This paper is focused on the design and verification of the characteristics of a speed control loop with a fractional-order PI controller. The controller parameters are tuned by two methods: the pole placement method and the phase margin method. The fractional-order controller itself has been implemented in two ways, namely using MATLAB FOMCON Toolbox and in the form of finite power series expansion. The characteristics of the speed control loop using both tuning methods and with both types of controllers have been verified experimentally on an industrial servo drive with permanent magnet synchronous motors.
An appropriate design of the model predictive control algorithm for the artificial pancreas to control glycemia in patients with type 1 diabetes should ideally take into account the asymmetrical nature of glycemia dynamics that consequently results in asymmetrical requirements on the control performance with respect to normoglycemia. This demand is primarily physiology involved since the state of hypoglycemia is much more dangerous and easier to reach by careless insulin administration than hyperglycemia. As the traditional formulation of model predictive control considers only symmetrical penalization of the control error, a more sophisticated strategy is required. One of the approaches is to apply hard constraints on the controlled variable. However, the issue with this is that the control may become infeasible if there are considered too tight constraints of the manipulated variable and the controlled variable at the same time. Therefore, in order to ensure the control feasibility while constraining the controlled variable, we proposed a soft constraints formulation introducing special slack variables and a quadratic penalty to the corresponding cost function. Independently weighted soft constraints of the controlled variable with asymmetric bounds are considered while anticipating the desired suppression of hypoglycemia during automated glycemia control. The proposed strategy was validated within a simulation experiment, and it turned out that the soft constraint formulation allowed avoiding hypoglycemia at the expense of mild hyperglycemia.
Robot guidance in industry is a significant issue that needs to be dealt with in modern manufacturing facilities. One of the common tasks in this area is the pick and place problem. For proper implementation of an automatic pick and place application using a robotic arm for object grasping, it is necessary to detect the accurate pose of the objects of interest. In this contribution, a novel engineering approach to object positioning, based on image processing is proposed. In this approach, the operation is composed of a cascade of convolutional neural networks. This cascade consists of 2 different types of networks. The first one is the object detection network called YOLOv5. It is used to process the raw image data from the scene to provide precise localization and determine the position of the objects of interest. After that, crops of the detected objects are created and processed by the second neural network, namely EfficientNet. This classification network is used to determine the rotation angle of the detected objects. The proposed approach provides a precision rate of 0.997 and a recall rate of 0.999 for locating and determining the correct position. For angle classification, EfficientNet provides an accuracy of 0.951. All tests are performed on the testing set of the legitimate positioning problem.
The paper shows how the aeronautical heritage is affected by airborne pollutants and the air temperature-humidity complex. Monitoring meteorological and environmental data in a heritage site intended for protection, these effects are analyzed. The starting point is thus the acquisition of the meteorological and environmental data from the intended hangar with stored historical aircrafts taken part in fights of the second world war. The following step is an evaluation of the penetration of moisture into the hangar environment from outside. This moisture penetration is deducted from wet/dry cycles obtained from air humidity and temperature fluctuations in the hangar environment. In addition, an evolution of the aircraft surface temperature is pertinent to potential moisturizing the aircraft surface. As a measure of moisture penetration, a time of wetness (ToW) is determined indoors. Furthermore, pollutants infiltrated into the hangar environment are derived from a standardized methodology. Beside the pollution infiltration, the indoor generated pollution is estimated per material from which the historic aircrafts are constituted. Because of major material in the aircraft, the deposition rate of indoor pollutants onto an aluminum alloy is determined by means of a standardized statistical model. Finally, monitoring the hangar environment, the heritage aircraft vulnerability to corrosion is estimated based on atmosphere corrosivity modeling.
The success of the real-world implementation of advanced control policies relies on the robustness of the designed control laws. This paper presents a new software package for the robust model predictive control (MPC) synthesis in the framework of the Multi-Parametric toolbox (MPT) that is representing one of the most successful open-source tools in this field. In this paper, we introduce the Tube MPC design in a user-friendly way. The goal of the paper is to demonstrate that the wide research community may benefit from the ease of the advanced controller design in a few lines of code and its implementation to control the laboratory plant.
The article deals with methods of usability testing of industrial SCADA/HMI interfaces such as graphical operator panels or control room visualization screens. Analysis of the designed HMI screens and quantitative and qualitative testing methods are used. Usability is estimated by measuring the reaction times of the defined test tasks and by the Eye Tracking methods. The proposed method for testing and evaluating the industrial HMI is presented as a case study. The method allows for to improvement the quality of industrial HMI. Especially its usability, reliability, and efficiency.
The development of dynamic models based on physics is one of the biggest problems that real time optimization (RTO) faces for industrial processes. In this work, we develop a new approach by using system identification for the closed-loop dynamics in the optimization algorithm to overcome such a problem. The identification is done by using transfer functions and Wiener models with piece-wise linear output functions to generate multi-model for different cases of disturbances or other measured external inputs. These models are used to calculate optimal references to be used in RTO. The new procedure is applied to a continuous stirred tank reactor (CSTR). The numerical simulations show the effectiveness of the implemented method by comparing it with previous work from literature.
This paper proposes a design strategy of hierarchical architectures for discrete-event control of complex industrial systems. The system behaviour complexity is addressed by modelling its operation in a two-dimensional space built by operation-mode (i.e., degree of user intervention) and operation-state (i.e., operational conditions) dimensions as proposed by the ISA-S88.01 control standards. Procedural control modules are synthesized using Procedural Control Theory. The notion of a "safe state" is employed to guarantee the system evolution along the two-dimension operation domain. To demonstrate the design strategy, a two-level architecture is built to operate a tubular continuous reactor. The architecture was implemented and tested with Stateflow in Simulink-MATLAB and translated to structured language in the Siemens TIA portal environment for an S1200-PLC following IEC61131 guidelines.
TelePresence devices enable their users to appear and move at distant locations. In this work, to achieve TelePresence, we installed a 360° camera on a mobile robot and streamed the video feed to a virtual reality headset. The user’s view field is outsourced, which is disadvantageous for traditional control methods. To overcome this issue, here we designed an sEMG-based control paradigm, enabling users to control a robot with hand gestures. We created a dataset of the necessary control commands, by recording the visual representation of the hand and the corresponding sEMG activity. The final dataset consisted of 5200 train and 400 validation samples. We investigated several neural network architectures to decode the biosignals and finally applied a CNN + LSTM Recurrent Neural Network to control a mobile robot by hand sEMG signals.
This paper presents a software tool for the automatic testing of PI(D) autotuners. The described tool was created in the REXYGEN system. The measured data were subsequently processed in MATLAB. The PID_Compact controller from global automation producer Siemens and PIDMA controller from Czech SME company REX Controls were tested. The autotuning methods were compared on a subset of well-known PI(D) control benchmarks by K. J. Åström and T. Hägglund. The presented results show the number of failed tuning experiments or the number of stable and unstable closed-loops for both controllers. Furthermore, the time of tuning experiments and frequency quality indices are compared. According to the results, the PIDMA autotuner significantly outperforms the Siemens, and the stability margins also prove the robustness of PIDMA.
The paper is devoted to the education and teaching of process control and automation. Various laboratory equipment is used to explain and better understand the theory and to gain practical experience. The authors have designed and developed a simple electrical dynamical system RCDue (dynamic model with passive RC components and Arduino Due as measurement and communication unit) that allows students to perform various laboratory experiments – e.g. static and dynamic characteristics measurements, modeling, experimental identification, control design and application of from the simplest strategies to advanced methods. Specifically, in this paper, the authors focus on experimental identification.
Symmetries of linear MPC problems are reflected in symmetries of their explicit solutions. We recently showed these symmetries also appear in the set of the active sets that define the explicit solution. Consequently, symmetries can be used to speed up algorithms for the calculation of the set of active sets. In this paper, we exploit symmetries to accelerate the approach from [1] by improving the exploration of the combinatorial tree of active sets. The reductions of the computational effort that can be achieved are illustrated and analyzed with two examples.
First, the paper introduces a specific class of controllers with two tunable parameters called affine controllers, which includes almost all controllers with a fixed structure commonly used in industrial practice, including PI(D) controllers. The main result of this paper presents a new analytical method for the design of the $H_{\infty}$ affine controller based on a description of the boundary of the $H_{\infty}$ region in the parametric plane of the controller. A user-friendly interactive implementation of this method, supporting multiple system models, is available at www.pidlab.com. The use of this tool is also illustrated by an example.
This paper showcases the use of homomorphic encryption (HE) scheme for securing process data during the controller evaluation in a simulated untrusted cloud environment. The controller implemented in this work is a neural network (NN) that mimics a model predictive controller (MPC) designed for disturbance rejection. Firstly, an MPC was designed for a process of biochemical reactor. From obtained MPC control data, a neural network (NN-MPC) with fully connected layers was trained. Multiple HE-friendly activation functions were tested during the NN training and testing, and based on the results, a polynomial approximation of hyperbolic tangent was used. Subsequently, the NN-MPC controller was implemented in encrypted control scenario. The measured states of the biochemical reactor were encrypted on the side of the process and sent for the homomorphic evaluation to the simulated cloud (NN-MPC).
Offline trajectory optimization of a robotic leg’s jump over an obstacle, using an iterative linear quadratic regulator (iLQR), is presented. The algorithm itself is modified by formulating the running and final cost as a single function of the system’s outputs while dynamics of the nonlinear system are modeled using the open-source physics engine MuJoCo. A redesign of the robotic leg with a parallel linkage configuration is also presented. The leg is based on the open-source Doggo project of Stanford University and intended for a quasi-direct-drive quadruped. The redesign of some of its mechanical parts focuses on increasing geometric accuracy and durability while, more importantly, reducing overall weight. An experimental setup for testing the vertical dynamics of the redesigned leg’s movement is consequently described.
This contribution focuses on a problem that appears when using a relay with non-symmetric output in the closed loop. Such a scheme is usually used for process model parameters identification, possibly followed by automatic controller tuning. Whenever static or dynamic properties of the process reveal asymmetry when the sign of the input changes, the setpoint (reference) becomes different from the operating point value of the process output. As a class of relay-based identification methods utilize calculations in the frequency domain that are based on integral computation around the operating point, the discrepancy between the setpoint and the operating point can lead to incorrect results. The aim of the paper is mainly to provide the reader with problem formulation and step-by-step proposition of how it can be solved. Concise numerical examples are also given. The concluding remarks suggest possible further ways of research.
This paper deals with the problem of defining the Power Purchase Agreement (PPA) that enables power exchange between two or multiple parties. Generally, PPA is a long-term contract with predefined price and power profiles. Energy reliability risk needs to be properly considered to make PPA more attractive to a potentially interested party. That is the reason, why we decided to propose the concept of a so-called dynamical power purchase agreement. The method provides short-term contracts where parameters can change as often as needed. Dynamical PPA should allow each party to selfishly minimize its cost or maximize its revenue while producing a consensus solution. The dynamic PPA is formulated as a model predictive control optimization problem in a multi-objective fashion, solved by the Pareto front and method of global criterion. Results show the potential of such an agreement to react to the power market conditions and still satisfy the needs of each involved party.
The current era of electrification of the aerospace industry brings many challenges, but also brings forward radically new aircraft designs. The new eVTOL and eSTOL concepts were enabled by the distributed electric propulsion. Even though eVTOL and eSTOL projects are gaining a lot of attention, another concept is also notable. The so-called High-Lift propulsion is based on the increase of airflow over the wing, thus reducing landing speed. Most notable example is NASA's X-57 Maxwell. However, this concept can be used to control the aircraft as well. This paper introduces new approach of aircraft control using distributed High-Lift propulsion concept. The controller uses differential thrust to completely control the aircraft states and therefore eliminates the need for traditional control surfaces.
We aim to incorporate data analytics into industrial process control by utilizing machine learning (ML) algorithms to classify the real-time data of online analyzers. Real-time visualization of results onto a front-end system (i.e., refinery control room) provides an extensive view of the production process, increasing efficiency of production. Selected ML classifiers are assessed according to the performance metrics based on individual scores. These parameters, along with the complexity of implementation, provide an adequate pointer for selecting a suitable classifier model to serve as a decision-making tool. In our use case, accurate categorization of measurements provides a cheap validation guideline that would otherwise be not possible. Computed metrics indicate a difficulty to classify the cases when the slight deviations (drifts) occur from real values. Based on the true positivity rate, linear SVM separation is desirable for data drift prediction (64 %), while k-Means is more successful in detecting outliers (65 %) and normal operation (99 %).
This paper deals with modelling of vehicle dynamics and development of control system for a Formula Student all-wheel drive electric vehicle developed by the STUBA Green Team. The aim is to exploit traction of all tyres to achieve a better racing performance and thus innovate the current control system. Proposed models describe the effect of traction forces on tyre loads and the directional stability of the vehicle. The control system was validated in the simulation environment IPG CarMaker. Simulation with equal torque distribution (without torque vectoring) was used as a reference. The simulation results have shown a considerable improvement of vehicle stability in corners.
