Development Of Control Algorithms Research Articles

Modular Multilevel Converter With Parallel Branch Providing Integrated Partially Rated Energy Storage

In the line of research on inclusion of Energy Storage Systems (ESS) into HVDC stations, this paper presents the integration of partially rated ESS into Modular Multilevel Converter (MMC) substations using parallel branches to the arm inductors. The proposed ESS-MMC topology keeps the design of the original MMC unmodified, as it just adds partially rated ESS with electrical connections outside the valve hall. The system utilizes another frequency component in the arm circulating current to exchange power between the energy storage branches and the original MMC stacks. This third frequency component allows decoupling to some extent the power on the dc and ac sides of the substation. Analysis of the circuit performance led to the development of control algorithms to maximize the power flow capabilities from the ESS within the operational limits of the existing MMC design. This allows the addition of up to 37% power from the ESS considering similarly rated power semiconductors in a simulated 1 GW MMC substation. Analysis shows that extra device losses are kept under 1% for an addition of 10% of ESS power to the nominal substation power. Therefore, the proposal is suitable for adding services such as fast frequency response or black start capability to existing MMC designs.

Event-triggered controller design for active suspension systems: An adaptive backstepping method with error-dependent gain

This paper studies an adaptive control problem for active suspension systems (ASSs) with parameter uncertainties based on the event-triggered mechanism (ETM). The main focus of this work is to improve the transient response of the body and save the communication resources of the in-vehicle network. The controller area network (CAN) is the most widely utilized in-vehicle communication network in automotive systems, so it is a vital issue that how to improve the utilization of the CAN resources since the CAN has a low bandwidth as a shared media. To this end, we present two error-dependent fixed and relative threshold ETMs to achieve satisfactory suspension performance while significantly reducing the communication burden on the CAN. Then, in the recursive design process, the backstepping strategy with error-dependent gain is employed to improve the transient response of the systems. Meanwhile, the radial basis function neural networks are adopted to identify the unknown nonlinearities in ASSs. Finally, the developed adaptive control algorithm ensures that all the signals in the closed-loop suspension systems are bounded in presence of uncertainties. Simulation results based on the professional vehicle simulation software Carsim verify the effectiveness of the presented scheme.

Development of a control algorithm for active control of rolling motion of a ship using a gyrostabilizer

In this study, a new control algorithm for an active gyrostabilizer was developed and applied to a ship model. The active gyrostabilizer consists of a gyro sensor that measures the angular rate of the rolling motion of a ship, a controller that realizes the active control algorithm, and an actuator that is connected to the precession axis. The new control algorithm receives the angular rate of the ship, then outputs the desired precession angle. A suitable actuator for this kind of mission could be a servomotor. Based on the concept of tuned mass damper, a new control algorithm for an active gyrostabilizer was developed. It was theoretically found that instead of increasing stiffness, the damping of the rolling motion of the ship could be increased by tuning the filter frequency of the control algorithm to the rolling frequency. The results of both numerical and experimental investigations showed that the proposed control algorithm is valid and can be used to effectively suppress rolling motion with a relatively low speed or light spinning wheel compared to passive gyrostabilizers.

Preface

International Workshop “Mathematical Modeling and Scientific Computing: Focus on complex processes and systems” (dedicated to the memory of Nikolai Botkin) Munich, October 4-7, 2022 This proceedings volume contains papers presented at the second International Workshop “Mathematical Modeling and Scientific Computing: Focus on complex processes and systems”, MMSC 2022, dedicated to the memory of Nikolai Botkin (22.03.1956–14.09.2019). Nikolai Botkin contributed significantly to mathematical modeling of various physical and biological processes, to the development of numerical methods and algorithms for optimal control of mechanical systems described by ordinary differential equations as well as for complex distributed systems, optimization and nonlinear programming, elasticity theory, hydrodynamics, thermodynamics, and homogenization theory.The first International Workshop MMSC 2020 was held on November 19-20, 2020 at the Technical University of Munich. The proceedings were published with CEUR-WS.org https://ceur-ws.org/Vol-2783/The second workshop MMSC 2022 was held on October 4-7, 2022 at the Technical University of Munich, Germany, in virtual format. Following the scientific interests of Nikolai Botkin, the workshop topics covered mathematical modeling and computer simulation for a wide range of optimal control and trajectory optimization problems, linear and nonlinear programming issues, as well as comprehensive applications in biology, medicine, thermodynamics and aerospace engineering. Recent achievements in machine learning and neural network technologies, challenges in simulation of cerebral blood circulation and bacterial quorum sensing were presented and discussed.In total, 35 invited talks of 77 authors from 12 countries were delivered. Out of 22 papers submitted to Morressier platform, 22 were accepted after peer-review.The editors would like to cordially thank the members of the Program and Organizing Committees, the participants, and the reviewers for actively contributing to the success of MMSC 2022.Editors of the MMSC 2022 ProceedingsVarvara Turova, Andrey Kovtanyuk, Johannes ZimmerList of Program Committee, Organizing Committee are available in this pdf.

Feasibility Conditions-Free Prescribed Performance Decentralized Fault-Tolerant Neural Control of Constrained Large-Scale Systems

This article investigates the command filter-based decentralized prescribed performance adaptive fault-tolerant compensation control strategy for uncertain nonlinear large-scale systems subject to asymmetric time-varying full-state constraints. Via integrating the performance function with command filter-based backstepping technique, the prescribed performance control problem is addressed, under which the complexity of controller design is reduced. Under the prescribed performance control framework, the nonlinear transformed function is constructed so as to ensure that the asymmetric time-varying full-state constraints free from feasibility conditions imposed on virtual control signals are not violated. Besides, the effect of infinite number of time-varying actuator faults is compensated with the aid of projection adaption design. Furthermore, based on the piecewise Lyapunov function analysis, it is rigorously testified that entire involved signals are bounded, desired constraints are not breached and tracking errors within the predefined domains. Finally, the effective performances of the developed control algorithm are confirmed by some simulation results.

Development and Experimental Validation of Control Algorithm for Person-Following Autonomous Robots

Automatic guided vehicles, in particular, and industrial autonomous mobile robots, in general, are commonly used to automate intralogistics processes. However, there are certain logistic tasks, such as picking objects of variable sizes, shapes, and physical characteristics, that are very difficult to handle fully automatically. In these cases, the collaboration between humans and autonomous robots has been proven key for the efficiency of industrial processes and other applications. To this aim, it is necessary to develop person-following robot solutions. In this work, we propose a fully autonomously controlling autonomous robotic interaction for environments with unknown objects based on real experiments. To do so, we have developed an active tracking system and a control algorithm to implement the person-following strategy on a real industrial automatic-guided vehicle. The algorithm analyzes the cloud of points measured by light detection and ranging (LIDAR) sensor to detect and track the target. From this scan, it estimates the speed of the target to obtain the speed reference value and calculates the direction of the reference by a pure-pursuit algorithm. In addition, to enhance the robustness of the solution, spatial and temporal filters have been implemented to discard obstacles and detect crossings between humans and the automatic industrial vehicle. Static and dynamic test campaigns have been carried out to experimentally validate this approach with the real industrial autonomous-guided vehicle and a safety LIDAR.

Field programmable gate array hardware in the loop validation of fuzzy direct torque control for induction machine drive

Introduction. Currently, the direct torque control is very popular in industry and is of great interest to scientists in the variable speed drive of asynchronous machines. This technique provides decoupling between torque control and flux without the need to use pulse width modulation or coordinate transformation. Nevertheless, this command presents two major importunities: the switching frequency is highly variable on the one hand, and on the other hand, the amplitude of the torque and stator flux ripples remain poorly controlled throughout the considered operating speed range. The novelty of this article proposes improvements in performance of direct torque control of asynchronous machines by development of a fuzzy direct torque control algorithm. This latter makes it possible to provide solutions to the major problems of this control technique, namely: torque ripples, flux ripples, and failure to control switching frequency. Purpose. The emergence of this method has given rise to various works whose objective is to show its performance, or to provide solutions to its limitations. Indeed, this work consists in validation of a fuzzy direct torque control architecture implemented on the ML402 development kit (based on the Xilinx Virtex-4 type field programmable gate array circuit), through hardware description language (VHDL) and Xilinx generator system. The obtained results showed the robustness of the control and sensorless in front of load and parameters variation of induction motor control. The research directions of the model were determined for the subsequent implementation of results with simulation samples.

STUDY OF THE ILLUMINATION AND DEVELOPMENT OF RECOMMENDATIONS FOR ITS IMPROVEMENT

Goal. Investigate the existing methods of lighting rooms of various search purposes, make effective decisions on increasing the energy efficiency of electrical engineering systems of internal education through the implementation of automated education management systems and energy-efficient management algorithms, explain the principles of lighting construction using natural lighting and the application of modern technologies of familiar and modern lighting devices.
 Task. Analysis of existing technical solutions to increase the energy efficiency of the internal lighting system and substantiation of their energy efficiency indicators.Synthesis of the internal lighting control system of the administrative building. Development of algorithms for energy-efficient control of the internal lighting system taking into account external factors.
 The result of the study. The work is devoted to the analysis of lighting methods and devices used today, as well as the search for effective solutions to increase the energy efficiency of the electrical system of internal lighting due to the implementation of an automated lighting control system and energy-efficient control algorithms, an explanation of the principles of lighting construction using natural lighting and modern application technologies conventional and modern lighting devices, as well as methods of improving lighting efficiency using the latest technologies from existing lighting devices. The main attention is paid to the problems of the currently existing methods of artificial lighting of rooms in houses of various purposes, and to the solution of these problems with the most effective and real methods in practical use. This article may be useful for those interested in upgrading existing lighting or building a new lighting system with efficient use of electricity.
 Conclusions and practical significance. In this work, an analysis of existing energy-efficient technical solutions in interior lighting systems was carried out, based on the results of which it can be concluded that it is appropriate to use LED lamps in the lighting system due to their excellent indicators of luminous flux, light output, long service life and affordable price. It was also a source of various energy-saving methods in lighting systems, which can be attributed to: the introduction of half-light, LED lights and fluorescent lamps, the installation of electronic start-up control devices, more efficient use of light output, the introduction of a system of automatic lighting regulation depending on external factors and maintaining efficiency. education system.

Aerodynamics of Landing Maneuvering of an Unmanned Aerial Vehicle in Close Proximity to a Ground Vehicle

<div class="section abstract"><div class="htmlview paragraph">Autonomous takeoff and landing maneuvers of an unmanned aerial vehicle (UAV) from/on a moving ground vehicle (GV) have been an area of active research for the past several years. For military missions requiring repeated flight operations of the UAV, precise landing ability is important for autonomous docking into a recharging station, since such stations are often mounted on a ground vehicle. The development of precise and efficient control algorithms for this autonomous maneuvering has two key challenges; one is related to flight aerodynamics and the other is related to a precise detection of the landing zone. The aerodynamic challenges include understanding the complex interaction of the flows over the UAV and GV, potential ground effects at the proximity of the landing surface, and the impact of the variations in the surrounding wind flow and ambient conditions. While a large body of work in this area can be found on the control aspect of the UAV landing and takeoff maneuvers, research on the aerodynamic aspects of such maneuvers is non-existent. This paper presents an in-depth computational fluid dynamics (CFD) based aerodynamic characterization of the transient flow fields associated with the landing of a hobby-model quadcopter (the UAV) on an idealized road vehicle (the GV), the 35-degree slant angle Ahmed body. Transient improved delayed detached eddy simulations (IDDES) are carried out using the commercial CFD code STAR-CCM+. Our study indicates that the pressure field is the first flow property that gets impacted by the proximity of the UAV to the GV.</div></div>

Pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms

Recent accessibility of affordable sensing technologies, microcontrollers, and wireless communication technology has made it possible for stormwater systems to be retrofitted with an assortment of sensors and actuators. These smart stormwater systems have enabled the real-time sensing of their surrounding environmental dynamics, and subsequently, provide the basis for autonomous and adaptive operational control strategies. Additionally, these systems allow for inexpensive and minimally-invasive stormwater control interventions (e.g. hydraulic valve operated by cellularly-connected actuator) in lieu of new construction. However promising this area of smart stormwater control, there still remain barriers -- for experts and novices alike -- to access a set of shared tools and methods for investigating, developing, and contributing to it. In an effort to make smart stormwater control research more methodical, efficient, and accessible, we present pystorms, a Python-based simulation sandbox that facilitates the quantitative evaluation and comparison of control strategies. The pystorms simulation sandbox comes with (i) a collection of real world-inspired stormwater control scenarios on which any number of control strategies can be applied and tested via (ii) an accompanying Python programming interface coupled with a stormwater simulator. For the first time, pystorms enables rigorous and efficient evaluation of smart stormwater control methodologies across a diverse set of watersheds with only a few lines of code. We present the details of pystorms here and demonstrate its usage by applying and evaluating two stormwater control strategies.

Command-Filtered Neuroadaptive Output-Feedback Control for Stochastic Nonlinear Systems With Input Constraint.

In this article, an adaptive neural-network (NN) command-filtered output-feedback control strategy is proposed for a class of stochastic nonlinear systems (SNSs) with the actuator constraint. The problem of "explosion of complexity" existing in the conventional backstepping design procedure for SNSs is successfully resolved based on the command filter technique, and the error compensation mechanism is introduced to remove effectively the influence of filtered error. By using the NNs to identify the unknown nonlinear functions, a neural-network-based state observer is designed to estimate the unmeasurable states of the SNSs. Based on the quartic Lyapunov function, the stability of stochastic closed-loop systems is analyzed. It is proved that all signals of the closed-loop systems are bounded in probability, and the tracking error approaches a small neighborhood of the origin in probability. Finally, the effectiveness of the developed control algorithm in this article is verified by a comparison example.

Development of Transmission Control Algorithm for Power Shuttle Transmission System for an Agricultural Tractor

A tractor is a versatile machine and is used in many applications other than agriculture, like in mining, transportation, and construction. One such application is with front end loader where it requires a frequent change in direction of motion. To meet this requirement a power shuttle transmission system is used in agricultural tractors. The present research focuses on the transmission control algorithm development for one such a power shuttle transmission system. The development and testing of the control algorithm were carried out in MATLAB & Simulink environments with an additional specific strategy for the clutch pressure modulation. Further National Instruments CompactRIO (NI-cRIO) was used to verify the system’s functionality in a real-time environment. The obtained simulation results were in close accordance with the real system in terms of system functionality and response time. The alikeness between the simulated and actual response time varies from − 20 to 11% and the tractor intended functionality is verified as per the developed control logic. This research provides an approach for the development of control strategies for tractor transmission systems and their rapid prototype development and testing.

Magnetorheological fluids: A concise review of composition, physicochemical properties, and models

Magnetorheological fluids (MRFs) are classified as intelligent materials whose rheological and mechanical properties can be modified by interaction with an external magnetic field. These unique features allow for a controlled change of their viscosity, which is applied in technology to build adaptive devices and effectively suppress vibrations in various mechanical systems. In this paper, we overview and discuss our previous results regarding advances and physicochemical MRF properties in the context of broader literature. We concentrated on such properties as flow, yield strength, and viscoelastic behavior under shearing flows. We briefly discussed continuum and discrete MRFs modeling. Since the magnetic core is mainly based on iron or its compounds, depending on its chemical composition, morphology, stabilizing agents, and the liquid medium’s viscosity, its rheological and micromechanical properties can be moderated. To predict the behavior of such a fluid, it is necessary to propose and implement an appropriate model. Simple models like Bingham can consider the quasi-static and dynamic behavior of the MRFs, while discrete models are applied to the development and implementation of the MRF control algorithms. Thus, analytical and numerical simulation compromise the accuracy, quantity of considered phenomena, and computational cost.

A Surgical Robotic Framework for Safe and Autonomous Data-Driven Learning and Manipulation of an Unknown Deformable Tissue with an Integrated Critical Space

Aside from reliable robotic hardware and sensing technologies, to successfully transition from teleoperation to an autonomous and safe minimally invasive robotic surgery on unknown Deformable Tissues (U-DTs), various challenges need to be simultaneously considered and tackled to ensure safety and accuracy of the procedure. These challenges mainly include but are not limited to online modeling and reliable tracking of a U-DT with integrated critical tissues as well as development of reliable and fast control algorithms to enable safe, accurate, and autonomous surgical procedures. To collectively and simultaneously address these challenges and toward performing an autonomous and safe minimally invasive robotic surgery in a confined environment, in this paper, we present a surgical robotic framework with (i) real-time vision-based detection algorithm based on a Convolutional Neural Network (CNN) architecture that enables tracking the time-varying deformation of a critical tissue located within a U-DT and (ii) a complementary data-driven adaptive constrained optimization approach that learns deformation behavior of a U-DT while autonomously manipulating it within a time-varying constrained environment defined based on the output of the CNN detection algorithm. To thoroughly evaluate the performance of the proposed framework, we used the da Vinci Research Kit (dVRK) and performed various experiments on a custom-designed U-DT phantom with an arbitrary deformable vessel embedded within the phantom’s body (serving as the U-DT’s integrated critical space). Various experiments were conducted and analyzed to demonstrate the performance of the proposed framework and ensure robustness and safety while performing an autonomous surgical procedure.

FLL Unified AVF and NAAP Filter for Performance Investigation of Renewable Based AC Microgrid Under Grid to Standalone Transition Mode

In this work, grid synchronization control, islanded microgrid operation and power surety to the residential/critical loads are ensured for each microgrid operating stage i.e., off-grid, grid associated and seamless transfer. The microgrid under study consists of photovoltaic power generation unit and wind power generation unit that power the consumer loads under on-grid and grid-outage conditions. The variability of the resources is comprehended by the battery energy storage unit, which allows easy power flow between the microgrid and loads. The mode of operation is executed by the power electronic based static transfer switches, which obtain their control signals from the developed control algorithm comprising of two major control algorithms fulfilling two tasks. The first task is to detect the grid outage and that is continuously monitored using frequency locked loop unified adaptive vectorial filter (FLL-AVF), which effectively estimates the grid and load angle under all the adverse conditions. The second task is to extract the load fundamental current, which is successfully estimated the normalized adaptive all pass (NAAP) filter. It allows the sensitive load operation. The control and microgrid operation are verified by the Matlab/Simulation result. For real world validation of the concept and control, the experimental results obtained from the laboratory developed prototype (mimicking the microgrid) are highly convincing. The grid current quality is improved and the microgrid successfully operates under all the conditions.

Model-based hardware in the loop control of collaborative robots: Simulink and Python based interfaces

ABSTRACT Collaborative robots are growing exponentially, with applications in the most diverse domains and operating side by side with humans in unstructured and changing environments. Such a scenario requires robot programmers to develop complex applications, integrating Artificial Intelligence (AI), sensing and control, both rapidly and efficiently. Python and model-based design software packages such as Simulink are widely used in engineering, and when it comes to robotics those tools are very important for robot design, simulation and the development of control algorithms before the implementation on the real robot. Python is widely used in AI while Simulink supports several hardware components, facilitating rapid deployment of the developed programs on the target hardware. In this study, a novel Python library (iiwaPy) and SimulinkIIWA are presented to interface KUKA iiwa robots. The iiwaPy is based on the Transmission Control Protocol/Internet Protocol (TCP/IP) and the SimulinkIIWA on the User Datagram Protocol (UDP). Both support robot motion control (point-to-point and real-time) at the joint level and the end-effector (EEF) level, as well as the monitoring of the robot state (position, orientation, torques feedback). Open-source code and example applications are provided showing the flexibility and the ease of use of the proposed interfaces.

Development and Validation of Control Algorithm for Variable Speed Fixed Pitch Small Wind Turbine

In this study, a power control algorithm of a variable-speed fixed-pitch horizontal-axis lift-type 20 kW small wind turbine (SWT) was proposed and verified through dynamic simulations. The power control algorithm proposed in this study consists of algorithms for Region II to track the maximum power coefficient, for Region II-1/2 to maintain the rated rotor speed, and for Region III to maintain the rated power. To verify the proposed power control algorithm, simulations were performed at the rated wind speed and above the rated wind speed, to which turbulence intensity based on the IEC regulation’s normal turbulence model was applied. As a result, it was confirmed that the proposed controller operates properly in the whole three regions including Regions II, II-1/2, and III. The controller performance was then compared with the variable-speed variable-pitch power controller. Although the performance of the proposed controller was considered good for the target VSVP wind turbine, it was lower than that of the conventional controller applied to the same wind turbine. Compared to the VSVP wind turbine, the VSFP wind turbine with the proposed controller was found to have higher mean loads on the blade and the tower but the fatigue loads in terms of Damage Equivalent Load (DEL) were found to be reduced.

Distributed finite-time neural network observer-based consensus tracking control of heterogeneous underwater vehicles

This note scrutinizes the distributed finite-time tracking problem of heterogeneous multi-agent systems with uncertain dynamics and environmental disturbances. To estimate the exogenous disturbances, an adaptive neural network-based disturbance observer is developed in which the approximation error of the neural network is compensated using adaptation laws. Based on finite-time stability theory, a distributed finite-time observer is proposed for each follower in order to estimate the leader's position. Moreover, an adaptive dynamic sliding mode-based controller is also developed for each follower, which ensures finite-time convergence of the tracking error between the leader and the follower. Incorporating adaptive control into the developed composite control structure leads to reducing the chattering, as well as enhancing the robustness and accuracy. Stability analysis and finite-time convergence of the tracking error are performed using the Lyapunov theory and multiple time scale principle, respectively. Finally, simulations and comparisons are performed in order to verify the effectiveness of the developed control algorithm for a group of autonomous underwater vehicles (AUVs).

Accurate prediction of magnetorheological damper characteristics based on a new rheological constitutive model

Physical modeling of magnetorheological dampers (MRDs) plays a crucial role in understanding the dynamic characteristics, optimizing design, and developing control algorithms of MRDs. The systematic study of various factors indicates that an accurate magnetorheological fluid (MRF) rheological model can effectively improve the estimation accuracy of the MRD physical model. The difficulty in establishing an accurate MRF model lies in the rheological behavior of MRFs at low and medium shear rates (0.01 ∼ 103 s−1) measured by commonly used rheometers exhibits notable differences from that at extremely high shear rates (105 ∼ 107 s−1) in MRDs. Therefore, this paper proposes to estimate the rheological behavior of MRFs at extremely high shear rates using the exponential linear mixed analytical (ELMA) model with excellent description and prediction capabilities, combined with the infinite viscosity and nonlinear term decay rate constraints determined based on rheology and related limited rheological data. This paper constructs a series of predictive MRD physical models combined with other relevant information, from the simple analytical model to the complex numerical model. The simulation results of the monotube MRD physical model, constructed based on the ELMA model identified by the above method and considering all deformation and inertial effects, are consistent with the corresponding dynamic test results of the MRD, reflecting the effectiveness and prospect of the parameter identification method and physical model.

Optimization of the Course Movement of an Unmanned Vehicle in the Presence of Obstacles and Disturbances

The article discusses the optimization of the control process of an unmanned vehicle. Currently, there is an active development and use of unmanned vehicles. There is a practice of using unmanned shuttles in closed areas (conferences, forums, etc.). The use of cars with automated control in urban conditions and on rough terrain is being tested. In this regard, it is important to develop control algorithms that allow solving problems of car control in real time under the influence of disturbances and the presence of obstacles. With the development of technology and an increase in computing power, it becomes possible to use optimal control algorithms that allow you to achieve better results when the terminal conditions are met, minimizing energy costs. This paper shows the solution of the problem of optimal control of an unmanned vehicle in the presence of a penalty function, measurement noise and disturbances from incomplete data using the separation principle. The problem of optimal control in a deterministic and stochastic setting is solved using an algorithm with a predictive model with a generalized work functional. The effectiveness of applying the Kalman filter is shown depending on the different intensity of measurement noise and different vehicle speeds. The results of numerical modeling are presented, showing the possibility of using the proposed algorithm to control an unmanned vehicle under various initial and final conditions. The developed algorithm has been successfully applied to bypass a moving object.