Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Gas turbine control and load sharing of a shipboard power system

The objective of this research is to design a controller for a gas turbine of an Electric Shipboard Power System (ESPS) and to develop a load sharing strategy for its energy management. A suitable model for the gas turbine is selected and the effects of the dynamics are investigated for the different loads of the ESPS. The gas turbine controller is a Proportional Integral Derivative (PID) controller, whose parameters are tuned using the Particle Swarm Optimization (PSO) technique. The load on the system has three components: a propulsion load, a pulsed load to simulate a high energy weapon system and a power supply load for the remaining loads such as pumps, lighting systems, etc. Load sharing is inevitable when demand exceeds the available power supply. In this case, based on the priorities of the loads and the available power, a strategy is presented to supply power to the most critical loads. To illustrate this, a load allocation algorithm is developed using stateflow diagrams. The potential of this algorithm is demonstrated by two case studies performed using the three loads, with the highest priority assigned to the propulsion load in case 1, and power supply load in case 2. The results of this research can be further extended to real time applications

Hybrid optimization techniques based automatic artificial respiration system for corona patient

Artificial ventilation is widely used for various respiratory problems of human beings. The oxygen level of the corona patients has to be maintained for smooth breathing which is very difficult. For achieving this state, the air pressure should be controlled in the respiration system that has a piston mechanism driven by a motor. An Automatic respiration system model is designed and controller parameters are tuned using hybrid Optimization techniques. Hybrid Controllers like genetic algorithm based Fractional Order Proportional Integral Derivative controller (FOPID), Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller, and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) controllers were designed and verified. Integral Square Error is considered as the objective function of the optimization technique to find the controller parameters. The output responses of all three hybrid controllers are compared based on the error indices, time domain specifications, set-point tracking and Convergence speed graph. The genetic algorithm-based FOPID controller gives better results when compared with the Fmincon-Pattern search Algorithm based Proportional Integral Derivative (PID) controller and Hybrid Model predictive control (MPC) – Proportional Integral Derivative (PID) for the proposed artificial ventilation system

System Identification And Control Of Automatic Car Pedal Pressing System For Low-Speed Driving In A Road Traffic Delay

Congestion in major cities has been a typical occurrence among communities. Being stuck in traffic for hours in a sitting position necessitates recurrent chores of manually pressing the accelerator and stop pedals excessively, which, if performed without the proper sitting posture, can result in quick weariness, especially for the driver's leg and back. Therefore, this research is to automate the pressing mechanism by modelling an actuator for pedals pressing concentrating for low-speed driving in a road traffic delay. This project presents system identification and control of automatic car pedal pressing system for low-speed driving in a road traffic delay. Two parameters such as force and low speed will be observed for both controllers and the ability to attenuate disturbance will be simulated. Output of the controller will determine the force of the car brake. The parallel linkage is connected to the actuator such that it provides an opposite reaction to the car pedal. The system identification is taken from the dynamic behavior of the system and modelled using input-output data acquired directly from the experimental rig. The work utilized a neural network to model the system as the system exhibits highly nonlinear behavior. This paper compares the low-speed control with both controllers: conventional PID and fuzzy logic controller with respect to overshoot and steady state error. A PID controller and a Fuzzy controller were designed, simulated, and compared in their ability to control the speed of the car. Both controllers were then implemented and tested in automatic car pedal pressing system. The controller gains were tuned using metaheuristic algorithm which is Particle Swarm Algorithm (PSO) for optimal values of fuzzy controller parameters. The controller parameters are optimized based on Integral Squared Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Mean Squared Error) MSE. The comparative assessment of both controllers was reported and discussed. It shown that Fuzzy logic controller performs better than PID controller with 1.2724% reduction in steady state error and 3.64% in overshoot respectively

Decentralized Synergetic Control of Power Systems

The objective of this dissertation is to design decentralized controllers to enhance the transient stability of power systems. Due to the nonlinearities and complexities of the system, nonlinear control design techniques are required to improve its dynamic performance. In this dissertation a synergetic control technique is being proposed to design supplementary controller that is added to the exciter of the generation unit of the system. Although this method has been previously applied to a Single Infinite Machine Bus (SMIB) system with high degree of success, it has not been employed to systems with multi machine. Also, the method has good robust characteristic like that of the popular Sliding Mode Control (SMC) technique. But the latter technique introduces steady state chattering effect which can cause wear and tear in actuating system. This gives the proposed technique a major advantage over the SMC. In this work, the method is employed for systems with multi machine. Each of the machines is considered to be a subsystem and decentralized controller is designed for each subsystem. The interconnection term of each subsystem with the rest of the system is estimated by a polynomial function of the active power generated by the subsystem. Particle Swarm Optimization (PSO) technique is employed for optimum tuning of the controller\u27s parameters. To further enhance the performance of the system by widening its range of operation, Reinforcement Learning (RL) technique is used to vary the gains of the decentralized synergetic supplementary controller in real time. The approach is illustrated with several case studies including a SMIB system with or without a Static Var Compensator (SVC), a Two Area System (TAS) with or without an SVC, a three --machines-nine-bus system and a fifty machine system. Results show that the proposed control technique provides better damping than the conventional power system stabilizers and synergetic controllers with fixed gains

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate physical models for these plants using first principles may be impossible. With the increased developments in the computing world, large amounts of measured data can be easily collected and stored for processing purposes. Data can also be collected and used in an on-line fashion. Thus it would be very sensible to make full use of this data for controller design, performance evaluation, and stability analysis. The design methods imposed in this work ensure that the dynamics of a system are captured in an experiment and avoids the problem of unmodeled dynamics associated with parametric models. The devised methods consider robust designs for both linear-time-invariant (LTI) single-input-single-output (SISO) systems and certain classes of nonlinear systems. In this dissertation, a data-driven approach using the frequency response function of a system is proposed for designing robust controllers with H∞ performance. Necessary and sufficient conditions are derived for obtaining H∞ performance while guaranteeing the closed-loop stability of a system. A convex optimization algorithm is implemented to obtain the controller parameters which ensure system robustness; the controller is robust with respect to the frequency-dependent uncertainties of the frequency response function. For a certain class of nonlinearities, the proposed method can be used to obtain a best-linear-approximation with an associated frequency dependent uncertainty to guarantee the stability and performance for the underlying linear system that is subject to nonlinear distortions. The concepts behind these design methods are then used to devise necessary and sufficient conditions for ensuring the closed-loop stability of systems with sector-bounded nonlinearities. The conditions are simple convex feasibility constraints which can be used to stabilize systems with multi-model uncertainty. Additionally, a method is proposed for obtaining H∞ performance for an approximate model (i.e., describing function) of a sector-bounded nonlinearity. This work also proposes several data-driven methods for designing robust fixed-structure controllers with H∞ performance. One method considers the solution to a non-convex problem, while another method convexifies the problem and implements an iterative algorithm to obtain the local solution (which can also consider H2 performance). The effectiveness of the proposed method(s) is illustrated by considering several case studies that require robust controllers for achieving the desired performance. The main applicative work in this dissertation is with respect to a power converter control system at the European Organization for Nuclear Research (CERN) (which is used to control the current in a magnet to produce the desired field in controlling particle trajectories in accelerators). The proposed design methods are implemented in order to satisfy the challenging performance specifications set by the application while guaranteeing the system stability and robustness using data-driven design strategies

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research