A reconfigurable pid controller

Architectures, Tools, and Applications pp Cite as. We survey the Proportional-Integral-Derivative PID controller variants and we switch them in runtime via reconfiguration, as the control requirements change. Depending on the PID variant, e. We rely on a previous published design to shorten the execution cycle of each controller variant, by increasing the number of arithmetic units operating concurrently.

Furthermore, we incorporate a design based on multiplexers that allows for eliminating frequent reconfigurations, which were required in the previous work. Finally, we evaluate our approach in terms of resource utilization and reconfiguration time. Skip to main content. This service is more advanced with JavaScript available. Advertisement Hide. International Symposium on Applied Reconfigurable Computing.

a reconfigurable pid controller

Conference paper First Online: 08 April This process is experimental and the keywords may be updated as the learning algorithm improves. This is a preview of subscription content, log in to check access.

Desborough, L. Ang, K. IEEE Trans. Control Syst. Hang, C. Automatica 28 11—9 CrossRef Google Scholar. Mikkilineni, I. Johansen, T. Jia, B. Otsubo, A. Fuzzy Sets Syst. Vagia, M. Control Eng. Monmasson, E. Economakos, G. Pelc, M. Control Sci. Fons, M. Xilinx: Vivado design suite user guide - partial reconfiguration, UG vSkip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. Using four units to imitate the immune system's surveillance process, response process, memory mechanism, and self-learning process, respectively, the IRC is capable of actuator fault detection and fault-tolerant control for multi-input multioutput systems.

a reconfigurable pid controller

Meanwhile, in order to further improve the control performance, an online optimization process with the multiobjective clonal selection algorithm is designed. To verify its effectiveness, the IRC is applied to the coagulation bath of polyacrylonitrile carbon fiber production line.

Comparison experiments with conventional PID and reconfigurable model-based predictive controller control schemes are conducted. The simulation results demonstrate that the IRC can rapidly eliminate the fluctuation due to the actuator fault and guarantees the stability of the coagulation bath. In addition, the IRC has the ability of quick response to the same failure as well as unknown faults. Article :. Date of Publication: 27 January DOI: Need Help?High performance machines often require specialized control algorithms and advanced synchronization with sensors and vision systems.

These requirements can be difficult or impossible to achieve with fixed function motion controllers and drives, and turning to custom design often isn't feasible. Motion Control: Traditional vs. Reconfigurable Fixed function controllers and drives ship with firmware that implements behavior that cannot be modified by the end user.

These controllers and drives could be optimized for a specific purpose such as driving a CNC endmill spindle, or designed with the intent to be as generic as possible to cover a wide variety of applications within or even across industries.

As long as you operate within the designed use case, these fixed function controllers and drives are usually the most effective choice for implementing an application because you can take advantage of all the design work and feature definition that went into that product, such as advanced filtering, auto-tuning, test panels, diagnostic tools, and a host of other features baked into the firmware.

The problem with fixed function controllers arises when you, as a machine builder, need to step outside the capabilities defined by the motion controller and drive firmware. These scenarios become more common as machines become more specialized and increasingly sophisticated.

The alternative solution machine builders often turn to is custom design. With a custom built motion controller or drive, a machine builder can define exactly the behavior they want to see out of the system.

However, custom design is costly, time-consuming, and has its own set of limitations. Custom solutions can be challenging to build and manufacture requiring a full team of engineers with a specific set of skills. They also include the burden of creating revisions for life-cycle management due to bugs or part availability.

Perhaps the most important limitation is that, because of their specific design, the custom solution is likely not a good fit for new features or for the next project. The result is a continuous cycle of custom design that can be difficult to break, and this is a problem, especially for smaller companies that need to be as agile and lean as possible in their operation.

Alternatively, the design and manufacturing can be outsourced to a third-party company, but this approach is expensive and can expose any specialized IP. So how do you combine the full feature sets and performance of fixed function devices with the ability to customize them as needed for specified applications?

You need a persistent framework that can be developed along the lines of a fixed function device with lots of testing, iterative feature improvements, and the like, but that also is modular such that components of the system can be completely defined by the user to meet very specific and demanding application needs.

The progression of computing technology and reconfigurable embedded design tools specifically FPGA technology makes this combination of fixed functionality and custom design benefits possible.

A diagram of this architecture is shown in Figure 1. Taking this concept to software is also important. The ability to reconfigure a standard framework; customizing where necessary but still utilizing the rest, is very useful for machine builders.

Understanding PID Control, Part 1: What is PID Control?

In this software architecture, motion tasks are disaggregated so that largely you can choose where to run a particular task to meet the needs of the application.Explore the fundamentals behind PID control.

PID is just one form of feedback controller.

a reconfigurable pid controller

That is why PID is the most prevalent form of feedback control across a wide range of physical applications. So, this video skips most of the math and instead focuses on building a solid foundation. We start with a plant. This is what we call the system that we want to control, or the system whose behavior we want to affect. The input into the plant is the actuating signal and the output is the controlled variable. Different industries refer to these signals by various names, so you might hear them called something else, like plant input and plant output, but regardless of the names, the basic idea of a control system is to figure out how to generate the appropriate actuating signal, the input, so that our system will produce the desired controlled variable, the output.

That is basically the job of the control engineer, produce the right input into the system to get the output you want. And just like before output you want also goes by various names. Here, I call it the command or the commanded variable, but you might also hear it as the set point, the reference, or the desired value.

And in feedback control, the output of the system is fed back, hence the name, and compared to the command to see how far off the system is from where we want it to be. This difference between the two is the error term. If the output was exactly what we commanded it to be, then the error would go to zero. That is what we want, zero error. So, the question is, how do we take this error term and convert it into suitable actuator commands so that over time, the error is driven to zero?

The answer is with a controller. Imagine you are standing on the goal line of a soccer field and you want to walk to the half field line, 50 meters away.

A Reconfigurable PID Controller

In this case, you are the plant, the actuating signal is the speed and direction that you walk, your current location on the field is the output variable or 0 meters to start, and then 50 meters is the command. Therefore, at the beginning, your position error is 50 meters, or 50 minus 0. You still have a ways to go. Your brain, being the controller, tells your legs how fast to walk and one way your brain can do this is to use the error at the present moment to decide your walking speed.Two typical plants usually found in industrial applications are used to test the controllers and the results are compared with those obtained with the software MATLAB.

The PID has been thoroughly researched and is a well understood algorithm. We can mention numerous analysis tools, tuning techniques, and simulation tools available to analyze and design PID controllers [ 123 ]. The emergence of dynamically reconfigurable programmable analog circuits has been added to the options available to designers for implementing PID control loops. Their use increases the analog design integration and productivity, reducing the development time and facilitating future hardware reconfigurations reducing the costs.

These technologies are very recent and are in rapid development to achieve a level of flexibility and integration to penetrate more easily the market. The applications of this technology are wide and include signal conditioning, filtering, data acquisition, and closed-loop control [ 4567891011121314 ]. The plant models are also implemented in a dpASP device.

The paper is organized as follows. Finally, Sect. In this work we use the Anadigm QuadApex development board from Anadigm manufacturer [ 15 ]. Furthermore, with its 32 bit PIC32 microcontroller and four dpASP devices, it provides a powerful platform to develop programmable analog designs [ 16 ]. Ziegler and Nichols suggested two heuristic methods for the tuning of PID controllers [ 23 ].

Design of sTetro: A Modular, Reconfigurable, and Autonomous Staircase Cleaning Robot

Compare the results of the open-loop step responses with both methods. Compare the results of the closed-loop step responses using the PID parameters from Z-N tuning rules.

In fact, the design of the PID controller can be done at a block level, and the simulation and test of each circuit takes only few minutes without the need to do complex mathematical calculations, choosing discrete components or focus on analog circuit details.

a reconfigurable pid controller

Also, the output of the proportional and integrator blocks should be monitored using probes during simulation as saturation can occur easily after increase of proportional and integral gains. The obtained step responses using both methods show the quarter decay amplitude between the first and second oscillation which is in accordance with the Z-N methods.

Skip to main content Skip to sections. This service is more advanced with JavaScript available. Advertisement Hide. Conference paper First Online: 04 September Download conference paper PDF. These structures are constructed from a combination of conventional Switched-Capacitor SC circuit elements and are programmed from off-chip non-volatile memory or by a host processor.The mechanical, electrical, and autonomy aspects of designing a novel, modular, and reconfigurable cleaning robot, dubbed as sTetro stair Tetroare presented.

The developed robotic platform uses a vertical conveyor mechanism to reconfigure itself and is capable of navigating over flat surfaces as well as staircases, thus significantly extending the automated cleaning capabilities as compared to conventional home cleaning robots. The mechanical design and system architecture are introduced first, followed by a detailed description of system modelling and controller design efforts in sTetro.

A staircase recognition algorithm is presented to distinguish between the surrounding environment and the stairs. The misalignment detection technique of the robot with a front staircase riser is also given, and a feedback from the IMU sensor for misalignment corrective measures is provided. The experiments performed with the sTetro robot demonstrated the efficacy and validity of the developed system models, control, and autonomy approaches.

Due to a faster pace of life in most of the developed world, floor cleaning is often seen as a dull, dirty, laborious, time-consuming, and tedious job Figure 1 a giving rise to the development of robotic products for handling the cleaning task autonomously.

Such robotic platforms have given their vast potential by improving productivity in cleaning jobs in domestic and commercial settings and witnessed a steep rise over the last two decades [ 1 ]. It is estimated that between andabout Numerous research literature deal with different aspects of floor cleaning robots such as the mechanism design [ 3 — 5 ], autonomy [ 67 ], human-robot interaction studies [ 89 ], multirobot teams [ 1011 ], and benchmarking strategies [ 1213 ].

What is PID controller ? How to tune a PID Control loop ? How to program a PID Loop ?

Even though there exists such literature demonstrating the benefits of floor cleaning robots, the conventional floor cleaning robots suffer from serious performance issues that curtain their full potential dexterousness.

One major factor attributing to their performance loss is their inability to access staircases that form the integral part of almost every built infrastructure. Considering the crucial role staircases play and their permanent presence even after the advent of lifts and escalators virtually in every multistory building, the subject of cleaning staircases has received little attention from roboticists. One viable approach to overcome this bottleneck is to design next-generation cleaning robots that are able to reconfigure themselves between floor and staircase cleaning modes, thereby maximizing their dexterous task performance.

A number of design mechanisms towards realizing staircase climbing robots have been proposed and validated [ 14 — 18 ]. However, these robots target mostly search, rescue, and security applications using design principles and mechanisms that are not optimal for cleaning tasks. A very limited research effort has been made towards the design of staircase cleaning robots. An autonomous cleaning robot is proposed in [ 19 ] capable of accessing both flat floors and staircases.

This robot consists of a rectangular-shaped body frame with L-shaped legs on both sides of the frame and moves forward and climbs stairs by rotating the body so that the top and bottom sides of it may be reversed using L-shaped legs. Megalingam et al. The work describes a case study with the design of one such robot that can function in a 3D space.

However, these robotic platforms that target staircase cleaning are complex and suffer from serious design and performance issues. The sTetro a staircase cleaning robot is a novel modular reconfigurable cleaning robot, which uses a vertical conveyor belt mechanism and is capable of navigating over flat surfaces e. The working principle concept of sTetro is borrowed from the Tetris Tiling Theory [ 21 ]. The blocks have a modular design, which allows reusing many pieces in the system.

An intelligent application allows the user to control the robot to start and stop, navigate to follow a defined path, and change the operational configuration, as desired. The reconfiguration mechanism allows the sTetro robot to efficiently clean both the floor and the stairs at one time without user intervention, which is almost impossible in presently available home cleaning robots in the market.

In this paper, the mechanical design and system architecture of sTetro are introduced, followed by a detailed description of system modelling and controller design efforts.Jonah Goodhart, SVP, Oracle Data Cloud Eric Roza, General Manager and SVP, Oracle Data Cloud Interviewed by: Ryan Joe, Managing Editor, AdExchangerInvestment drives innovation as industry participants know well.

With that in mind, Dan Salmon provides his Wall Street perspective on some key trends to consider across the marketing technology landscape in the year to come. Dan Salmon, Equity Research Analyst, BMO Capital MarketsThis featured fireside chat will be announced shortly and include a discussion with a key industry leader in the year ahead.

Guided by his unique journey from corner store to corner office, SAP CEO Bill McDermott will present his analysis on the state of marketing as the enabler of the consumer-driven growth revolution.

He will also share secrets from his boardroom conversations with Fortune 1000 CEOs around the world. Bill McDermott, CEO, SAPJohn Ebbert, Publisher, AdExchangerGoogle VP Brad Bender will discuss the company's evolving advertiser business. How does NBCU juggle traditional "Upfront" sales, advanced TV, digital programmatic and cooperative sales with key partners like Apple and Vox.

Jon Suarez-Davis, Chief Strategy Officer, Salesforce Marketing Cloud, SalesforceIBM continues to augment and develop its technology strategy in service to marketers. This fireside chat looks at "what's next" through the lens of the company's Chief Digital Officer, Bob Lord. Bob Lord, Chief Digital Officer, IBM Interviewed by: Zach Rodgers, Executive Editor, AdExchangerThe last few years have given the "principals" in the programmatic transaction - marketers and publishers - plenty of reasons to question its value.

From brand safety to ad fraud to agency rebates and price transparency issues, ad tech has had its share of trouble. And yet the industry continues to grow in spite of it all. Is 2018 the year the technology ecosystem moves beyond its "trust issues".

This panel will explore the question, and provide a roadmap for how to move forward. Barrett, CEO, Rubicon Project Brian O'Kelley, CEO and Co-Founder, AppNexus Brett Wilson, VP and General Manager, Advertising, Adobe Moderated by: Sarah Sluis, Senior Editor, AdExchangerThe General Data Protection Regulation (GDPR) is an effort by the European Commission to shift the balance of power in favor of consumers so that they can determine and command when a company can or cannot use their personal data to drive business decisions.

This session will explore how your strategies, technologies and operations must change to be compliant with the Regulation, and how good, early preparation will help you thrive in this stricter environment. Melissa Parrish, VP, Research Director, ForresterA special presentation from the native advertising software company, Sharethrough. Dan Greenberg, CEO, SharethroughGreat Chief Marketing Officers (CMOs) are hard to find - ask any board. Blodget will discuss media trends in the year ahead as well as provide an inside view on the next steps for BI under owner Axel Springer SE, the leading digital publisher in Europe.

Henry Blodget, CEO and Editor, Business Insider Interviewed by: Sara Fischer, Media Reporter, AxiosQuantcast Co-Founder and CEO Konrad Feldman looks at the year ahead as his company pursues a strategy based on artificial intelligence (AI) technology and its application for brands, agencies and publishers. Konrad Feldman, Co-Founder and CEO, QuantcastCBS is looking at creating new and improved ways in 2018 for their clients to buy their TV show lineup and all CBS content across its broadcast and digital platforms.

JoAnn Ross will discuss her company's plans and the important trends she sees in the year ahead. Getting there requires industry collaboration, strong data science and airtight privacy. The CEOs of some of the world's largest audience and data platforms will survey the landscape, discussing how they and their peers are moving toward a world of "known" users.

He'll provide insights on the huge opportunity ahead he sees for his company. In this fireside chat, Steve King, Publicis Media's CEO, will discuss his view on how the agency is achieving its mission. Steve King, CEO, Publicis Media Interviewed by: Brian Wieser, Sr. Brian Andersen, Partner, LUMATwo days focused on what to expect in the next 12 months in marketing technology. Hear the leaders of the advertising and marketing technology ecosystem reveal new visions for 2018 and beyond.

thought on “A reconfigurable pid controller”

Leave a Reply

Your email address will not be published. Required fields are marked *