NL4Py: Agent-Based Modeling in Python with Parallelizable NetLogo Workspaces

NL4Py is a NetLogo controller software for Python, for the rapid, parallel execution of NetLogo models. NL4Py provides both headless (no graphical user interface) and GUI NetLogo workspace control through Python. Spurred on by the increasing availability of open-source computation and machine learning libraries on the Python package index, there is an increasing demand for such rapid, parallel execution of agent-based models through Python. NetLogo, being the language of choice for a majority of agent-based modeling driven research projects, requires an integration to Python for researchers looking to perform statistical analyses of agent-based model output using these libraries. Unfortunately, until the recent introduction of PyNetLogo, and now NL4Py, such a controller was unavailable. This article provides a detailed introduction into the usage of NL4Py and explains its client-server software architecture, highlighting architectural differences to PyNetLogo. A step-by-step demonstration of global sensitivity analysis and parameter calibration of the Wolf Sheep Predation model is then performed through NL4Py. Finally, NL4Py's performance is benchmarked against PyNetLogo and its combination with IPyParallel, and shown to provide significant savings in execution time over both configurations

Analysis of Tuning Parameters of Model Predictive Controller (MPC)

Process optimization is very important in the engineering industries. As optimisation is achieved, less consumption of energy and utilities can be obtained for the process. In achieving optimisation, the response should be responded close to the reference values. The refineries nowadays consist mainly of multi variable unit process. Thus, to achieve optimisation using classical approach will be less reliable and time consuming. Hence, the introduction of Model Predictive Controller (MPC) to the process unit is more suitable compared to the classical approach. MPC is capable to solve high order problem and multivariate processes. The successful of MPC depends on the selection of tuning parameters. Therefore, by analysing the effect of each tuning parameters on the controller performance, promising performance of MPC can be produced. Firstly, the processes are selected from books as a case study to resemble the high order and multi variable problem processes. Then, the analysis will be done to study the effect of input weightage (UwO, output weightage (ywt), control horizon (M) and prediction horizon (P) on the controller performance. By changing one of the tuning parameter, the other tuning parameters have to be kept constant

Design challenges in the use of silicon carbide JFETs in matrix converter applications

This paper investigates some of the challenges en¬countered during the implementation of a Silicon Carbide JFET matrix converter which has been designed to meet a specific power density of 20kW/litre with forced air cooling. After a brief introduction to the main features of the hardware implementation of the power converter, an insight into the control strategy and controller platform adopted is given with a particular attention to the issues relating to the high switching frequencies on the controller requirements and the performance implications of the gate drive circuitry. An analysis of the results which show the effects of gate driver and controller induced commutation time limitations on the output waveform quality is presented. Wide bandgap semiconductor devices offer the power electronic engineer new opportunities for high speed, high efficiency designs but these devices cannot be used as a simple like for like replacements and as such the whole converter system needs to be looked at

Levenberg-Marquardt Backpropagation Algorithm Neural Network을 이용한 디젤엔진 동정과 속도제어에 관한 연구

Diesel engine is known as nonlinear system because of its dead time due to injection delay and ignition delay. So, it is very difficult and complex to model this nonlinear system because it varies widely according to number of cylinder and RPM. In this paper, in order to design the speed control system of a diesel engine, neural network architecture is introduced and the optimal structure of neuro emulator is determined based on the modelling of a diesel engine, trained with various backpropagation algorithms and the performance of each trained networks is compared . Also, neuro controller, the inversely trained neural network of neuro emulator, is designed for the speed control system of a diesel engine. The selective neuro controller is proposed for the sake of improvement of the neuro controller performance and by combining a PI controller with the proposed controller, the efficiency of this combination speed control system of a diesel engine is ascertained.？疇？ Chapter 1. Introduction = 5 1.1 Background = 5 1.2 Study Objective = 8 Chapter 2. Review of Neural Networks = 10 2.1 Neuron Model = 10 2.2 Neural Networks = 14 2.3 Learning of Neural Networks = 15 2.3.1 Simple Backpropagation = 16 2.3.2 Backpropagation with Momentum(BPM)18 2.3.3 Adaptive Backpropagation(BPA) = 18 2.3.4 Fast Backpropagation(BPX) = 19 2.3.5 Levenberg-Marquardt Backpropagation(BPLM) = 19 2.4 Initialization of Neural Networks = 20 Chapter 3. Design of Neuro Emulator for Diesel Engine 22 3.1 Modelling of a Diesel Engine System = 22 3.2 Structure of a Neuro Emulator = 24 3.3 Data Collection Method = 25 3.4 Training Results and Analysis with respect to Various Backpropagation Algorithms = 29 Chapter 4. Design of a Neuro Controller for Diesel Engine = 33 4.1 Neuro Controller Design = 33 4.2 Design of a Neuro Control System = 36 4.3 Design of Combination Control System with PI and Neuro Controller = 39 Chapter 5. Conclusion = 42 Reference = 4

On the Jitter Sensitivity of an Adaptive Digital Controller::A Computational Simulation Study

In many real-time control applications, the ability to accurately track a reference trajectory with stable, pre-specified closed-loop dynamics is highly desirable. For fixed gain control systems, the detrimental impact of jitter on performance has been relatively well studied. However, research that quantifies the possible impact of jitter on the performance and relative stability of adaptive control schemes is comparatively much rarer. With technology advances now making real-time adaptive control a viable option for high-speed applications, this situation requires further investigation. In this paper, the jitter sensitivity of a digital parameter adaptive tracking control system is studied using precise software-in-the-loop computational simulations. The results obtained indicated that the adaptive controller was significantly susceptible to jitter. In particular, key metrics such as the phase margin, gain margin, settling time, overshoot and root mean square parameter and tracking errors were all significantly impacted following the introduction of 5% jitter in the controller. The obtained data are thought to be the first detailed results of this kind and present useful insights into the practical complexities when innovating adaptive real-time tracking control systems and indicate that specialized controller implementations that minimize jitter should be employed and that further analysis is warranted

Dynamic Modeling and Analysis for Supply Chain

The objective of this study is to use system dynamics methodology to model the supply chain system and then present the optimal control to optimize the performance of supply chain by minimize the quadratic cost function while tracking and keeping the inventory close to target level. Under the system dynamics point of view, the supply chain was modeled as the continuous differential equation with lead time delay modeled as the first order delay model. In contrast to the frequency domain analysis of the classical control approach, the proposed control utilizes the time-domain state space representation with a set of input, output and state variables to build the dynamic system. On the other hand, by using the system dynamics it allows us to apply different control laws and analyze the dynamic behavior of system so that the decision policies can be found to improve the performance of supply chain. In this paper we employ the linear quadratic optimal control for such kind of supply chain dynamic system, the aim of controller is to find the control input as the order quantity to minimize the cost function and keep high customer satisfaction by tracking the target inventory level. Finally, the numerical simulation results are carried out in Matlab/Simulink environment and the performance of optimal controller will be compared with some classical control policies such as proportional and order-up-to level control policy. It is shown that our approach can obtain some good performances.Chapter 1. Introduction 1 1.1 Background 1 1.2 Motivation of this research 3 1.3 Research objectives 5 Chapter 2. Supply chain management and performance measurements 7 2.1 Supply chain management 7 2.2 Structure of supply chain management 10 2.3 Performance measurement of supply chain management 13 2.4 Process in supply chain management 15 Chapter 3. Dynamic modeling of supply chain 21 3.1 Production model 21 3.2 Transportation model 27 3.3 Distribution model 29 3.4 State-space of supply chain model 30 3.5 Costs function of supply chain 31 Chapter 4. Controller design 33 4.1 State-space model 33 4.2 Linear Quadratic Regular control design 33 4.3 Optimal tracking controller 35 Chapter 5. Simulation results 37 5.1 Demand and control parameters 37 5.2 Simulation results and analysis 38 Chapter 6. Conclusion 44 References 4

Dynamical Analysis and Robust Control Synthesis for Water Treatment Processes

Nowadays, water demand and water scarcity are very urgent issues due to population growth, drought and poor water quality all over the world. Therefore, water treatment plants are playing a vital role for good living condition of human. Water area needs more concentration study to increase water productivity and decrease water cost. This dissertation presents the analysis and control of water treatment plants using robust control techniques. The applied control algorithms include H∞, gain scheduled and observer-based loop-shaping control technique. They are modern control algorithms and very powerful in robust controlling of systems with uncertainties and disturbances. The water treatment plants include a desalination system and a wastewater process. Since fresh water scarcity is getting more serious, the desalination plants are to produce drinking water and wastewater treatment plants give the reusable water. The desalination system is a RO one used to produce drinking water from seawater and brackish water. The nonlinear behaviors of this system is carefully analyzed before the linearization. Due to the uncertainty caused by concentration polarization, the system is linearized using linear state-space parametric uncertainty framework. The system also suffer from many disturbances which water hammer is one of the most influential one. The mixed robust H∞ and μ-synthesis control algorithm is applied to control the RO system coping with large uncertainties, disturbances and noises. The wastewater treatment process is an activated sludge process. This biological process use microorganisms to convert organic and certain inorganic matter from wastewater into cell mass. The process is very complex with many coupled biological and chemical reactions. Due to the large variation in the influent flow, the system is modelized and linearized as a linear parametric varying system using affine parameter-dependent representation. Since the influent flow is quickly variable and easily to be measured, the robust gain scheduled robust controller is applied to deal with the large uncertainty caused by the scheduled parameter. This control algorithm often gives better performances than those of general robust H∞ one. In the wastewater treatment plant, there exist an anaerobic digestion, which is controlled by the observer-based loop-shaping algorithm. The simulations show that all the controllers can effectively deal with large uncertainties, disturbances and noises in water treatment plants. They help improve the system performances and safeties, save energy and reduce product water costs. The studies contribute some potential control approaches for water treatment plants, which is currently a very active research area in the world.Contents ······················································································· iv List of Tables ··············································································· viii List of Figures ··············································································· ix Chapter 1. Introduction ···································································· 1 1.1 Reverse osmosis process ···································································· 2 1.2 Activated sludge process ···································································· 6 1.3 Robust H∞ and gain scheduling control ··················································· 10 Chapter 2. Robust H∞ controller ······················································· 13 2.1 Introduction ·················································································· 13 2.2 Uncertainty modelling ······································································ 13 2.2.1 Unstructured uncertainties ···························································· 14 2.2.2 Parametric uncertainties ······························································· 15 2.2.3 Structured uncertainties ································································ 16 2.2.4 Linear fractional transformation ······················································ 16 2.3 Stability criterion ············································································ 17 2.3.1 Small gain theorem ····································································· 17 2.3.2 Structured singular value (muy) synthesis brief definition ·························· 19 2.4 Robustness analysis and controller design ··············································· 20 2.4.1 Forming generalised plant and N-delta structure ····································· 20 2.4.2 Robustness analysis ···································································· 24 2.5 Reduced controller ·········································································· 26 2.5.1 Truncation ··············································································· 27 2.5.2 Residualization ········································································· 29 2.5.3 Balanced realization···································································· 29 2.5.4 Optimal Hankel norm approximation ················································ 31 Chapter 3. Robust gain scheduling controller ······································· 37 3.1 Introduction ·················································································· 37 3.2 Linear parameter varying (LPV) system ·················································· 39 3.3 Matrix Polytope ·············································································· 40 3.4 Polytope and affine parameter-dependent representation ······························· 41 3.4.1 Polytope representation ································································ 41 3.4.2 Affine parameter-dependent representation ········································· 42 3.5 Quadratic stability of LPV systems and quadratic (robust) H∞ performance ········· 43 3.6 Robust gain scheduling ····································································· 44 3.6.1 LPV system linearization ······························································ 44 3.6.2 Polytope-based gain scheduling ······················································ 45 3.6.3 LFT-based gain scheduling ··························································· 48 Chapter 4. Mixed robust H∞ and μ-synthesis controller applied for a reverse osmosis desalination system ····························································· 52 4.1 RO principles ················································································ 52 4.1.1 Osmosis and reverse osmosis ························································· 52 4.1.2 Dead-end filtration and cross-flow filtration ········································ 53 4.2 Membranes ··················································································· 54 4.2.1 Structure and material ································································· 54 4.2.2 Hollow fine fiber membrane module ················································ 55 4.2.3 Spiral wound membrane module ····················································· 57 4.3 Nonlinear RO modelling and analysis ···················································· 58 4.3.1 RO system introduction ······························································· 58 4.3.2 Modelling ··············································································· 60 4.3.3 Nonlinear analysis ······································································ 62 4.3.4 Concentration polarization ···························································· 64 4.4 Water hammer phenomenon ······························································· 66 4.4.1 Water hammer, column separation and vaporous cavitation ······················ 66 4.4.2 Water hammer analysis and simulation ·············································· 69 4.4.3 Prevention of water hammer effect··················································· 78 4.5 RO linearization ············································································· 79 4.5.1 Nominal linearization ·································································· 79 4.5.2 Uncertainty modeling ·································································· 81 4.5.3 Parametric uncertainty linearization ················································· 83 4.6 Robust H∞ controller design for RO system ·············································· 85 4.6.1 Control of uncertain RO system ······················································ 85 4.6.2 Robustness analysis and H∞ controller design ······································ 86 4.7 Simulation result and discussion··························································· 90 4.8 Conclusion ··················································································· 95 Chapter 5. Robust gain scheduling control of activated sludge process ······· 96 5.1 Introduction about activated sludge process ············································· 96 5.1.1 State variables ·········································································· 98 5.1.2 ASM1 processes ······································································ 100 5.1.3 The control problem of activated sludge process ································· 102 5.2 System modelling ········································································· 104 5.3 Model linearization ········································································ 107 5.4 Robust gain-schedule controller design for activated sludge process ··············· 109 5.5 Simulation result and discussion························································· 115 5.6 Conclusion ················································································· 120 Chapter 6. Observer-based loop-shaping control of anaerobic digestion ···· 121 6.1 Introduction ················································································ 121 6.1.1 Control problem in anaerobic digestion ··········································· 122 6.2 System modelling ········································································· 123 6.3 Controller design ·········································································· 124 6.3.1 H∞ loop-shaping controller ························································· 125 6.3.2 Coprime factor uncertainty ·························································· 126 6.3.3 Control synthesis ····································································· 127 6.4 Simulation result ··········································································· 131 6.5 Conclusion ················································································· 133 Chapter 7. Conclusion ··································································· 134 References ·················································································· 136 Appendices ················································································· 144Docto

The generation of dual wavelength pulse fiber laser using fiber bragg grating

A stable simple generation of dual wavelength pulse fiber laser on experimental method is proposed and demonstrated by using Figure eight circuit diagram. The generation of dual wavelength pulse fiber laser was proposed using fiber Bragg gratings (FBGs) with two different central wavelengths which are 1550 nm and 1560 nm. At 600 mA (27.78 dBm) of laser diode, the stability of dual wavelength pulse fiber laser appears on 1550 nm and 1560 nm with the respective peak powers of -54.03 dBm and -58.00 dBm. The wavelength spacing of the spectrum is about 10 nm while the signal noise to ratio (SNR) for both peaks are about 8.23 dBm and 9.67 dBm. In addition, the repetition rate is 2.878 MHz with corresponding pulse spacing of about 0.5 μs, is recorded

Validation and Verification of Aircraft Control Software for Control Improvement

Validation and Verification are important processes used to ensure software safety and reliability. The Cooper-Harper Aircraft Handling Qualities Rating is one of the techniques developed and used by NASA researchers to verify and validate control systems for aircrafts. Using the Validation and Verification result of controller software to improve controller\u27s performance will be one of the main objectives of this process. Real user feedback will be used to tune PI controller in order for it to perform better. The Cooper-Harper Aircraft Handling Qualities Rating can be used to justify the performance of the improved system