This paper introduces an effective framework for designing memoryless dissipative full-state feedbacks for general linear delay systems via the Krasovskiĭ functional (KF) approach, where an unlimited number of pointwise and general distributed delays (DDs) exists in the state, input and output. To handle the infinite dimensionality of DDs, we employ the Kronecker-Seuret Decomposition (KSD) which we recently proposed for analyzing matrix-valued functions in the context of delay systems. The KSD enables factorization or least-squares approximation of any number of $\mathcal{L}^2$ DD kernels from any number of DDs without introducing conservatism. This also facilitates the construction of a complete-type KF with flexible integral kernels, following from an application of a novel integral inequality derived from the least-squares principle. Our solution includes two theorems and an iterative algorithm to compute controller gains without relying on nonlinear solvers. A challenging numerical example, intractable for existing methods, underscores the efficacy of this approach.
The Adaptative neuro-fuzzy inference system (ANFIS) has shown great potential in processing practical data from control, prediction, and inference applications, reflecting advantages in both high performance and system interpretability as a result of the hybridization of neural networks and fuzzy systems. Matlab has been a prevalent platform that allows to utilize and deploy ANFIS conveniently. On the other hand, due to the recent popularity of machine learning and deep learning, which are predominantly Python-based, implementations of ANFIS in Python have attracted recent attention. Although there are a few Python-based ANFIS implementations, none of them are directly compatible with scikit-learn, one of the most frequently used libraries in machine learning. As such, this paper proposes Scikit-ANFIS, a novel scikit-learn compatible Python implementation for ANFIS by adopting a uniform format such as fit() and predict() functions to provide the same interface as scikit-learn. Our Scikit-ANFIS is designed in a user-friendly way to not only manually generate a general fuzzy system and train it with the ANFIS method but also to automatically create an ANFIS fuzzy system. We also provide four kinds of representative cases to show that Scikit-ANFIS represents a valuable addition to the scikit-learn compatible Python software that supports ANFIS fuzzy reasoning. Experimental results on four datasets show that our Scikit-ANFIS outperforms recent Python-based implementations while achieving parallel performance to ANFIS in Matlab, a standard implementation officially realized by Matlab, which indicates the performance advantages and application convenience of our software.
The paper highlights efforts by major global engineering companies to develop agricultural robotic systems, with a particular focus on universal unmanned mobile energy vehicles designed to perform technological operations autonomously. Analysis of current machinery for harvesting and preparing flax straw indicates a lack of sufficient automation and robotization in these processes. Among the operations involved in flax harvesting, turning is defined as the least energy-intensive. In this regard, research has begun on the development of a remotely controlled, self-propelled flax windrow turner, with potential for further adaptation to autonomous operation. (Research purpose) The study aims to substantiate the optimal operating modes and develop a power electrical circuit for a remotely controlled, self-propelled flax windrow turner equipped with an electric drive. (Materials and methods) The study provides a theoretical basis for the conveyor's linear speed in relation to the rectilinear motion of the flax turner. (Results and discussions) The study identified the following operating modes for the flax windrow turner: the conveyor's angular velocity was determined to be 4.63 radians per second, with a machine speed of 2.78 meters per second. Considering the machine's weight, 7.00-12 F-42-1 drive wheels were selected, with tires that offer excellent road traction and maneuverability. The drive wheels had a load index of 133, an outer diameter of 660 millimeters, and a profile width of no more than 195 millimeters without load. Additionally, a power electrical circuit was designed for a remotely controlled self-propelled flax windrow turner. (Conclusions) The study determined the operating modes for an electrified radio-controlled, self-propelled flax windrow turner and proposed a powered electrical circuit for designing the units and assemblies of machinery used in flax straw harvesting.
In fully automatic operation systems widely implemented in the urban rail transit sector, train doors play an important role of passenger service. Their control logic and mode directly affect both the personal safety of passengers and the operational efficiency of these transport systems. This paper introduces train door control schemes for fully automatic operation systems in normal, fault and emergency scenarios. An in-depth analysis focuses on the logic and timing of door unlocking in emergencies. Two timing options are discussed for establishing evacuation protection zones for the operation of the emergency door unlocking at zero speed, the first involves unlocking the doors after the operation control center receives a request for emergency door unlocking and confirming the authorization; the second involves unlocking the doors upon receiving a request for emergency door unlocking from passengers. The comparison results showed that both options were effective in unlock the doors within a specified time-frame. Although the first option took more time for emergency door unlocking (up to 18.5 s), it played an effective role in avoiding impacts to the operational order by passenger misoperations during high-density train operation.
Control engineering systems. Automatic machinery (General), Technology
Germán Obando, Juan Martinez-Piazuelo, Nicanor Quijano
et al.
En la última década, se han venido desarrollando técnicas inspiradas por la naturaleza y la economía con el fin de resolver problemas de control y toma de decisiones. En este artículo, se presenta este nuevo paradigma que combina los juegos poblacionales y los modelos dinámicos de pago. Se introducen conceptos fundamentales en torno a estas áreas, incluyendo un desarrollo matemático formal (basado en teoría de pasividad para sistemas dinámicos, estabilidad de Lyapunov e invarianza de conjuntos) que valida su uso tanto para abordar problemas de optimización como para diseñar sistemas de control en lazo cerrado con restricciones (físicas y operacionales). Específicamente, nos enfocamos en problemas cuyos objetivos se alinean con la distribución dinámica de recursos y el alcance de equilibrios generalizados de Nash. La pertinencia del paradigma formulado se ilustra a través de diferentes problemas de ingeniería con aplicaciones en múltiples campos.
Control engineering systems. Automatic machinery (General)
We present a chance-constrained model predictive control (MPC) framework under Gaussian mixture model (GMM) uncertainty. Specifically, we consider the uncertainty that arises from predicting future behaviors of moving obstacles, which may exhibit multiple modes (for example, turning left or right). To address the multi-modal uncertainty distribution, we propose three MPC formulations: nominal chance-constrained planning, robust chance-constrained planning, and contingency planning. We prove that closed-loop trajectories generated by the three planners are safe. The approaches differ in conservativeness and performance guarantee. In particular, the robust chance-constrained planner is recursively feasible under certain assumptions on the propagation of prediction uncertainty. On the other hand, the contingency planner generates a less conservative closed-loop trajectory than the nominal planner. We validate our planners using state-of-the-art trajectory prediction algorithms in autonomous driving simulators.
Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger
et al.
We consider the problem of optimizing the economic performance of nonlinear constrained systems subject to uncertain time-varying parameters and bounded disturbances. In particular, we propose an adaptive economic model predictive control (MPC) framework that: (i) directly minimizes transient economic costs, (ii) addresses parametric uncertainty through online model adaptation, (iii) determines optimal setpoints online, and (iv) ensures robustness by using a tube-based approach. The proposed design ensures recursive feasibility, robust constraint satisfaction, and a transient performance bound. In case the disturbances have a finite energy and the parameter variations have a finite path length, the asymptotic average performance is (approximately) not worse than the performance obtained when operating at the best reachable steady-state. We highlight performance benefits in a numerical example involving a chemical reactor with unknown time-invariant and time-varying parameters.
A hybrid integrator-gain system (HIGS) is a control element that switches between an integrator and a gain, which overcomes some inherent limitations of linear controllers. In this paper, we consider using discrete-time HIGS controllers for the digital control of negative imaginary (NI) systems. We show that the discrete-time HIGS themselves are step-advanced negative imaginary systems. For a minimal linear NI system, there always exists a HIGS controller that can asymptotically stablize it. An illustrative example is provided, where we use the proposed HIGS control method to stabilize a discrete-time mass-spring system.
In this paper, we revisit state estimation and weak detectability verification for discrete event systems (DES) from a span-new perspective. Specifically, using the semi-tensor product (STP) technique, we construct two new matrix-based information structures called a current-state estimator (C-estimator) and an initial-state estimator (I-estimator) for computing three fundamental types of state estimates, namely, current-state estimate (CSE), initial-state estimate (ISE), and delayed-state estimate (DSE). The complexity of building C-estimator and I-estimator is polynomial time with respect to the size of a plant. A notion of weak delayed detectability is introduced, which captures that, after observing a $k_{1}$ -length sequence/string, whether or not one can always accurately determine the state of a plant at this moment after at most $k_{2}$ steps of delays for some trajectories. Further, using the proposed C-estimator and I-estimator, we discuss the different types of detectability verification problems, including, but not restricted to, weak current-state detectability (C-detectability), weak initial-state detectability (I-detectability), and weak delayed detectability. Accordingly, several necessary and sufficient criteria are derived for verifying the aforementioned different types of detectability. Our approaches are numerically tractable and only involve some basic matrix manipulations. Finally, some examples are given to illustrate the obtained results. Note to Practitioners—State estimation is one of the most fundamental problems in many practical engineering systems. For instance, one needs to infer the state of a manufacturing system before a failure occurs. For a communication system, can we guarantee that whether important information remains secret to outsiders for security requirements? Finding an alternative and efficient approach to capture the state of a plant based on imperfect observations is still crucial for engineers. To solve these problems, in this paper we develop a novel methodology to tackle simultaneously three fundamental categories of state estimation for practical engineering systems that are inherently abstracted as partially-observed discrete-event systems. Our approaches are technically quite different from the existing ones. The novel results obtained in this paper are all of matrix-based characterization, which can be implemented algorithmically by means of the user-friendly STP software package. We believe that the alternative methodology provides an innovative insight for engineers in the field of automatic control.
Ralf H. Reussner, Ina Schaefer, Bernhard Beckert
et al.
Cyber-Physical Systems (CPS) integrate computational processes with physical processes. Different systems are summarised in this term, from cars, trains, and aircrafts to modern smart home systems. These systems must meet requirements of openness, connectivity, increased software-implemented functionality, flexible configurability, dependability, and resilience, all in a cost-effective way, and during all phases of their life-time. The limitations of current CPS design approaches become obvious when trying to fulfil these requirements simultaneously. The central concept to cope with the ever-increasing complexity of CPS, alongside functional decomposition, is the definition of views which enable the specialisation of developer roles. While dealing with component dependencies is well researched, the unsolved scientific challenge of view consistency is the central reason for the above-mentioned trade-offs between configurability, functionality, dependability, and cost-effectiveness. In the new Collaborative Research Centre (CRC) “Convide”, starting in July 2023, we develop a general, comprehensive understanding of view consistency and mechanisms to detect and, when possible, automatically or interactively resolve consistency violations between views in CPS design. Therefore, we will investigate how to extend, generalise and transfer work in the area of view consistency in software engineering to systems engineering. The project is formed around the methodological core of a so-called virtual single underlying model that has been investigated by the principal investigators. We see a window of opportunity as elaborated meta-models of non-software domains are now being standardised. This gives us the chance to research the extension of software engineering approaches to non-software views of CPS. Prof. Ralf Reussner is the speaker of the project, with members coming from the faculties of Informatics, Electrical Engineering as well as Mechanical Engineering. Furthermore, TU Munich, TU Dresden, and the University of Mannheim participate in the CRC. The centre has a budget of 11 million euros over four years.
In view of the problems of abandoning wind and solar energy, and carbon emissions, inevitable reduce of thermal power generation caused by the consumption of wind and solar energy, and the changes in the proportion of wind and solar energy consumption to thermal power at different times under the influence of time-of-use price, a time-of-use ladder carbon emission rights exchange mechanism is proposed, and on this basis, a low-carbon virtual power plant optimal dispatching model with ‘wind-solar-gas-storage’ is built. The maximum daily operating income of the virtual power plant is taken as the objective function in the model. The IBM commercial solver CPLEX is employed to optimize the solution, so as to obtain the optimal economic dispatching strategy of the virtual power plant, and compares it with the set scenarios. The simulation results reveal that the proposed mechanism can effectively enhance the enthusiasm and operating benefits of virtual power plants for renewable energy consumption, and reduce carbon emissions, providing new methods for low-carbon dispatching, and wind and solar energy consumption of virtual power plants.
Control engineering systems. Automatic machinery (General), Systems engineering
Tossaporn Udomsap, Sakda Chinchouryrang, Siwat Liampipat
et al.
Abstract In this paper, a 3-PRS (prismatic, revolute, and spherical) parallel manipulator for platform stabilization is designed. The main purpose of this device is to stabilize visual equipment, which is placed on top of a car to inspect electrical transmission cables, as part of routine maintenance. Due to the bulky and heavy infrared cameras used during inspections, a stabilizer platform has been designed to handle the weight of camera equipment up to 10 kg. This device consists of two major mechanisms. The first mechanism is able to adjust the angle of the camera. Thus, the user can focus the camera along the electric transmission lines. The second mechanism is stabilization. The mechanism serves to stabilize the orientation and position of the camera in the roll, pitch, and heave directions. To test the performance of the stabilization mechanism, the device is fed with the known value of the angle with regard to the input. As such, the device is trying to compensate for the change in angle. The results show that the errors between the input angles and compensated angles are in the range of 0.4–3%. Errors are seen to be within an acceptable range. It is significant that the resultant errors do not affect the orientation of the camera.
In the calculation of rail flaw echo location, the rail flaw location is related to the ultrasonic propagation time in rail. The more accurate the extracted propagation time, the more accurate the rail flaw location calculation. During high speed rail flaw detection, unevenness in the vertical direction of the rail, structural differences in the detection wheel lifting unit and differences in the amount of fluid filled in the detection wheel will cause vertical jump of the detection wheel, leading to changes in ultrasonic arriving time at the rail surface of different probes and influencing flaw localization. Therefore, the paper proposes a rail flaw echo accurate localization method based on interface wave tracking technology. Taking 45° and 70°probes as examples, flaw echo localization based on interface wave tracking technology and the effectiveness of interface wave tracking technology under different pressure conditions of detection wheels are analyzed. An artificial flaw test block is designed, and flaw detection experiment is carried out and verified. The results show that interface wave tracking technology can guarantee precise extraction of ultrasonic echo time of different probes in the detection wheel(the time accuracy is 0.025 μs), and it is beneficial for rail flaw localization. In addition, based on the analysis of actual detection data of rail lines, suggestions for the application of interface wave tracking technology in flaw detection are proposed.
Control engineering systems. Automatic machinery (General), Technology
Recent years have seen significant progress in the realm of robot autonomy, accompanied by the expanding reach of robotic technologies. However, the emergence of new deployment domains brings unprecedented challenges in ensuring safe operation of these systems, which remains as crucial as ever. While traditional model-based safe control methods struggle with generalizability and scalability, emerging data-driven approaches tend to lack well-understood guarantees, which can result in unpredictable catastrophic failures. Successful deployment of the next generation of autonomous robots will require integrating the strengths of both paradigms. This article provides a review of safety filter approaches, highlighting important connections between existing techniques and proposing a unified technical framework to understand, compare, and combine them. The new unified view exposes a shared modular structure across a range of seemingly disparate safety filter classes and naturally suggests directions for future progress towards more scalable synthesis, robust monitoring, and efficient intervention.
This paper proposes a novel approach for modeling and controlling nonlinear systems with varying parameters. The approach introduces the use of a parameter-varying Koopman operator (PVKO) in a lifted space, which provides an efficient way to understand system behavior and design control algorithms that account for underlying dynamics and changing parameters. The PVKO builds on a conventional Koopman model by incorporating local time-invariant linear systems through interpolation within the lifted space. This paper outlines a procedure for identifying the PVKO and designing a model predictive control using the identified PVKO model. Simulation results demonstrate that the proposed approach improves model accuracy and enables predictions based on future parameter information. The feasibility and stability of the proposed control approach are analyzed, and their effectiveness is demonstrated through simulation.
Privacy engineering, as an emerging field of research and practice, comprises the technical capabilities and management processes needed to implement, deploy, and operate privacy features and controls in working systems. For that, software practitioners and other stakeholders in software companies need to work cooperatively toward building privacy-preserving businesses and engineering solutions. Significant research has been done to understand the software practitioners’ perceptions of information privacy, but more emphasis should be given to the uptake of concrete privacy engineering components. This research delves into the software practitioners’ perspectives and mindset, organizational aspects, and current practices on privacy and its engineering processes. A total of 30 practitioners from nine countries and backgrounds were interviewed, sharing their experiences and voicing their opinions on a broad range of privacy topics. The thematic analysis methodology was adopted to code the interview data qualitatively and construct a rich and nuanced thematic framework. As a result, we identified three critical interconnected themes that compose our thematic framework for privacy engineering “in the wild”: (1) personal privacy mindset and stance, categorised into practitioners’ privacy knowledge, attitudes and behaviours; (2) organizational privacy aspects, such as decision-power and positive and negative examples of privacy climate; and, (3) privacy engineering practices, such as procedures and controls concretely used in the industry. Among the main findings, this study provides many insights about the state-of-the-practice of privacy engineering, pointing to a positive influence of privacy laws (e.g., EU General Data Protection Regulation) on practitioners’ behaviours and organizations’ cultures. Aspects such as organizational privacy culture and climate were also confirmed to have a powerful influence on the practitioners’ privacy behaviours. A conducive environment for privacy engineering needs to be created, aligning the privacy values of practitioners and their organizations, with particular attention to the leaders and top management's commitment to privacy. Organizations can also facilitate education and awareness training for software practitioners on existing privacy engineering theories, methods and tools that have already been proven effective.
The thrusters and propulsion propellers systems, as well as the operating situations, are all well-known nonlinearities which are caused less accuracy of the dynamic positioning system (DPS) of vessels in the path planning control process. In this study, to enhance the robust performance of the DPS, we proposed a robust adaptive fuzzy control model to reduce the effect of uncertainty problems and disturbances on the DPS. Firstly, the adaptive fuzzy controller with adaptive law is designed to adjust the membership function of the fuzzy controller to minimize the error in path planning control of the vessel. Secondly, the [Formula: see text] performance of robust tracking is proved by the Lyapunov theory. Moreover, compared to the other controller, a simulation experiment comprising two case studies confirmed the efficiency of the approach. Finally, the results showed that the proposed controller reaches control quality, performance and stability.
Control engineering systems. Automatic machinery (General), Automation
Including local automatic gain control (AGC) circuitry into a silicon cochlea design has been challenging because of transistor mismatch and model complexity. To address this, we present an alternative system-level algorithm that implements channel-specific AGC in a silicon spiking cochlea by measuring the output spike activity of individual channels. The bandpass filter gain of a channel is adapted dynamically to the input amplitude so that the average output spike rate stays within a defined range. Because this AGC mechanism only needs counting and adding operations, it can be implemented at low hardware cost in a future design. We evaluate the impact of the local AGC algorithm on a classification task where the input signal varies over 32 dB input range. Two classifier types receiving cochlea spike features were tested on a speech versus noise classification task. The logistic regression classifier achieves an average of 6% improvement and 40.8% relative improvement in accuracy when the AGC is enabled. The deep neural network classifier shows a similar improvement for the AGC case and achieves a higher mean accuracy of 96% compared to the best accuracy of 91% from the logistic regression classifier.
This paper addresses the inverse optimal control problem of finding the state weighting function that leads to a quadratic value function when the cost on the input is fixed to be quadratic. The paper focuses on a class of infinite horizon discrete-time and continuous-time optimal control problems whose dynamics are control-affine and whose cost is quadratic in the input. The optimal control policy for this problem is the projection of minus the gradient of the value function onto the space formed by all feasible control directions. This projection points along the control direction of steepest decrease of the value function. For discrete-time systems and a quadratic value function the optimal control law can be obtained as the solution of a regularized least squares program, which corresponds to a receding horizon control with a single step ahead. For the single input case and a quadratic value function the solution for small weights in the control energy is interpreted as a control policy that at each step brings the trajectories of the system as close as possible to the origin, as measured by an appropriate norm. Conditions under which the optimal control law is linear are also stated. Additionally, the paper offers a mapping of the optimal control formulation to an equivalent reinforcement learning formulation. Examples show the application of the theoretical results.