Real-time Control Research Articles

Autonomous High-Speed Overtaking of Intelligent Chassis Using Fast Iterative Model Predictive Control

High-speed overtaking is one of the structured driving scenarios of autonomous vehicles (AVs), and safety and real-time control requirements must be fulfilled during this scenario. In this work, we develop an overtaking path-planning and tracking framework for AVs based on the improved artificial potential field (APF) and fast iterative model predictive control (MPC). The improved APF is used to plan a feasible, high-speed overtaking path by considering the constraints of vehicle dynamics and introducing the adjustment factor into the potential function to avoid local optimization and target unreachability problems. In addition, to reduce the computational burden and fulfill the real-time control requirements of trajectory tracking, a fast iterative MPC algorithm is proposed herein. This algorithm adds disturbances that possess the nonlinear characteristics of the real model by using a linearized model. In terms of vehicle configuration, we consider that the target vehicle has the four-wheel steer (4WS)-four-wheel drive (4WD) chassis configuration and the fast iterative MPC is also used to coordinate the conflict between 4WS and direct yaw moment control (DYC) to improve tracking accuracy and vehicle stability during overtaking. Simulink/Carsim co-simulation and hardware-in-the-loop (HIL) tests are conducted to verify the effectiveness of the proposed controller.

Monitoring Welding Torch Position and Posture Using Reversed Electrode Images – Part I: Establishment of the REI-TPA Model

Sensing, modeling, and control are the key technologies of intelligent welding manufacturing. The position and attitude of the welding torch relative to the weld seam affects the quality of the produced weld directly, which is the basis of control and offline programming of the welding robot. Based on the existing research of reversed electrode image (REI) on monitoring of weld pool surface and penetration state, this work focused on the application of REI on monitoring of welding torch position and attitude (TPA). In this paper, a REI-TPA model was established to quantitively relate REI to TPA by considering the surface of the weld pool as a spherical mirror. With the REI-TPA model, the offset distance and deflection angle of the welding torch relative to the correct position and attitude can be calculated. The calculation is based on the measurement of the relative distance between the tip of REI and the electrode in a passive visual image of the weld pool, arc length, and weld pool geometry. This model can be applied to the real-time control of TPA, which is a supplement to the application of welding passive visual image and an extension to multi-information acquisition and processing methods in the robotic welding process.

Reinforcement Learning Control With Knowledge Shaping.

We aim at creating a transfer reinforcement learning framework that allows the design of learning controllers to leverage prior knowledge extracted from previously learned tasks and previous data to improve the learning performance of new tasks. Toward this goal, we formalize knowledge transfer by expressing knowledge in the value function in our problem construct, which is referred to as reinforcement learning with knowledge shaping (RL-KS). Unlike most transfer learning studies that are empirical in nature, our results include not only simulation verifications but also an analysis of algorithm convergence and solution optimality. Also different from the well-established potential-based reward shaping methods which are built on proofs of policy invariance, our RL-KS approach allows us to advance toward a new theoretical result on positive knowledge transfer. Furthermore, our contributions include two principled ways that cover a range of realization schemes to represent prior knowledge in RL-KS. We provide extensive and systematic evaluations of the proposed RL-KS method. The evaluation environments not only include classical RL benchmark problems but also include a challenging task of real-time control of a robotic lower limb with a human user in the loop.

Error-Triggered Adaptive Sparse Identification for Predictive Control and Its Application to Multiple Operating Conditions Processes.

With the digital transformation of process manufacturing, identifying the system model from process data and then applying to predictive control has become the most dominant approach in process control. However, the controlled plant often operates under changing operating conditions. What is more, there are often unknown operating conditions such as first appearance operating conditions, which make traditional predictive control methods based on identified model difficult to adapt to changing operating conditions. Moreover, the control accuracy is low during operating condition switching. To solve these problems, this article proposes an error-triggered adaptive sparse identification for predictive control (ETASI4PC) method. Specifically, an initial model is established based on sparse identification. Then, a prediction error-triggered mechanism is proposed to monitor operating condition changes in real time. Next, the previously identified model is updated with the fewest modifications by identifying parameter change, structural change, and combination of changes in the dynamical equations, thus achieving precise control to multiple operating conditions. Considering the problem of low control accuracy during the operating condition switching, a novel elastic feedback correction strategy is proposed to significantly improve the control accuracy in the transition period and ensure accurate control under full operating conditions. To verify the superiority of the proposed method, a numerical simulation case and a continuous stirred tank reactor (CSTR) case are designed. Compared with some state-of-the-art methods, the proposed method can rapidly adapt to frequent changes in operating conditions, and it can achieve real-time control effects even for unknown operating conditions such as first appearance operating conditions.

Implementing a digital twin for optimized real-time control of Gothenburg's regional sewage system

Abstract The emerging challenges facing the City of Gothenburg and surrounding municipalities include the impacts of a variable climate, rapid growth and urbanization, aging infrastructure and changing community values and customer expectations. A digital twin (DT), based on the Future City Flow (FCF) platform, was created for the catchment to address many of these challenges in terms of improved real-time sewerage performance and predictability in the face of storms, improved DT visualization of the regional sewage transport system for operators and greater responsiveness to avoid service interruption. The FCF-based DT provides a sound base for modelling, simulating, forecasting and controlling the catchment. The versatility of the FCF DT makes it useful for gaining insight into catchment dynamics and the simulator can be used to study the effects of different scenarios or changes in the catchment. The predictive control imbedded in the FCF DT recommends setpoints for controlling the catchment that can be used by the operators for decision support or even be directly applied in the control system as model-based predictive control. A DT requires good quality online data from the catchment and, perhaps most importantly, high-quality rain forecasts, especially when prognosing long time horizons.

What are the key stability challenges in high-bandwidth, non-minimum phase systems with time-varying, and non-smooth delays?

The analysis and control of stability in high-bandwidth systems characterized by non-minimum phase delays represent a formidable challenge within the realm of control theory and engineering. This research aims to address the pivotal question of whether it is feasible to enhance the stability of such intricate systems. These systems inherently possess uncertain and swiftly changing delay characteristics, rendering them exceptionally demanding to control effectively. In the course of this investigation, we embark on a comprehensive exploration of the theoretical underpinnings of the stability of high-bandwidth, non-minimum phase delay systems. This encompassing inquiry encompasses a meticulous consideration of both derivative-delay and piecewise continuous delay components. To underpin our analysis, we judiciously incorporate feedback mechanisms, drawing upon mathematical tools such as the Jensen inequality and Lyapunov-based methodologies to rigorously establish stability conditions. Furthermore, our exploration extends to encompass the concept of input-output stability and complements it with the notion of asymptotic stability, thereby ensuring that the systems in question exhibit uniform stability across diverse temporal domains. The outcomes of our investigation furnish compelling evidence that by harnessing the power of discrete-time Lyapunov-Krasovskii functionals, it becomes conceivable to circumscribe the maximum delay within predefined thresholds. This achievement holds the promise of enhancing stability in non-minimum phase delay systems characterized by high bandwidth. These findings have far-reaching implications, profoundly influencing the design and control paradigms across a spectrum of engineering applications. Notably, this impact extends to areas such as communication networks, real-time control systems, and robotics, where the mitigation of instability due to non-minimum phase delays has been an enduring challenge.

GAZE DIRECTION MONITORING MODEL IN COMPUTER SYSTEM FOR ACADEMIC PERFORMANCE ASSESSMENT

This paper focuses on the research and application of eye movement and gaze direction analysis in online testing systems. The practical novelty of the proposed model of monitoring the direction of gaze in a computer-based knowledge control system lies in the possibility of automated remote control over a large audience of students. The practical significance lies in creating the same conditions for computer testing for all students and increasing the correspondence of knowledge level to the received test results. The implemented and tested system is relevant and necessary in higher educational institutions, particularly in Ukraine, where remote education has emerged as the safest means of acquiring knowledge. This is especially true for fields of study where practical tasks and laboratory work do not necessitate a student's physical presence at the institution. That is why the application of the latest information technologies is extremely important. The dynamic authentication based on the sequence of eye movements proposed in the model allows error-free user's eye area detection for further analysis of the test subject's behavior. The proposed authentication method excludes the need to enter passwords or CAPTCHAs, ensures the speed of determining the user's presence. Further analysis of the user's direction of vision includes responding to the information received, such as skipping a question or the need for re-authorization. Question skipping occurs when the system decides that the user has not been looking at the screen for an extended period (looking sideways, down, up for more than 30 seconds). Re-authentication becomes necessary if the user exits the test or if there is a user replacement. Real-time gaze control capability is implemented by using massive parallel processing system (NVIDIA GeForce GTX 1650 graphics card) for calculations. The analysis of the results obtained shows that the proposed approach allows gaze detection at different illumination ranges (from 0.3 lux to 10,000 lux), as well as to detect states of the user's eyes that violating test rules for further system response (skipping a question or re-authentication request).

New bell plate controlled multi-inertia channel magnetorheological fluid mount wide frequency vibration isolation control study

This paper introduces a novel type of magnetorheological fluid (MRF) mount designed to achieve wide-frequency vibration isolation for engines. While previous studies have explored vibration isolation using hydraulic mounts, there has been limited research on achieving wideband isolation with MRF mounts. Consequently, this paper presents an innovative MRF mount structure and investigates wideband vibration isolation control for engines. Initially, experiments with a multi-channel MRF damper validate the switchable operation of controllable channels. A novel controllable multi-inertia channel MRF mount system is proposed, aligning the working mode of controllable inertia channels with that of the MRF damper. The lumped parameter method is employed to derive the mathematical model of the MRF mount, and its high and low-frequency dynamic characteristics are thoroughly examined. Subsequently, a real-time multi-island genetic algorithm-optimized controller is developed to investigate wideband vibration isolation within the MRF mount system. The findings indicate that: (1) The MRF mount demonstrates adjustable low-frequency dynamic stiffness and loss angle within the frequency range of f = 0–50 Hz. In the frequency range of f = 50–100 Hz, the mount achieves its lowest dynamic stiffness value, effectively addressing the issue of high-frequency stiffening. (2) The real-time multi-island genetic optimized controller exhibits superior vibration isolation performance compared to traditional sky-hook control and hybrid sky-hook control methods, achieving optimal vibration isolation for the mount.

Real-Time Line Drop Compensation Control Method of Step Voltage Regulator on Distribution Lines with Distributed Generation

Real-Time Line Drop Compensation Control Method of Step Voltage Regulator on Distribution Lines with Distributed Generation

Attitude modelling and real-time robust control of a 3-DoF quadcopter UAV test bench

Abstract In this work, a three degrees-of-freedom (3-DoF) static quadcopter unmanned aerial vehicle (UAV) test-rig of a pendulum-type configuration is custom-designed, developed, instrumented, and interfaced with a PC. The rig serves as a test bed to develop high-fidelity mathematical models as well as to investigate autopilot designs and real-time closed-loop controllers’ performances. The Simulink Desktop Real-Time software is employed for the quadcopter’s attitude signals acquisition and real-time implementation of closed-loop controllers on a target microcontroller hardware. The mathematical models for pitch, roll, and yaw axes are derived via the first principle and validated with the experimental linear system identification (SI) techniques. Subsequently, employing the multi-parameter root contour technique, the classical proportional integral derivative (PID) controllers are designed and implemented in real-time on the quadcopter UAV test rig. This served as a benchmark controller for comparing it with an integral-based linear quadratic regulator (LQR) controller. Further, to improve the transient response of the LQR controller, a novel robust integral-based LQR controller with a feedforward term (LQR-FF) is implemented, which shows much superior performance than the benchmark and basic LQR controller. This work thus will act as a precursor for a more complex 3-DoF autopilot design of an untethered quadcopter.

Hybrid magnetic field system with helmholtz coils and magnets for real-time circulating tumor cell separation

Extensive research is currently underway to devise effective strategies for the isolation of circulating tumor cells (CTCs) from blood samples. The utilization of magnetic particles and magnet as a means for this separation process is garnering increased attention within this field. However, when utilizing fixed magnets to capture cancer cells with attached magnetic particles, the inability to control the magnetic field may result in cellular damage due to the strong magnetic field compressing the magnetic particles. In this study, we developed a hybrid system that leverages electromagnetic coils to modulate the strength of the magnetic field from the magnets within a microfluidic channel, enabling real-time control of cancer cells with attached magnetic particles.Through the strategic application of electromagnetic coils, we achieved precise control over the magnet’s position, thereby enabling real-time observation of the cancer cell within the microfluidic channel. Additionally, this approach facilitated the release of captured cancer cells from the magnet. The isolated cells could then be cultured to assess individualized drug responses, and evaluate their reactivity to the treatment. Through the implementation of this novel technique, we anticipate that the real-time capture and analysis of CTCs from blood samples will enable more efficient and effective diagnostics and treatment monitoring, ultimately improving patient outcomes.

The 85-electrode adaptive optics system of the Swedish 1-m Solar Telescope

We discuss the chosen concepts, detailed design, implementation and calibration of the 85-electrode adaptive optics (AO) system of the Swedish 1-meter Solar Telescope (SST), which was installed in 2013. The AO system is unusual in that it uses a combination of a monomorph mirror with a Shack-Hartmann (SH) wavefront sensor (WFS) and a second high-resolution SH microlens array to aid the characterization, calibration, and modal control of the deformable mirror. An Intel PC workstation performs the heavy image processing associated with cross-correlations and real-time control at a 2 kHz update rate with very low latency. The computer and software continue the successful implementation since 1995 of earlier generations of correlation tracker and AO systems at SST and its predecessor, the 50-cm Swedish Vacuum Solar Telescope, by relying entirely on work-station technology and an extremely efficient algorithm for implementing cross-correlations with the large field of view of the WFS. We describe critical aspects of the design, calibrations, software, and functioning of the AO system. The exceptionally high performance is testified through the highest Strehl ratio (inferred from the measured granulation contrast) of existing meter-class solar telescopes, as demonstrated here at wavelengths shorter than 400 nm and discussed in more detail in a previous separate publication We expect that some aspects of this AO system may also be of interest outside the solar community.

Traffic light control with reinforcement learning

Urban traffic signal optimization is important for alleviating congestion in urban transportation systems. This study proposes a real-time traffic light control algorithm based on deep Q learning with a reward function that accounts for queue lengths, delays, travel times, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed signal timing and an online stage using real-time traffic data. A deep Q network structure with aphase gate component is used to simplify the model's learning task under different phases. Amemory palace" mechanism is used to address sample imbalance during the training process. Both synthetic and real-world traffic flow data are used to validate our approach under an urban road intersection scenario in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal timing plans.

An eco-driving strategy for autonomous electric vehicles crossing continuous speed-limit signalized intersections

The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco-driving strategy for autonomous electric vehicles to navigate continuous signalized intersections. This strategy incorporates the realistic powertrain's working efficiency and constraints into the vehicle motion optimization. In the domain of the global path, we formulate a co-optimization problem of vehicle motion and powertrain operation to tap the vehicle energy-saving potential and derive an optimal velocity profile, considering constraints related to queue release and limit speed in intersections. In the short rolling time domain, we propose a predictive control approach for vehicle speed tracking and real-time powertrain control. Simulation results under various scenarios show the advantages of the proposed method in terms of energy consumption compared to the baseline and existing methods.

Low-Thrust Nonlinear Orbit Control for Very Low Lunar Orbits

In the next decades, both space agencies and private competitors are targeting the lunar environment as a scientific and technological resource for future space missions. In particular, the confirmed existence of water-ice deposits in the vicinity of the poles (predominantly the south pole) makes polar or near-polar low lunar orbits attractive for the purpose of designing space missions that could search for suitable Lunar base sites. However, traveling very-low-altitude orbits is very challenging, as they are strongly perturbed by the Moon’s gravity field as well as third- and fourth-body effects due to the Earth and the Sun. Several studies demonstrate that these orbits are expected to impact the lunar surface in a few months. Therefore, the definition and implementation of an effective station-keeping strategy represents a crucial issue in order to extend satellites’ lifetime. In this paper, a feedback nonlinear control law is employed in order to perform corrective maneuvers aimed at keeping the state of the satellite within acceptable margins. The satellite is assumed to be equipped with a steerable and throttleable low-thrust propulsion system. The control law is based on the Lyapunov stability theory and does not require any reference path to track, with a considerable decrease in the computational cost. The proposed real-time control law includes control saturation, related to the maximum available thrust magnitude, and is developed employing modified equinoctial elements, in order to avoid singularities and extend its range of application. Finally, the strategy at hand is tested in the presence of all the relevant perturbations (i.e., harmonics of the selenopotential, third- and fourth-body effects) in order to show its effectiveness and efficiency.

Quality control in the establishment and management of gastric cancer database

The establishment of a high-quality gastric cancer database significantly improves the efficiency and standardization of diagnosis and treatment of this disease. Our center has developed a specialized, single-center gastric cancer database and initiated the China Gastrointestinal Cancer Surgery Union, catalyzing the exploration of multi-center databases. This article encapsulates multi-level experience and provides a detailed overview of the quality control methods we implement in both constructing and managing the gastric cancer database. Utilizing an electronic medical record system and a multi-disciplinary treatment (MDT) approach, we have designed the database in a modular and multi-nodal manner. A synthesis of automatic retrieval of structured data and manual entry, coupled with a rigorous MDT system and real-time supervision at various nodes, bolster our real-time quality control efforts. Ensuring data security and digitized management plans alongside real-time review protocol and a multi-level review system, we maintain the highest standards in the initiation and management of the database. Through the establishment of the China Gastrointestinal Cancer Surgery Union platform, we endorse the concept that multi-center database construction should be driven by research objectives, consider data accessibility, while placing an emphasis on building inter-center consensus on data quality control. Moving forward, it is crucial that the development of multi-center databases promotes uniformity in medical standards across centers, cultivates stable public data sharing platforms, ensures robust data security protocols, routinely conducts data quality assessments, and bolsters multi-center cooperation and exchanges to promote the homogeneity of medical standards.

Fuzzy-based predictive deep reinforcement learning for robust and constrained optimal control of industrial solar thermal plants

Integrating distributed solar fields (DSFs) into conventional heat and power plants (CHPs) of industries is mostly constrained by the availability of a real-time capable control scheme. Safe and efficient operation of industrial DSF requires the supply of a fluctuating and periodically available energy at the required temperature while reducing losses and ensuring operational constraint. Reinforcement learning (RL), specifically Q-learning methods, are being recently applied for such control tasks. However, existing RL approaches cannot be directly applied to continuous domain control tasks or tasks with operation constraints. While using deep learning in model-free RL can remove these limitations, tractability and scalability boundaries on performing the deep learning are becoming significant, with scarce data and multi-objective concerns. In contrast, model-based deep RL schemes can be applied for sample efficiency and satisfying operational constraints, but their modeling framework is not general and accurate. To address these challenges, the work here develops a hybrid fuzzy convolution model (HFCM) that takes full advantages of data, models (dynamic and steady-state), and prior knowledge on industrial DSF. The HFCM is then extended for use in deep deterministic policy gradient (DDPG) algorithm to learn the DSF control task. This is done so by solving a multi-objective optimization problem, which is formulated as a constrained Markov decision process (CMDP) with continuous state and actions. Some practical and relevant findings were made with this HFCM. Firstly, it allowed the DDPG agent to learn independently a temperature state and a disturbance state, using only temperature measurement and single realizations of state vector. It also permitted the testing of an operational state violation strategy on a DDPG actions, and at the same time, the correction of the effects at a safety layer, if needed. Furthermore, the proposed control strategy simultaneously reduced mean temperature tracking error by 24%–51% and energy gain by 13.9%–17.35% when compared respectively to model-free DDPG and MPC baselines.

Analysis of the results of study allergens in food in 2021

One of the biggest problems in the world is allergy, the clinical manifestations of which can be of varying degrees of severity – from dermatitis to Quincke's edema and anaphylactic shock. More than 250 million people suffer from food allergies. It has been scientifically proven that more than 70 food products can cause allergies. The main allergens are products of plant and animal origin, such as nuts, cereals, fish and seafood, eggs, etc. Celiac disease is one of the manifestations of allergy or individual intolerance to cereal plants. This genetic, autoimmune disease manifests itself due to the consumption of gluten-containing products occurs with a frequency of 1:3000. According to the current Ukrainian legislation, product manufacturers are obliged to indicate on the packaging of products information about the presence of food allergens, but very often they do not do this, misleading buyers and putting people who have such a problem at risk. Considering the above, there is a need to conduct state control of food products for the presence of allergens to control manufacturers regarding the labeling of allergens. The work aims to analyze the results of real-time state control of allergens in food products by PCR. The analysis of the studies of allergens in food products showed that 25.4 % contained allergens not indicated on the product packaging. This means insufficient control by manufacturers and severely violates current legislation. According to the results of the State Allergen Control Program, undeclared allergens were found in 25.4 % of food products from the total number of tested samples: in 9.3 % of sweets, 6.9 % of bakery products, 2.3 % of beverages, 6.9 % of precise products. Considering the urgency of the problem, there is a need to develop and implement a state program for monitoring allergens in food products in order to control the requirements of the current legislation.

Efficient injection of gas tracers into rivers: A tool to study Surface water–Groundwater interactions

Surface water (SW) - groundwater (GW) interactions exhibit complex spatial and temporal patterns often studied using tracers. However, most natural and artificial tracers have limitations in studying SW–GW interactions, particularly if no significant contrasts in concentrations between SW and GW exist or can be maintained for long durations. In such context, (noble) gases have emerged as promising alternatives to add to the available tracer methods, especially with the recent development of portable mass spectrometers, which enable continuous monitoring of dissolved gas concentrations directly in the field. However, long-duration gas injection into river water presents logistical challenges. To overcome this limitation, we present an efficient and robust diffusion-injection apparatus for labeling large amounts of river water. Our setup allows fine, real-time control of the gas injection rate, and is suitable for extended injection durations and different gas species. To illustrate the effectiveness of our approach, we present a case study where helium (He) is used as an artificial tracer to study river water infiltration into an alluvial aquifer. Our injection of He as a tracer increased the dissolved He concentration of the river water by one order of magnitude compared to air-saturated water concentration for 35 days. This experiment yields valuable information on travel times from the river to a pumping well and on the mixing ratios between freshly infiltrated river water and regional groundwater.

Real-time two-axis control of a spin qubit

Optimal control of qubits requires the ability to adapt continuously to their ever-changing environment. We demonstrate a real-time control protocol for a two-electron singlet-triplet qubit with two fluctuating Hamiltonian parameters. Our approach leverages single-shot readout classification and dynamic waveform generation, allowing full Hamiltonian estimation to dynamically stabilize and optimize the qubit performance. Powered by a field-programmable gate array (FPGA), the quantum control electronics estimates the Overhauser field gradient between the two electrons in real time, enabling controlled Overhauser-driven spin rotations and thus bypassing the need for micromagnets or nuclear polarization protocols. It also estimates the exchange interaction between the two electrons and adjusts their detuning, resulting in extended coherence of Hadamard rotations when correcting for fluctuations of both qubit axes. Our study highlights the role of feedback in enhancing the performance and stability of quantum devices affected by quasistatic noise.