Hardware-in-the-loop experimental study on a fractional order networked control system testbed

Networked Control Systems (NCS) are of great interest in many industries because of their convenience in data sharing and manipulation remotely. However, there are several problems along with NCS itself due to the uncertainties in network communication. One issue inherent to NCS is the network-induced delays which may deteriorate the performance and may even cause instability of the system. Therefore a controller which can make the plant stable at large values of delay is always desirable in NCS systems. Our past work on Optimal Fractional Order Proportional Integral (OFOPI) controller showed that fractional order PI controllers have larger jitter margin (maximum value of delay for which system is stable) for lag-dominated systems when compared to traditional Proportional Integral Derivative (PID) controllers, whereas integer order PID controllers have larger jitter margin for delay-dominated systems. This paper aims at the design process of a tele-presence controller based on OFOPI tuning rules. To illustrate this, an extensive experimental study on the real-time Smart Wheel networked speed control system is performed using hardware-in-the-loop control. The real-time random delay in the world wide network is collected by pinging different locations, and is considered as the delay in our simulation and experimental systems. Comparisons are made with existing integer order PID controller. It is found that the proposed OFOPI controller is a promising controller and has faster response time than the traditional integer order PID controllers. Since the plant into consideration viz. the Smart Wheel is a delay-dominated system, it is verified that PID achieves larger jitter margin as compared to OFOPI tuning rules. Simulation results and real-time experiments showing comparisons between OFOPI and OPID tuning rules prove the significance of this method in NCS.     

Finding attractive technical patterns in cryptocurrency markets

The cryptographic currency market is an emerging venue for traders looking to diversify their investments. We investigate the use of genetic programming (GP) for finding attractive technical patterns in a cryptocurrency market. We decompose the problem of automatic trading into two parts, mining useful signals and applying them to trading strategies, and focus our attention on the former. Extensive experiments are performed to analyze the factors that affect the quality of the solutions found by the proposed GP system. With the introduction of domain knowledge through extended function sets and the inclusion of diversity preserving mechanism, we show that the proposed GP system successfully finds attractive technical patterns. Out-of-sample performance of the patterns indicates that the GP consistently finds signals that are profitable and frequent. A trading simulation with the generated patterns suggests that the captured signals are indeed useful for portfolio optimization.     

Real-coded genetic algorithm for system identification and controller tuning

This paper presents an application of real-coded genetic algorithm (RGA) for system identification and controller tuning in process plants. The genetic algorithm is applied sequentially for system identification and controller tuning. First GA is applied to identify the changes in system parameters. Once the process parameters are identified, the optimal controller parameters are identified using GA. In the proposed genetic algorithm, the optimization variables are represented as floating point numbers. Also, cross over and mutation operators that can directly deal with the floating point numbers are used. The proposed approach has been applied for system identification and controller tuning in nonlinear pH process. The simulation results show that the GA based approach is effective in identifying the parameters of the system and the nonlinearity at various operating points in the nonlinear system.     

A Simple Approach for Multi‐fidelity Experimentation Applied to Financial Engineering

In practical applications, information about the accuracy or ‘fidelity’ of alternative surrogate systems may be ambiguous and difficult to determine. To address this problem, we propose to treat surrogate system fidelity level as a categorical factor in optimal response surface design. To design the associated experiments, we apply the Expected Integrated Mean Squared Error optimal design criterion, which takes into account both variance and bias errors. The performance of the proposed design was compared using three test cases to four types of alternatives using the Empirical Integrated Squared Error. Because of its ability to foster relatively accurate predictions, the proposed design is recommended in fidelity experimental design, particularly when the experimenters lack sufficient information about the fidelity levels of surrogate systems. The method was applied to the case of intraday trading optimization in which data were collected from the Taiwan Futures Exchange. We also calculated the implied volatility from the Merton's Jump‐diffusion model via the fast Fourier transform algorithm with three different models of varying fidelity levels. Copyright © 2014 John Wiley & Sons, Ltd.     

The relevance of system dynamics to engineering systems design

With the recent advent of new technology using micro computers for the centralised monitoring of information, the scope for totally automatic, real time control of large engineering systems has been advanced. We are entering a period which, retrospectively, may well be seen as the era of control; where for the first time it is both feasible and necessary to design system in terms of the way in which they will be operated, as well as in the traditional terms of their capacity requirements. However, before such comprehensive design can be undertaken compatible advances are required in information usage technology.The purpose of this paper is to demonstrate how the subject of System Dynamics can be used to assist with this problem, though its capacity to model information feedback and hence to model and test alternative system control rules based on the information retrieved. This type of application in the hard system area of engineering provides a strong contrast to the more usual applications of System Dynamics in softer socio-economic systems.The problem described is taken from the coal mining industry and related to the design of large scale underground conveyor belt systems used for the clearance of coal from mines. The demonstration presented, therefore, concerns research in this field, but has considerable scope for application in the general area of large scale bulk handling system.Of particular importance in the results is an indication of the potential savings in physical capacity which can stem from the careful design and implementation of control in such systems.     

Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation

The increasing demand for high reliability, safety and availability of technical systems calls for innovative maintenance strategies. The use of prognostic health management (PHM) approach where maintenance action is taken based on current and future health state of a component or system is rapidly gaining popularity in the maintenance industry. Multiclass support vector machines (MC-SVM) has been identified as a promising algorithm in PHM applications due to its high classification accuracy. However, it requires parameter tuning for each application, with the objective of minimizing the classification error. This is a single objective optimization problem which requires the use of optimization algorithms that are capable of exhaustively searching for the global optimum parameters. This work proposes the use of hybrid differential evolution (DE) and particle swarm optimization (PSO) in optimally tuning the MC-SVM parameters. DE identifies the search limit of the parameters while PSO finds the global optimum within the search limit. The feasibility of the approach is verified using bearing run-to-failure data and the results show that the proposed method significantly increases health state classification accuracy. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Evolving technical trading rules for spot foreign-exchange markets using grammatical evolution

Grammatical Evolution (GE) is a novel, data-driven, model-induction tool, inspired by the biological gene-to-protein mapping process. This study provides an introduction to GE, and applies the methodology in an attempt to uncover useful technical trading rules which can be used to trade foreign exchange markets. In this study, each of the evolved rules (programs) represents a market trading system. The form of these programs is not specified ex-ante, but emerges by means of an evolutionary process. Daily US-DM, US-Stg and US-Yen exchange rates for the period 1992 to 1997 are used to train and test the model. The findings suggest that the developed rules earn positive returns in hold-out sample test periods, after allowing for trading and slippage costs. This suggests potential for future research to determine whether further refinement of the methodology adopted in this study could improve the returns earned by the developed rules. It is also noted that this novel methodology has general utility for rule-induction, and data mining applications.MSC codes: 91B84, 68W10, 62J02     

Investment using evolutionary learning methods and technical rules

In this paper, I propose a genetic learning approach to generate technical trading systems for stock timing. The most informative technical indicators are selected from a set of almost 5000 signals by a multi-objective genetic algorithm with variable string length. Successively, these signals are combined into a unique trading signal by a learning method. I test the expert weighting solution obtained by the plurality voting committee, the Bayesian model averaging and Boosting procedures with data from the S&P 500 Composite Index, in three market phases, up-trend, down-trend and sideways-movements, covering the period 2000–2006. Computational results indicate that the near-optimal set of rules varies among market phases but presents stable results and is able to reduce or eliminate losses in down-trend periods.     

Chaos synchronization using fuzzy logic controller

The design of a rule-based controller for a class of master-slave chaos synchronization is presented in this paper. In traditional fuzzy logic control (FLC) design, it takes a long time to obtain the membership functions and rule base by trial-and-error tuning. To cope with this problem, we directly construct the fuzzy rules subject to a common Lyapunov function such that the master–slave chaos systems satisfy stability in the Lyapunov sense. Unlike conventional approaches, the resulting control law has less maximum magnitude of the instantaneous control command and it can reduce the actuator saturation phenomenon in real physic system. Two examples of Duffing–Holmes system and Lorenz system are presented to illustrate the effectiveness of the proposed controller.     

Mean–Variance portfolio selection in presence of infrequently traded stocks

This paper deals with a mean–variance optimal portfolio selection problem in presence of risky assets characterized by low-frequency trading and, therefore, low liquidity. To model the dynamics of illiquid assets, we introduce pure-jump processes. This leads to the development of a portfolio selection model in a mixed discrete/continuous time setting. We pursue the twofold scope of analyzing and comparing either long-term investment strategies as well as short-term trading rules. The theoretical model is analyzed by applying extensive Monte Carlo experiments, in order to provide useful insights from a financial perspective.     

Robust optimization for performance tuning of modern database systems

Issues regarding design and management of database systems have been studied by applying operations research (OR) techniques. The purpose of this study is to propose a new alternative towards database performance tuning for query-processing needs of modern database systems from the perspective of operations research using robust optimization. We use a query-driven approach to specify database structures (schema) so that they are robust to uncertainty and dynamics of queries in a changing environment and allow fast and timely information retrieval and exchange. Instead of applying hardware tuning or traditional database tuning techniques, we examine queries by their types and properties to derive database structures that are robust at efficiently processing future queries of any type. This query-driven approach improves the efficiency of processing queries by setting up database structures based on the queries’ information needs. This new methodology provides a new approach of tuning database performance that is robust to unexpected changes and dynamics. To further demonstrate the idea, we develop a robust optimization model using a non-linear von Neumann–Morgenstern expected utility function and present two computational examples.     

Dynamic Data Structures for a Direct Search Algorithm

The DIRECT (DIviding RECTangles) algorithm of Jones, Perttunen, and Stuckman (Journal of Optimization Theory and Applications, vol. 79, no. 1, pp. 157–181, 1993), a variant of Lipschitzian methods for bound constrained global optimization, has proved effective even in higher dimensions. However, the performance of a DIRECT implementation in real applications depends on the characteristics of the objective function, the problem dimension, and the desired solution accuracy. Implementations with static data structures often fail in practice, since it is difficult to predict memory resource requirements in advance. This is especially critical in multidisciplinary engineering design applications, where the DIRECT optimization is just one small component of a much larger computation, and any component failure aborts the entire design process. To make the DIRECT global optimization algorithm efficient and robust on large-scale, multidisciplinary engineering problems, a set of dynamic data structures is proposed here to balance the memory requirements with execution time, while simultaneously adapting to arbitrary problem size. The focus of this paper is on design issues of the dynamic data structures, and related memory management strategies. Numerical computing techniques and modifications of Jones' original DIRECT algorithm in terms of stopping rules and box selection rules are also explored. Performance studies are done for synthetic test problems with multiple local optima. Results for application to a site-specific system simulator for wireless communications systems (S ⁴ W) are also presented to demonstrate the effectiveness of the proposed dynamic data structures for an implementation of DIRECT.     

Numerical methods of stabilizer construction via guidance control

This work concerns guidance stabilization of non‐autonomous control systems. Global stabilization problem is usually quite complex and difficult to solve. To overcome this difficulty, guidance control is used. A guidance stabilizer uses a trajectory of a globally asymptotically stable auxiliary system as a guide. A local stabilizer keeps the trajectory of the system in a neighborhood of a solution of the auxiliary system. In this way, the trajectory of the system tends to the equilibrium position. The main idea of this method is to solve the global stabilization problem by applying local stabilization methods. The presented procedure also yields additional possibilities for the design of a stabilizer that eliminates the peak effect, that is, the large deviation of the solutions from the equilibrium position at the beginning of the stabilization process. This effect represents a serious obstacle to the design of cascade control systems and to guidance stabilization. The optimal control problem used in this paper eliminates this effect that we have when we apply known construction methods of local stabilizers to obtain a high speed of damping of the control systems trajectories. According to this approach and using ε‐strategies introduced by Pontryagin in the frame of differential games theory, the stabilizing control is constructed as a function of time defined in a small time interval and not as a feedback. An application to a mechanical stabilization problem is provided here. Copyright © 2012 John Wiley & Sons, Ltd.     

Robot fingers to tune TV amplifiers using fuzzy logic

This paper contains the definition and the resolution of the problem of tuning electronic amplifiers of aerials for VHF, UHF and SHF signals, in terms of a control problem with fuzzy sets and fuzzy rules. An industrial prototype of a specific robotic system to tune the amplifiers automatically is described briefly. This prototype has been made in collaboration with the manufacturer of this amplifiers Televés S.A. The robot is driven with a fuzzy controller based on the fuzzy sets and fuzzy rules found for the tuning problem.     

Characteristic Boundary Layers for Mixed Hyperbolic-Parabolic Systems in One Space Dimension and Applications to the Navier-Stokes and MHD Equations

We provide a detailed analysis of the boundary layers for mixed hyperbolic-parabolic systems in one space dimension and small amplitude regimes. As an application of our results, we describe the solution of the so-called boundary Riemann problem recovered as the zero viscosity limit of the physical viscous approximation. In particular, we tackle the so-called doubly characteristic case, which is considerably more demanding from the technical viewpoint and occurs when the boundary is characteristic for both the mixed hyperbolic-parabolic system and for the hyperbolic system obtained by neglecting the second-order terms. Our analysis applies in particular to the compressible Navier-Stokes and MHD equations in Eulerian coordinates, with both positive and null electrical resistivity. In these cases, the doubly characteristic case occurs when the velocity is close to 0. The analysis extends to nonconservative systems. © 2020 Wiley Periodicals, Inc.     

A Forex trading system based on a genetic algorithm

In this paper, a genetic algorithm will be described that aims at optimizing a set of rules that constitute a trading system for the Forex market. Each individual in the population represents a set of ten technical trading rules (five to enter a position and five others to exit). These rules have 31 parameters in total, which correspond to the individuals’ genes. The population will evolve in a given environment, defined by a time series of a specific currency pair. The fitness of a given individual represents how well it has been able to adapt to the environment, and it is calculated by applying the corresponding rules to the time series, and then calculating the ratio between the profit and the maximum drawdown (the Stirling ratio). Two currency pairs have been used: EUR/USD and GBP/USD. Different data was used for the evolution of the population and for testing the best individuals. The results achieved by the system are discussed. The best individuals are able to achieve very good results in the training series. In the test series, the developed strategies show some difficulty in achieving positive results, if you take transaction costs into account. If you ignore transaction costs, the results are mostly positive, showing that the best individuals have some forecasting ability.     

Optimization of an Active Noise Control System Inside an Aircraft, Based on the Simultaneous Optimal Positioning of Microphones and Speakers, with the Use of a Genetic Algorithm

In the last decade, Active Noise Control (ANC) has become a very popular technique for controlling low-frequency noise. The increase in its popularity is a consequence of the rapid development in the fields of computers in general, and more specifically in digital signal processing boards. ANC systems are application specific and therefore they should be optimally designed for each application. Even though the physical background of the ANC systems is well known and understood, efficient tools for the optimization of the sensor and actuator configurations of the ANC system, based on classical optimization methods, do not exist. This is due to the nature of the problem that allows the calculation of the effect of the ANC system only when the sensor and actuator configurations are specified. An additional difficulty in this problem is that the sensor and the actuator configurations cannot be optimized independently, since the effect of the ANC system directly depends on the combined sensor and actuator configuration. For the solution of this problem several other optimization techniques were applied, such as simulated annealing for example. In this paper the successful application of a Genetic Algorithm, an optimization technique that belongs to the broad class of evolutionary algorithms, is presented. The results obtained from the application of the GA are very promising. The GA was able to identify various configurations that achieved a reduction of 6.3 dBs to 6.5 dBs, which corresponds to an actual reduction of 50% of the initial acoustic pressure.     

区域差异化制度设计下的碳排放交易市场效率评价

本文依据中国碳排放交易试点阶段“差异化”制度设计的特点,基于分配制度、灵活机制、控排范围、交易规则、报告与核查制度以及法律制度六个维度,构建可数据化的碳交易市场投入产出效率评价指标体系。本文利用地区差异系数剔除制度外非均衡环境因素的影响,对现有试点地区差异化制度设计下的碳排放交易市场效率进行有效评价。研究表明:第一,较没有剔除制度外因素,上海、天津和北京3个省市碳交易市场效率有所下降,而其余4个省区投入产出效率则上升,说明碳交易市场效率确实受制度外因素的影响,同时说明探讨碳交易市场差异化制度设计有效性时,控制制度外因素干扰的必要性和合理性。第二,就制度设计本身,七个试点碳交易市场均未达到有效。广东碳排放交易市场效率最优,仅控排行业覆盖比例设计过低;重庆和天津的效率紧随其后,上海、深圳和北京的效率较低;而湖北由于控排范围设计不合理、准入门槛过高、MRV从业人员严重短缺等原因导致其交易市场效率最差。     

A Genetic Algorithm for Solving a Capacitated p-Median Problem

Facility-location problems have several applications, such as telecommunications, industrial transportation and distribution. One of the most well-known facility-location problems is the p-median problem. This work addresses an application of the capacitated p-median problem to a real-world problem. We propose a genetic algorithm (GA) to solve the capacitated p-median problem. The proposed GA uses not only conventional genetic operators, but also a new heuristic “hypermutation” operator suggested in this work. The proposed GA is compared with a tabu search algorithm.