous researchers have conducted studies on quadrotor altitude by using PID control but there are problems in the overshoot and oscillation. To optimize the control, tunning on PID algorithm must be first conducted to determine proportional and derivative constants. Hence, the paper presents altitude control modification by using PID-like fuzzy without tuning. The PID algorithm is a control algorithm for linear systems. While, system to be controlled is a non-linear, so that linearization is needed by using equilibrium. The proposed algorithm is a modification of the PID algorithm used as an altitude control which enables quadrotor to be stable when hovering. The algorithm used is not PID algorithm with tuning using fuzzy, but this is a single input single output (SISO) control PID-like fuzzy linear programming. The result of the research shows that quadrotor can hover in a rapid raise time, steady state and settling time without performing overshoot and oscillation

DC Motor Speed Control Using Hybrid PID-Fuzzy with ITAE Polynomial Initiation

DC motors are widely applied in industrial sector, especiallyprocesses of automation and robotics. Given its role in the sector, DC motor operation needs to be optimized. One of optimization steps is controlling speed using several control methods, for example conventional PID methods, PID Ziegler Nichols, PID based on ITAE polynomials, and Hybrid PID-Fuzzy. From these methods, Hybrid PID-Fuzzy was chosen as a method to be proposed in this paper because it can anticipate shortcomings of PID controllers and fuzzy controllers so as to produce system responses that are fast and adaptive to errors. This paper aimed to design a Hybrid PID-Fuzzy system based on ITAE polynomials (Hybrid-ITAE), to analyze its performance parameters values, and tp compare Hybrid-ITAE performance with conventional PID method. Working parameters being reviewed include overshoot, rise time, settling time, and ITAE. First of all, JGA25-370 DC motor was modeled in a form of a third order transfer function equation. Based on the transfer function, PID parameters were calculated using PID Output Feedback and ITAE polynomial methods. The best ITAE polynomial PID controllers were then be combined with a Fuzzy Logic Controller to form a Hybrid-ITAE system. Simulation and experimental stages were carried out in two conditions, namely no load and loaded. Simulation and experimental results showed that Hybrid-ITAE (l = 0.85) was the best controller for no-load simulation conditions. For loaded simulation Hybrid-ITAE (l=1) was a better controller. In no-loads experiment, the best controller was Hybrid PID-Ziegler Nichols, while for loaded condition the best controller was Hybrid PID-Ziegler Nichols

Formation Pattern Based on Modified Cell Decomposition Algorithm

The purpose of this paper is to present the shortest path algorithm for Quadrotor to make a formation quickly and avoid obstacles in an unknown area. There are three algorithms proposed in this paper namely fuzzy, cell decomposition, and potential field algorithms. Cell decomposition algorithm is an algorithm derived from graph theory used to create maps of robot formations. Fuzzy algorithm is an artificial intelligence control algorithm used for robot navigation. The merger of these two algorithms are not able to form an optimum formation because some Quadrotors which have been hovering should wait for the other Quadrotors which are unable to find the shortest distance to reach the formation quickly. The problem is that the longer time the multi Quadrotors take to make a formation, the more energy they use. It can be overcome by adding potential field algorithm. The algorithm is used to give values of weight to the path planning taken by the Quadrotors. The proposed algorithms have shown that multi Quadrotors can quickly make a formation because they are able to avoid various obstacles and find the shortest path so that the time required to get to the goal position is fast

Attitude Control of Quadrotor Using PD Plus Feedforward controller on SO(3)

This paper proposes a simple scheme of Proportional-Derivative (PD) plus Feedforward controller on SO(3) to control the attitude of a quadrotor. This controller only needs the measurement of angular velocity to calculate the exponential coordinates of the rotation matrix. With rotation matrix as an error variable of the controller, the simulation shows that the controller is able to drive the attitude of the quadrotor from hovering condition to desired attitude and from an attitude condition goes to the hovering condition, despite the system is disturbed. When the system is convergent, the rotation error matrix will be a 3x3 identity matrix

Cell Balancing On Three- Cell Lithium Polymer Batteries Connected In Series

Electric vehicle becomes popular recently, particularly in Indonesia. One of the most important and crucial components in an electrical vehicle is the battery. BMS (Battery Management System) is a system to monitor and regulate the performance of the battery resulting in effective-efficient-durable performance. Usually, BMS is needed to prevent battery from system failure. One of the problems that normally happens in a multi-cell battery and causing system failure is voltage unbalance. In this study, the system is designed so it can monitor the voltage condition of the three battery’s cells in series circuit and manage to balances it. The process of balancing the value of the voltage at the battery cells is known as cell balancing. The method used in this study is by using passive shunt resistor balancing method. In this method, an electronic circuit is designed in order to balance the value of the voltage at the battery cells using resistors to remove excess voltage. The result shows that the electrical circuit is capable to balance the voltage of each cell. Moreover, the designed circuit is monitored by software so it can perform in flexible manner