Stable Control Research Articles

Online learning of stable robust adaptive controllers design based on data-dependent feedback linearization with application to rotary inverted pendulum

This study introduces an online (supervised) learning method to design nonlinear auto-regressive moving average (NARMA) controllers for feedback-linearized nonlinear single-input single-output (SISO) systems. The algorithm ensures Schur stability of the overall closed-loop system and provides adaptiveness and robustness for the NARMA controllers. The first stage of the method derives, in a data-dependent way, a feedback-linearized model of the nonlinear plant by using its input and output sample pairs. The method’s second stage, which constitutes the novel part of the presented study, builds up an online learning scheme for the linear auto-regressive moving average (ARMA) controller based on an already learned feedback-linearized model of the nonlinear plant. During online supervised learning, ARMA parameters of the feedback-linearized SISO plant model and the closed-loop ARMA model are computed by minimizing the plant identification and the closed-loop system tracking errors. Both errors are defined as ℓ1,ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\ell}}_{1,{\\varvec{\\varepsilon}}}$$\\end{document}, namely ε-insensitive loss functions that provide NARMA controller the robustness against noise and outliers. The proposed online learning control algorithm is applied to a rotary inverted pendulum model and to a real rotary inverted pendulum setup. The tracking performance of the developed controller is compared with those of the linear quadratic regulator and coupled sliding mode controller in terms of mean square error.

BOATS: Bayesian Optimization for Active control of ThermoacousticS

This investigation presents novel adaptive control algorithms specifically designed to address and mitigate thermoacoustic instabilities. Gas turbines are limited in their operational range due to thermoacoustic instability. Two control strategies are available to alleviate this issue: active and passive. Active control strategies have a wider flexibility than passive control strategies because they can adapt to the operating conditions of the gas turbine. However, optimizing the control parameters remains a challenge, especially if additional constraints have to be fulfilled, such as e.g. pollutant emission levels. To address this issue, we propose three adaptive control strategies based on Bayesian optimization. The first and foundational algorithm is the safeOpt algorithm, and the two adaptations that have been made are stageOpt and shrinkAlgo. The algorithms facilitate safe exploration within the control parameter space, ensuring compliance with the constraint function, while simultaneously optimizing the objective function. The Gaussian Process Regressor (GPR) is employed to approximate both the objective and constraint functions, with continuous updates occurring during iterations. The algorithms also enable the transfer of knowledge obtained from one operating point to another, thereby reducing the number of iterations needed to reach the optimal point. We demonstrate the effectiveness of the algorithms both numerically and through two distinct experimental validations. In the numerical demonstration, we employ a low-order thermoacoustic network model to simulate a single-stage combustor setup equipped with loudspeaker actuation and a gain-delay (n−τ) controller for active stabilization. In the first experimental validation, we optimize the control parameters of a single-stage turbulent combustor with loudspeaker actuation and a gain-delay controller. For the second experimental validation, we apply the framework to a sequential combustor configuration utilizing nanosecond repetitively pulsed discharges (NRPD) as the control actuator. This demonstrates the framework’s adaptability to various control actuation methods in turbulent combustors where control parameter optimization is required.

High-density via RRAM cell with multi-level setting by current compliance circuits

In this work, multi-level storage in the via RRAM has been first time reported and demonstrated with the standard FinFET CMOS logic process. Multi-level states in via RRAM are achieved by controlling the current compliance during set operations. The new current compliance setting circuits are proposed to ensure stable resistance control when one considers cells under the process variation effect. The improved stability and tightened distributions on its multi-level states on via RRAM have been successfully demonstrated.

An Integrated Pest Management Strategy Approach for the Management of the Stable Fly Stomoxys calcitrans (Diptera: Muscidae).

Stable flies, Stomoxys calcitrans, stand as formidable pests with a global impact, inflicting significant economic losses on the livestock sector. Larval development occurs in diverse substrates, including decomposing plant material and manure, while emerged adults pose a threat through blood-feeding on both animals and humans. Conventional chemical control methods, predominantly reliant on insecticides, not only pose environmental risks but also face challenges of resistance among stable fly populations. To address this pressing issue, we propose an integrated pest management (IPM) strategy for stable fly control. This approach involved a combination of sanitary-cultural practices, animal protection, the release of natural enemies targeting immature stages, and a specialized trapping system for adults. The Stomoxycc® trap, designed for mass trapping of adult Stomoxys, was employed alongside the release of the predatory mite Macrocheles robustulus and two wasp parasitoids, Spalangia cameroni and Muscidifurax raptor (under the commercial brands Biomite® and Biowasp®) on animal bedding as a key component of this IPM strategy. The implementation of this initiative has been undertaken at a significant sanctuary for donkeys and mules in western Spain. In this publication, we present the application and results of the IPM strategy utilized and provide insights into its use as a sustainable and environmentally friendly option for controlling stable fly populations.

Abstract 5635: TPM4 overexpression promotes colon epithelial cell tumorigenesis in vitro and correlates with human colon tumor progression in vivo

Abstract Because colon cancer still exerts a heavy burden on human health, we are interested in identifying very early events in colon epithelial cell tumorigenesis in order to develop strategies to treat and possibly prevent colon carcinogenesis. We hypothesize that aberrant expression of genes involved in crypt-luminal colon epithelial cell migration, needed for proliferative, undifferentiated stem cells at the crypt bottom to mature into growth arrested, mature effector cells near the lumen, may disrupt normal differentiation and initiate events that can trigger the transition from a normal to premalignant state in colon epithelial cells. Forced upregulation of tropomyosin 4 (TPM4), an actin-binding protein that exerts many effects on contractility and migration, prevents certain aspects of Caco2 maturation induced by contact inhibition of growth, including increased expression of the digestive enzyme sucrose isomaltase and glutathione S-transferase alpha 1. Furthermore, TPM4.2-overexpressing cells exhibit numerous morphological abnormalities. Compared to untransfected Caco2 cells and empty-vector stable transfectant controls, TPM4.2-overexpressing Caco2 cell clones elaborate aberrant thin membrane protrusions and expanded membrane ruffles. Particularly striking are defective maintenance of dome structures at middle (day 14) and late (day 21) time points of induced differentiation in TPM4.2-overeexpressing clones. We further hypothesize that TPM4 is overexpressed in human colon tumors, particularly at early stages of tumorigenesis. In order to test this hypothesis, we examined TPM4 RNA expression in datasets obtained from human colon tissue at premalignant as well as tumorigenic stages. TPM4 trends toward overexpression in premalignant, relative to normal, human colon tissue (uninvolved mucosa from patients with familial adenomatous polyposis, or FAP) and is significantly overexpressed in adenocarcinoma tissue relative to normal controls. Interestingly, TPM4 overexpression may be more pronounced at earlier, rather than later, stages of adenocarcinoma. Our results suggest that aberrant TPM4 overexpression in colon epithelial cells can promote adoption of a tumorigenic state. Certain aspects of Caco2 cell maturation are disrupted by forced TPM4 overexpression during induced differentiation, and TPM4 overexpression correlates with early stages of colon tumorigenesis. Misregulation of TPM4 gene expression may therefore represent a critical molecular event that can block normal differentiation and induce early events in colon epithelial cell tumorigenesis. TPM4 expression level may therefore prove to be a useful biomarker that can, along with other diagnostics, help to determine tumorigenic progression in human colon tissue and to develop novel strategies to prevent colon cancer and treat it at early stages. Citation Format: Giulio Ferrero, Barbara Pardini, Alessio Naccarati, Michael Papetti. TPM4 overexpression promotes colon epithelial cell tumorigenesis in vitro and correlates with human colon tumor progression in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5635.

Learning Stabilization Control of Quadrotor in Near-Ground Setting Using Reinforcement Learning

With the development of intelligent systems, the popularity of using micro aerial vehicles (MAV) increases significantly in the fields of rescue, photography, security, agriculture, and warfare. New modern solutions of machine learning like ChatGPT that are fine-tuned using reinforcement learning (RL) provides evidence of new trends in seeking general artificial intelligence. RL has already been proven to work as a flight controller for MAV performing better than Proportional Integral Derivative (PID)-based solutions. However, using negative Euclidean distance to the target point as the reward function is sufficient in obstacle-free spaces, e.g. in the air, but fails in special cases, e.g. when training near the ground. In this work, we address this issue by proposing a new reward function with early termination. It not only allows to successfully train Proximal Policy Optimization (PPO) algorithm to stabilize the quadrotor in the near-ground setting, but also achieves lower Euclidean distance error compared to the baseline setup.

Abstract 417: Chromosomal instability leads to durable tumor suppression upon macrophage-checkpoint disruption, with induction of anti-tumor IgG

Abstract Solid tumors generally exhibit chromosome copy number variation caused by chromosomal instability (CIN) in mitosis, and the resulting aneuploidy associates with poor prognosis in various cancer types and poor Tcell checkpoint blockade response in melanoma. In such contexts, however, macrophages and the SIRPa-CD47 checkpoint are understudied. Here, CIN is induced pharmacologically in poorly immunogenic B16F10 mouse melanoma cells, generating micronuclei plus diverse aneuploidy and skewing macrophages towards an anti-cancer phenotype based on markers and short-term tumor studies. Mice bearing CIN-afflicted tumors with wild-type CD47 levels survive only slightly longer compared to chromosomally stable controls, but long-term survival can be maximized when anti-tumor IgG opsonization is combined with adoptive transfer of macrophages with SIRPa blockade or with CD47 knockout of the B16F10. Multi-epitope, de novo anti-cancer IgG in survivors promote phagocytosis of CD47 knockout B16F10 cells by macrophages and suppress tumoroids in vitro and growth of tumors in vivo. An unexpected benefit of pairing CIN with maximal macrophage anti-cancer activity is thus an anti-cancer vaccination-like response that can lead to durable cures and potentiate cell-mediated acquired immunity. Citation Format: Dennis E. Discher. Chromosomal instability leads to durable tumor suppression upon macrophage-checkpoint disruption, with induction of anti-tumor IgG [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 417.

Gradient‐Strained Van Der Waals Heterojunctions for High‐Efficient Photodetectors

AbstractMaximizing light‐to‐electricity conversion efficiency is a crucial challenge for the practical applications of 2D material photodetectors. However, due to the lack of stable and precise electronic structure control methods for 2D materials, the driving force of photogenerated carriers is insufficient that severely hinders the efficiency of separation and transport. Herein, a gradient‐modulated, stable and precise strain applied strategy for 2D materials is designed and constructed, which results in a significant improvement in the detect efficiency of ZnO/WSe2/graphene van der Waals heterojunction photodetectors. Different from the overall strain of all‐component materials in typical photodetectors, biaxial tensile strain is applied to WSe2 that can be precisely modulated by controlling the height of ZnO nanorods, while the strain is nearly unaffected to ZnO. As the strain modulation increases from 1.3% to 4.0%, the external quantum efficiency (EQE) of the heterojunction increases from 11.4% to 35.3%, representing a threefold increase. Furthermore, with increasing strain, the EQE can reach higher levels. Moreover, the strain‐enhanced conversion efficiency mechanism is elucidated that results from the synergistic effect of the strain‐induced WSe2 exciton convergence and the strain‐increased ZnO/WSe2 interface barrier, which enhances the carrier interface separation efficiency.

Self-tuning model predictive control for wake flows

This study presents a noise-robust closed-loop control strategy for wake flows employing model predictive control. The proposed control framework involves the autonomous offline selection of hyperparameters, eliminating the need for user interaction. To this purpose, Bayesian optimization maximizes the control performance, adapting to external disturbances, plant model inaccuracies and actuation constraints. The noise robustness of the control is achieved through sensor data smoothing based on local polynomial regression. The plant model can be identified through either theoretical formulation or using existing data-driven techniques. In this work we leverage the latter approach, which requires minimal user intervention. The self-tuned control strategy is applied to the control of the wake of the fluidic pinball, with the plant model based solely on aerodynamic force measurements. The closed-loop actuation results in two distinct control mechanisms: boat tailing for drag reduction and stagnation point control for lift stabilization. The control strategy proves to be highly effective even in realistic noise scenarios, despite relying on a plant model based on a reduced number of sensors.

Modeling of nonlinear supply chain management with lead-times based on Takagi-Sugeno fuzzy control model

In this study, we present a novel fuzzy control strategy that considers the influence of lead times on nonlinear supply chain management (SCM) systems. The authors have constructed a fuzzy control model for an SCM model based on nonlinear phenomena with lead times, drawing inspiration from Takagi-Sugeno's (T-S) fuzzy control system. Additionally, the authors have designed a fuzzy H∞ control approach to mitigate the effects of lead times, switch between sub-models, and address customers' stochastic requests, all while considering the concept of maximal overlapped rules groups. The control of nonlinear supply chain management is implemented in a manner that ensures asymptotic stability and facilitates smooth transitions from one subsystem to another. The introduction of the membership function into the fuzzy system results in reduced fluctuations in the system's variables. The key contributions of the research include the design of a fuzzyH∞ control approach, leveraging maximal overlapping-rules group (MRRG) to ensure stability through localized definite positive matrices identification. This strategy achieves two primary objectives: asymptotic stability of the supply chain system and smooth switching between nonlinear SCM elements. The authors demonstrate the effectiveness of their proposed method through comprehensive comparisons with the fuzzyH∞ control technique. The study provides a valuable insight into managing lead times in SCM through dynamic and stable control methodologies. Finally, a comprehensive comparison is provided between the suggested H∞ management approach and the fuzzyH∞ control strategy. This comparison is demonstrated through two-stage nonlinear numerical simulations, keeping SCM lead times in mind. Movement results are presented to illustrate the effectiveness of our suggested algorithm.

Neural Network-Based Formation Flying Using Aerodynamic Forces via Variable Shape Function

Conventional formation flying Earth-orbiting satellites control their orbits to perform their missions using thrusters, but the amount of propellant loaded into a satellite is limited. Therefore, the use of aerodynamic forces for orbit control has been attracting attention, particularly in low Earth orbit. The orbit control can be achieved by appropriately changing the state of satellite, such as attitude and shape, to meet the aerodynamic requirements. In modeling the relationship between satellite state and aerodynamic forces, conventional methods ignore the shielding caused by the nonconvexity of the satellite’s appearance. Ignoring the shielding creates a gap between the modeled and the real aerodynamic forces, resulting in poor control performance. To solve this problem, we propose an aerodynamic force modeling method that incorporates a neural network to estimate the shielding. We train the neural network using data from an aerodynamics simulator. The optimal state that not only generates the required aerodynamic forces but also improves controllability under various mechanical constraints is obtained by solving an optimization problem that incorporates the proposed aerodynamic model. We conduct numerical simulations for establishing and maintaining general circular orbit formations. The results show convergence and continuous stable control of the deputy satellite to the ideal orbit.

Cooperative control strategy of trajectory tracking and driving stability for distributed-drive vehicles under extreme conditions

Active chassis dynamics control has become increasingly important in recent years for achieving high-level automated driving of intelligent electric vehicles. To improve tracking performance of the commonly-used model predictive control (MPC) in extreme scenarios, such as emergency lane changing, an improved MPC motion stability controller (MPC_ISMC) based on sliding mode variable structure active steering control and differential torque yaw control is proposed. Firstly, an MPC-based trajectory tracking controller is designed to track the desired trajectory. Subsequently, active front-wheel steering (AFS) and direct yaw moment (DYC) controllers are designed by using the backstepping sliding mode variable structure and the adaptive super-twisting sliding mode variable structure, respectively. Moreover, a cooperative control law for the AFS and the DYC is formulated by the smooth switching function and intervention intensity factor with respect to vehicle stable state and trajectory tracking error. After that, a torque distribution scheme of four wheels is optimized to achieve the additional yaw moment target. Finally, the effectiveness of the proposed cooperative control strategy is validated using a hardware-in-the-loop platform. The results indicate that the proposed controller has better performance in trajectory tracking and vehicle stability under extreme conditions compared with traditional MPC and other methods.

Simple robust PD control for asymptotic stabilization of uncertain spacecraft with actuator constraints and disturbances

AbstractThis paper concerns the problem of asymptotic attitude stabilization for uncertain spacecraft subject to actuator constraints and bounded external disturbances. A simple model‐free saturated robust PD control is proposed within the framework of PD design methodology combining sliding mode control strategy. Asymptotic stabilization stability is proved in terms of Lyapunov's direct method and Barbalat's lemma. Advantages of the proposed control include the easy‐going implementation with simple and intuitive structure and explicit tuning procedure and abilities to guarantee the asymptotic attitude stabilization and bounded disturbance rejection and ensure the actuator constraint is not violated by selecting the control gains a priori. Simulation comparisons demonstrate the improved performance of the proposed approach.

Anticipatory postural adjustments during an archery performance

AbstractThe present study investigated whether there is an anticipatory postural adjustment (APA) prior to the onset of predictable perturbation generated by a releasing in the archery performance task. Eighteen volunteers were divided into two groups (expert vs. novice) depending on skill levels. Participants stood on a force plate holding a bow with a laser pointer and took a full drawing motion aiming the laser pointer to the center of target, placed 10 m away from the force plate, then released motion was performed based on the individual's preferred timing. Motion capture system measured the bow motion in 3D, two force plates estimated center of pressure (COP) displacements, and electromyographic (EMG) recorded eight lower limb muscles during the anticipatory and compensatory phases of postural control. The results revealed that, compared to the novice group, (1) the expert group showed smaller variability of bow motion, (2) the APA patterns of the COP and muscle activities before the onset of perturbation (releasing), and (3) shorter time interval between the perturbation onset and the COP peak displacement. The findings suggest that a long‐term practice results in the experts could be adopted to use APA generating mechanical benefit to compensate for predictable perturbation. This would be able to lead to a more stable subsequent postural control to realize the task goal.

Comparative study of maximum power point tracking control for PV arrays system integration process

Given the stochastic nature of solar energy, integrating PV arrays with energy storage systems (ESSs) is crucial for a consistent power supply. Effective maximum power point tracking (MPPT) control is of great importance in this context. The significance lies not just in achieving high energy efficiency but also in ensuring overall system safety and reliability, as rapid and stable power tracking control alleviates power stress on ESSs. Challenging factors like parameter uncertainty from the PV array linearization process, dynamic DC bus variations induced by pulsed power loads, and sudden irradiance variation under cloudy weather, make the maximum output power of PV arrays largely dependent on the effectiveness of the control laws. Therefore, this paper presents a comparative study for MPPT control using PV-interfaced DC/DC converters. The aim is to demonstrate the advantages of employing average large-signal model-based sliding mode-type controllers in PV system integration with health-conscious objectives. A comprehensive system-level control-oriented modeling process is presented, followed by the theoretical background of three types of controllers (classical proportional–integral (PI)-type controller, μ-synthesis-based robust controller, and sliding mode controller) for practical applications. Extensive tests with real experimental data reveal sliding mode-type controllers’ superiority in various integration scenarios. For example, with random solar irradiance variation over 0.5 s, they achieve a 54.75% reduction in energy loss, outperforming the commonly used PI-type counterparts.

Hierarchical Stabilization and Tracking Control of a Flexible-Joint Bipedal Robot Based on Anti-Windup and Adaptive Approximation Control

Hierarchical Stabilization and Tracking Control of a Flexible-Joint Bipedal Robot Based on Anti-Windup and Adaptive Approximation Control

Characterization of V-containing black plasma electrolytic oxide coatings on aluminium alloy: Impact of base electrolytes

Black oxide coatings on 6061 Al alloy were produced by plasma electrolytic oxidation (PEO) process in silicate- and phosphate-rich base electrolytes with addition of ammonium metavanadate (NH4VO3), respectively. The characteristics of PEO processes and resulting coatings were investigated. The thermal control properties of the coatings were measured. The corrosion protection performances of the coatings in an acidic environment were evaluated. Results show that the base electrolytes have significant influence on the PEO discharge behavior, coating characteristics and properties. There are more intensive discharges in silicate-rich electrolyte with addition of NH4VO3 (denoted as “Si–V″) than that in phosphate-rich electrolyte with addition of NH4VO3 (denoted as “P–V”), leading to higher thickness and more defects for the Si–V coating. Both Si–V and P–V coatings are characterized by black color in appearance due to the formation of V-containing compounds. However, the P–V coating has higher contents of V-containing compounds and therefore lower lightness and higher solar absorptance (αs) than the Si–V coating. In addition, the P–V coating exhibits better corrosion protection performance and color durability compared with the Si–V coating in acidic NaCl solution (pH = 3.1⁓3.3) due to the formation of AlPO4 in the coating. It is demonstrated that phosphate-rich base electrolyte with addition of NH4VO3 is a good candidate for getting stable black therm control coatings on Al alloy for practical applications in harsh environment.

Hybrid Mode: Routinization of the Transition Mode as the Third Common Mode for Compound VTOL Drones

Fixed-wing Vertical Takeoff and Landing (VTOL) drones have been widely researched and applied because they combine the advantages of both rotorcraft and fixed-wing drones. However, the research on the transition mode of this type of drone has mainly focused on completing the process quickly and stably, and the application potential of this mode has not been given much attention. The objective of this paper is to routinize the transition mode of compound VTOL drones, i.e., this mode works continuously for a longer period of time as a third commonly used mode besides multi-rotor and fixed-wing modes, which is referred to as the hybrid mode. For this purpose, we perform detailed dynamics modeling of the drone in this mode and use saturated PID controllers to control the altitude, velocity, and attitude of the drone. In addition, for more stable altitude control in hybrid mode, we identify the relevant parameters for the lift of the fixed-wings and the thrust of the actuators. Simulation and experimental results show that the designed control method can effectively control the compound VTOL drone in hybrid mode. Moreover, it is proven that flight in hybrid mode can reduce the flight energy consumption to some extent.

Flexible development and evaluation of machine‐learning‐supported optimal control and estimation methods via HILO‐MPC

AbstractModel‐based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used successfully for decades in many engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model‐based approaches. Thanks to recent technological advancements in digitalization, machine‐learning methods such as deep learning, and computing power, there has been an increasing interest in using machine learning methods alongside model‐based approaches for control and estimation. The number of new methods and theoretical findings using machine learning for model‐based control and optimization is increasing rapidly. However, there are no easy‐to‐use, flexible, and freely available open‐source tools that support the development and straightforward solution to these problems. This article outlines the basic ideas and principles behind an easy‐to‐use Python toolbox that allows to solve machine‐learning‐supported optimization, model predictive control, and estimation problems quickly and efficiently. The toolbox leverages state‐of‐the‐art machine learning libraries to train components used to define the problem. Machine learning can be used for a broad spectrum of problems, ranging from model predictive control for stabilization, set point tracking, path following, and trajectory tracking to moving horizon estimation and Kalman filtering. For linear systems, it enables quick generation of code for embedded model predictive control applications. HILO‐MPC is flexible and adaptable, making it especially suitable for research and fundamental development tasks. Due to its simplicity and numerous already implemented examples, it is also a powerful teaching tool. The usability is underlined, presenting a series of application examples.

Robust-optimal control of rotary inverted pendulum control through fuzzy descriptor-based techniques

Expanding upon the well-established Takagi–Sugeno (T–S) fuzzy model, the T–S fuzzy descriptor model emerges as a robust and flexible framework. This article introduces the development of optimal and robust-optimal controllers grounded in the principles of stability control and fuzzy descriptor systems. By transforming complicated inequalities into linear matrix inequalities (LMI), we establish the essential conditions for controller construction, as delineated in theorems. To substantiate the utility of these controllers, we employ the rotary inverted pendulum as a testbed. Through diverse simulation scenarios, these controllers, rooted in fuzzy descriptor systems, demonstrate their practicality and effectiveness in ensuring the stable control of inverted pendulum systems, even in the presence of uncertainties within the model. This study highlights the adaptability and robustness of fuzzy descriptor-based controllers, paving the way for advanced control strategies in complex and uncertain environments.