In this thesis we propose a robust PID controller to regulate the hydrogen flow of proton exchange membrane fuel cells. The controller allows the so-called hydrogen excess ratio to track a desired value rapidly in order to achieve saving hydrogen and to avoid damage of the fuel cell when the power output of the fuel cell varies from one level to another. The fuel cell system is governed by a set of complicated nonlinear dynamical equations. To ease the control design task, we model the system, at each operating point, as a feedback interconnection of a linear time-invariant nominal part with a norm-bounded perturbation. We use the technique of system identification to acquire the transfer function representation of the nominal part and the size of the perturbation. To do this, the chirp signal is adopted to excite the system and the observed response is analyzed using spectral analysis to obtain the model. Based on the model, a $H_{infty}$ PID controller is designed for the fuel cell system. The design is tested on an experimental platform. The experimental results verify that the proposed controller can regulate the hydrogen excess ratio rapidly under load variation, and effectively reject the influence of external disturbances.

For a long time determining the stability issue of characteristic polynomials has played avery important role in Control System Engineering. This thesis addresses the traditionalcontrol issues such as stabilizing a system with any certain controller analyzingcharacteristic polynomial, yet a new perspective to solve them. Particularly, in this thesis,Proportional-Integral-Derivative (PID) controller is considered for a fixed structuredcontroller. This research aims to attain controller gain set satisfying given performancespecifications, not from the exact mathematical model, but from the empirical data of thesystem. Therefore, instead of a characteristic polynomial equation, a speciallyformulated characteristic rational function is investigated for the stability of the systemin order to use only the frequency data of the plant. Because the performance satisfactionis highly focused on, the characteristic rational function for the investigation of thestability is mainly dealt with the complex coefficient polynomial case rather than realone through whole chapters, and the mathematical basis for the complex case is prepared.For the performance specifications, phase margin is considered first since it is avery significant factor to examine the system’s nominal stability extent (nominal performance). Second, satisfying H norm constraints is handled to make a more robustclosed loop feedback control system. Third, we assume undefined, but bounded outsidenoise, exists when estimating the system’s frequency data. While considering theseuncertainties, a robust control system which meets a given phase margin performance, isattained finally (robust performance).In this thesis, the way is explained how the entire PID controller gain setssatisfying the given performances mentioned in the above are obtained. The approachfully makes use of the calculating software e.g. MATLAB® in this research and isdeveloped in a systematically and automatically computational aspect. The result ofsynthesizing PID controller is visualized through the graphic user interface of acomputer.

Magnetic suspension systems are highly nonlinear and essentially unstable systems. In this thesis, we utilize a phase-lead controller operating in the inner loop to stabilize the magnetic suspension system at first. Furthermore, we design a fuzzy PID controller operating in the outer loop to overcome the nonlinearity and to improve the system¡¦s performances. Because of setting the parameters in traditional fuzzy PID is a long-winded trial and error, so we adopt non-binary modified adaptive genetic algorithms to help us finding the parameters of fuzzy PID controller. As to the experimental implementation, we set two situations in our experiment test: (1) we utilize fuzzy PID controller with initial voltage to test the positions control, and eliminate the extra disturbance. And, (2) we utilize fuzzy PID controller without initial voltage to control the position of suspension object. For the experimental results, we obtain that the designed fuzzy PID controller not only increases the system¡¦s operating range, but also positions accurately and rapidly, and it meanwhile can eliminate the extra disturbance.

ABSTRACT In this dissertation, several adaptive control design schemes for a class of nonlinear systems are proposed. The first topic of the research is concerned with self-tuning PID controller design. The main problem of designing PID controller is how to determine the values of three control gains, i.e., proportional gain , integral gain , and derivative gain . We attempt to use the technique of adaptive control based on the Lyapunov approach to design the PID controller for some class of partially known nonlinear systems. Three PID control gains are adjusted on-line such that better output performance can be achieved. The stability of the closed-loop PID control systems is analyzed and guaranteed by introducing a supervisory control and a modified adaptation law with projection. Second, two kinds of adaptive neural control systems including the direct and indirect neural controls are considered by using simple single auto-tuning neuron. We will first propose a novel neuron called auto-tuning neuron and use it to take place of the roles of the traditional neural networks used in the direct and indirect adaptive neural control systems. This can greatly reduce the computational time and network complexities due to the simple configuration of the auto-tuning neuron. It is also easy for hardware implementation. Third, based on the idea borrowed from natural evolution, genetic algorithm can search for optimal or near-optimal solutions for an optimization problem over the search domain. An optimization technique of real-coded genetic algorithm is used to design the PID controller by minimizing the performance index of integrated absolute error. The improvements of our results over that using other methods are also illustrated. In the last part of each section, some computer simulation results will also be provided to illustrate our proposed methods.

In position control systems like linear motor, friction is a key factor to influence the control performance when micron or sub-micron meter accuracy is required. To overcome the effect of the friction, besides a general model of the linear motor system, past researches have shown an additional static friction model of the system is necessary for a better control performance when the motor move into the micro region of the system (usually <100£gm). Two models, macro and micro model of the system have been well constructed by two different identification methods. After model construction, two different controllers are also designed for each model. A traditional pole-placement PID controller can be easily obtained for the macro model to move into the micro region quickly and stably. Then in micro model design, from the experiments, it is found that system parameter varies and thus degrades the positioning performance of the system. So, a Sliding-Mode Controller is designed to improve these problems. With a two step control strategy, macro and micro step, the linear motor positioning system can achieve a 0.1£gm accuracy within 0.2 sec.

In this study, the non-binary coding, elitist strategy, increasing mutation rate, extinction, and immigration strategy are used to improve the simple genetic algorithms. The improved search technique can reduce the possibility of falling into the local optimum due to the premature convergence in a large searching space, and increase the chance of finding out the near-optimal parameters. The hydraulic forming machine used in this thesis, includes a power source of a hydraulic motor and a actuator of two hydraulic cylinders. Both the internal pressure and axial force are controlled to hydroform the tubes into the shapes we want. The PID fuzzy logic controller is implemented to control the proportional direction valve and pressure reducing valve of this dual-cylinder electro-hydraulic system so that the loading path can follow the optimal forming curve of axial-feeding and pressure prescribed. From the experimental results, it is clear that the near-optimal PIDFLC controller designed via modified genetic algorithms can make the loading path follow the prescribed curve, and effective for reducing system uncertainty caused by the varying loads and system unstability resulting from the nonlinear characteristics of the hydraulic system.

The main focus of about this thesis is to design an anti-interference depth controller for underwater remotely operated vehicles(ROV). Since the underwater remotely operated vehicle experiences combination effects of nonlinearities, uncertain and time-varying parameters, and unknown disturbances, demand of robustness for the controller needs to be extremely strict. Therefore, an anti-interference depth controller using PID control and Sliding-mode control is developed. The Matlab simulation tool is employed to simulate the depth control performance of the behavior of the ROV. The simulation is also considered about the model uncertainty of ROV.

Zugl.: Dresden, Techn. Univ., Diss., 2009

This thesis is an ongoing project of Ericsson with collaboration of Blekinge Institute of Technology [BTH], and Linneaus University [LNU] to compare the functionality and performance of three controllers Polynomial Pole Placement, PID [Proportional Integral Derivative] and PD controller in third order. This paper presents the state space modeling approach of DC-DC Buck converter. The main aim of this thesis is, by considering the buck converter system of Ericsson BMR450 with the PID, POLYNOMIAL and PD controllers at feedback loop, thus running their Matlab file with their appropiate Simulink block diagram, and comparing the three controllers performance by verifying their appropiate output graphs. The third order controller design is complicated and response is slow. The second order design is easy and gives better responses than third order Polynomial, PID and PD controllers. / As per the results point of view, the polynomial performed well than PID and PD controllers. The simulations show that the polynomial controller reaches the reference voltage very well, were the PID and PD result does not differ very much while meeting with the required reference voltage. Thus we conclude that the Polynomial controller design and results were better than the PID and PD Controllers. If we compare both the second order [4] and third order controller methods, The second order controllers are easy in design and gives better responses than third order polynomial PID and PD controllers. / ERCS.MADHU KIRAN, D.NO: 1/1/131, B.C.COLONY, MUTHUKUR, NELLORE, ANDHRA PRADESH, INDIA. PIN - 524344. THOTA. Partha Saradhi, C/O CH SUVARNA RAJU D.NO: 4-5-47, VEGIVARI CHAVADI, KOTHA PETA, WARD NO:21, KOVVUR, WEST GODAVARI,ANDHRA PRADESH, INDIA PIN - 534350,

This research focuses primary on recognition of lane-change behaviors using support vector machines (SVMs). Previous research and statistical results show that the vast majority of motor vehicle accidents are caused by driver behavior and errors. Therefore, the interpretation and evaluation of driver behavior is important for road safety analysis and improvement. The main limit to understanding driver behavior is the data availability. In particular, a full-scale lane-change data set is difficult to collect in a real traffic environment because of the safety and cost issues. Considering the data demands of the recognition model development and the obstacles of field data collection, data were collected from two aspects: simulation data and the field data. To obtain field data, an in-vehicle data recorder (IVDR) that integrates a Global Positioning System (GPS) and Inertial Measurement Unit (IMU) are developed to collect data on speed, position, attitude, acceleration, etc. To obtain simulation data, a lane-change simulation with a speed controller and a trajectory tracking controller with preview ability were developed, and sufficient lane-change data were generated. Proportional-Integral-Derivative (PID) control is applied to the speed controller and trajectory tracking controller. Simulation data were divided into two classes: dual lane-change data and single lane-change data; field data were further divided as single lane-change and non-lane-change data. Two-class and three-class classification SVM model are trained by simulation data and field data, and the model parameters were optimized by Genetic Algorithm (GA). A radial basis function and polynomial kernel functions were found that suitable for this recognition task. The recognition results indicate that, the SVM model trained by simulation data and non-lane-change data can correctly classify up to 85 percent of single lane-change field data.