Modern methods of design, analysis, optimization and implementation of conventional control algorithms for processes with finite and infinite degrees of freedom

This paper proposes a DSP based simulator for development and implementation of control algorithms. The simulator is used to control a synchronous aggregate model in real time. The simulator consists of a PC (on which a synchronous generator connected to AC network is simulated) connected through a communication channel to a DSP (on which the control algorithm is implemented). The simulator makes implementation of a control algorithm faster and easier. It also enables verification of a control algorithm in real time. Simulation results show that there are no significant differences between non-real time simulations (on a PC) and real time simulations (on a DSP based simulator). This paper also presents design and implementation of a nonlinear control algorithm for excitation control system based on the Lyapunov's direct method. Both conventional excitation control algorithm and proposed nonlinear excitation control algorithm were implemented and tested on the real time simulator. The obtained simulation results show that proposed nonlinear excitation control algorithm better damps electromechanical oscillations than conventional excitation control algorithm.

This paper proposes a DSP based simulator for development and implementation of control algorithms. The simulator is used to control a synchronous aggregate model in real time. The simulator consists of a PC (on which a synchronous generator connected to AC network is simulated) connected through a communication channel to a DSP (on which the control algorithm is implemented). The simulator makes implementation of a control algorithm faster and easier. It also enables verification of a control algorithm in real time. Simulation results show that there are no significant differences between non-real time simulations (on a PC) and real time simulations (on a DSP based simulator). This paper also presents design and implementation of a nonlinear control algorithm for excitation control system based on the Lyapunov's direct method. Both conventional excitation control algorithm and proposed nonlinear excitation control algorithm were implemented and tested on the real time simulator. The obtained simulation results show that proposed nonlinear excitation control algorithm better damps electromechanical oscillations than conventional excitation control algorithm.

Computer Aided Design and Implementation of Control Algorithms

The design and testing of an optimal control algorithm, based on scientific models of greenhouse and tomato crop and an economic criterion (goal function), to control greenhouse climate, is described. An important characteristic of this control is that it aims at maximising an economic criterion, determined by the heating and CO 2 supply cost on the one side and the of tomato yield on the other side. Unlike conventional control, the economic criterion, e.g. when energy taxes are included in it, directly leads to predictable energy savings. Whereas growers, using the conventional control, are inclined to optimise the climate for the crop, the economic criterion makes a trade off between yield and costs. The costs are mainly energy costs. As a result the energy efficiency improves.In case of the greenhouse an existing model is modified and extended with a description of the air humidity and a heating pipe model. In case of the crop the number of states of the tomato model of de Koning has been reduced from several hundred to four based on 'reasoned aggregation', to make it suitable for control purposes. This results in a combined greenhouse crop model of nine states. The calibration of this model has been done sequentially. First the tomato model has been calibrated and then the greenhouse model using the output of the tomato model as an input. The different sub-models have been calibrated using independent data. The validation shows that the models predict the trend of the different states well. The value of the states displays a clear deviation, with the exception of the pipe temperature, which is accurately predicted.The time-scales of the greenhouse crop system are very different. Simulations show that neglecting the fast greenhouse dynamics results in a considerable loss of performance. To take these different time-scales and the poor predictability of the weather into account, a time-scale decomposition is used. First a long term optimisation over a growing season is carried out, based on a long term weather prediction, neglecting the greenhouse dynamics. For the first time a solution of the long-term optimal greenhouse tomato crop production problem is presented. This solution is used to adapt the short-term criterion. In the short-term optimisation the greenhouse dynamics are taken into account. The computations are carried out using the 'receding horizon' principle combined with 'lazy man' weather predictions, and an optimal horizon of one hour. This way feedback is introduced into the system and the control becomes real-time implementable.Economic results of experiments with the implemented optimal control algorithm show the applicability of the optimal control. The results were not inferior to the experimental results obtained with the conventional control in a second adjacent greenhouse compartment. The experiments seem unique because, to our best knowledge, never before the climate in a greenhouse has been completely determined by an optimal control algorithm designed on the basis of a greenhouse crop model and an economic criterion. The small difference between experimental and simulated results, despite model discrepancies, indicates the robustness of the algorithm. As an objective comparison of the optimal and conventional control is only possible based on a larger number of experiments, in this research the conventional and optimal control are compared by means of simulation. The simulations clearly show that a considerable amount of the energy consumption is needed for dehumidification (11%). Therefore it is important to choose the humidity bounds adequately. To be able to compare the results the penalty on crossing the 90% upper humidity bound is tuned such that the cumulative crossing for the conventional and optimal controller are equal.The comparison shows that the optimal control of a greenhouse tomato crop production system without heat storage and autonomous availability of CO 2 , when simulated over four characteristic days, improves the energy efficiency by 8.5%. Against a 5% drop of crop yield the energy consumption reduces by 12.5%. The optimal controller explicitly takes into account long-term effects. This may reduce the short term profit. However, assuming that the investments in bio-mass on the plant will finally pay off, extrapolation of the results from these four days shows an average gain in profit of 60%.

Traditionally, renewable energy sources have been connected to the grid by using current-controlled voltage source converters (CC-VSCs) based on phase-locked loops (PLLs). This approach simplifies the design and implementation of control algorithms and it has been well studied in the literature. Nevertheless, the massive integration of CC-VSCs in electrical systems can lead to undesired dynamic interactions. In particular, the connection of CC-VSCs to weak (high-impedance) grids have been thoroughly analysed. However, the connection to low-inertia power networks has received less attention. Therefore, the main issue addressed in this paper is: how do CC-VSCs and power grids with reduced inertia interact? Simple power-frequency models are developed to analyse this issue, especially under transient grid conditions where the existing modelling tools fail to provide a clear answer. Theoretical and experimental results are used to validate the main contributions of this work.

The vertical climber 2 is mobile service robot which is able to walk on flat surfaces in vertical direction. The movement is possible thanks to drive control and pneumatic ejector system. The robot is equipped by sensoric subsystem and the operation could be semi autonomic. The implementation of algorithm is discussed in this paper, especially drive control and precise synchronization with pneumatic components.

To guarantee the adequate interaction between controllers and systems to be controlled, it is imperative that the design, implementation and testing of control algorithms are performed. It is not always posible to have the actual plant for initial testing, algorithm debugging and controller adjustments, because of safety and economical and operational considerations. Using scaled plants is costly and time-consuming and it does not completely eliminate the inherent danger of disruptions and failures caused by the testing of algorithms. A major drawback of testing is the imperative need for interaction between the controller and the plant to be controlled, in order to achieve reliable and safe controller settings. To overcome this drawback, there are different alternatives; among them, the processor-in-theloop (PIL) approach, which allows the observation of more realistic performance of the algorithms at an affordable cost. This paper presents an implementation guide for the PIL approach between Matlab / Simulink and Texas Instruments' C2000 family control hardware. This approach is applied to the evaluation of synchronization algorithms for distributed generation units. In particular, two algorithms for phase-locked loops (PLL) are considered: DSOGI-PLL and DSOGI-PLL with FLL; the Matlab / Simulink PLL is used to validate the implementations. The PLLs are processed in a digital signal controller (DSC) and electrical power system are implemented in Simulink.

Summary form only given. Systems that involve a tight integration of models of physical entities (such as their dynamics), algorithms for controlling them, and computational platforms for implementing these algorithms, are referred to as cyber-physical systems (CPS). Traditionally, control theory or the development of control algorithms, and hardware/software techniques for implementing these algorithms were studied and developed independently by disjoint communities - control theorists on one hand, and embedded systems and software engineers on the other hand. But as embedded platforms become more complex and distributed, this isolated development of the two areas - control algorithm design and control algorithm implementation - is leading to significant integration, testing and debugging costs. This is because assumptions made during the design phase - like negligible time needed to compute the control law, zero-delays between the sensors and controllers and controllers and actuators, availability of infinite numerical precision when computing control inputs, etc. - do not hold during the implementation phase. This leads to a deviation in the expected control performance at the design phase, and what is realized after implementation.In order to address this, CPS-oriented design methods involve a co-design of the control algorithms and hardware/software platforms for implementing these algorithms. Towards this, this tutorial will discuss cross-layer techniques for designing embedded control systems. These layers include control models to code synthesis, and code to implementations on distributed architectures where we will talk about computation, communication and memory-aware design of control systems. Finally, the tutorial will also discuss how reliability issues of modern semiconductor devices may be accounted for during the control design phase. Such a cross-layer design - starting from models of physical systems, to software, to architecture, and finally to reliability of circuits and devices - will result in certifiable and trustworthy cyber-physical systems for which end-to-end guarantees may be offered. The application domains where these techniques may be applied are varied and range over automotive, avionics, industrial automation, and medical technology.Currently there is a tremendous amount of interest in cyber-physical systems. However, this is an interdisciplinary topic involving the intersection of control theory, communications, embedded systems, and embedded software. This tutorial will introduce to an audience with a primarily embedded systems background the opportunities that exist at the intersection of control theory and embedded systems & software - which constitutes the core of cyber-physical systems. Since we will cover all the layers of the design stack - from high-level models, to software, architecture, and then circuits and devices - the tutorial will also highlight verification challenges at all these layers, along with how modeling and analysis techniques from each of these layers may be composed to built certifiable and trustworthy systems. The tutorial will provide enough background to researchers and doctoral candidates - who are new to the topic of CPS - to be able to start exploring problems in this domain. It will also provide a variety of examples that will help practitioners from the industry.

Implementation and investigation of a robust control algorithm for an unmanned micro-aerial vehicle

The superimposition of Estelle programs: A tool for the specification and implementation of observation and control algorithms

The first edition of the Grand Cooperative Driving Challenge (GCDC) was held in the Netherlands in May 2011. Nine international teams competed in urban and highway platooning scenarios with prototype vehicles using cooperative adaptive cruise control. Team Scoop, a collaboration between KTH Royal Institute of Technology, Stockholm, Sweden, and Scania CV AB, Södertälje, Sweden, participated at the GCDC with a Scania R-series tractor unit. This paper describes the development and design of Team Scoop's prototype system for the GCDC. In particular, we present considerations with regard to the system architecture, state estimation and sensor fusion, and the design and implementation of control algorithms, as well as implementation issues with regard to the wireless communication. The purpose of the paper is to give a broad overview of the different components that are needed to develop a cooperative driving system: from architectural design, workflow, and functional requirement descriptions to the specific implementation of algorithms for state estimation and control. The approach is more pragmatic than scientific; it collects a number of existing technologies and gives an implementation-oriented view of a cooperative vehicle. The main conclusion is that it is possible, with a modest effort, to design and implement a system that can function well in cooperation with other vehicles in realistic traffic scenarios.

Active structural acoustic control of repetitive impact noise

Personal computers are increasingly becoming the platform of choice to design and implement control algorithms because it is simple to write, modify and update software programs that implement control algorithms. In this paper, the personal computer is used to control the electrical appliances which includes turning high power alternating current (AC) loads such as lights, fans, heaters etc ON or OFF. To successfully integrate the interface box with the machine (laptop), an interface device is used within the PC that can perform the necessary tasks. The interface box can be controlled by the computer by connecting to the USB port and developed a program in C-sharp(C#) programming language. The program will demonstrate the basic idea of how to control devices and monitor events. With the program, the computer can turn electric devices ON/OFF while disregarding the manual control system. Moreover, the people who are physically disabled in homes and work places are able to control the home appliances by interacting with the interface of the developed appliance. It is a necessity to employ the service of Home Appliances Control as it is more effective, efficient and stress-free.

Inverse kinematics solves the problem of how to control robot arm joints to achieve desired end effector positions, which is critical to any robot arm design and implementations of control algorithms. It is a common misunderstanding that closed-form inverse kinematics analysis is solved. Popular software and algorithms, such as gradient descent or any multi-variant equations solving algorithm, claims solving inverse kinematics but only on the numerical level. While the numerical inverse kinematics solutions are relatively straightforward to obtain, these methods often fail, even when the inverse kinematics solutions exist. Therefore, closed-form inverse kinematics analysis is superior, but there is no generalized automated algorithm. Up till now, the high-level logical reasoning involved in solving closed-form inverse kinematics made it hard to automate, so it's handled by human experts. We developed IKBT, a knowledge-based intelligent system that can mimic human experts' behaviors in solving closed-from inverse kinematics using Behavior Tree. Knowledge and rules used by engineers when solving closed-from inverse kinematics are encoded as actions in Behavior Tree. The order of applying these rules is governed by higher level composite nodes, which resembles the logical reasoning process of engineers. It is also the first time that the dependency of joint variables, an important issue in inverse kinematics analysis, is automatically tracked in graph form. Besides generating closed-form solutions, IKBT also explains its solving strategies in human (engineers) interpretable form. This is a proof-of-concept of using Behavior Trees to solve high-cognitive problems.

Much of the current theory of predictive control deals with ‘optimality’ of predictions and controls based on assumptions which are rarely verifiable in practice. Practitioners on the other hand know the value of simple signal conditioning when dealing with real data. This paper examines the effects of the simplest form of signal conditioning; namely that of a prefilter in the parameter estimator and a so-called ‘observer’ polynomial in the associated control calculations. It is demonstrated that these filters are absolutely vital in the design and implementation of control algorithms. The theoretical results are supported by some illustrative simulations. Two simple counter examples are used to demonstrate the necessity of the observer polynomial even when the unmodelled dynamics is very small.