Stable Control Research Articles

Steering collision avoidance and lateral stability coordinated control based on vehicle lateral stability region

The balance between vehicle lateral stabilization and collision avoidance is critical for steering collision avoidance in emergency situations. On the one hand, emergency steering may cause a vehicle to lose its lateral stability. On the other hand, the overly conservative stability controller may compress the safety margin of vehicle collision avoidance, leading to failure of collision avoidance. Therefore, steering collision avoidance and lateral stability coordinated control (SCALSC) based on the vehicle stability region is proposed. The Lyapunov’s Second Method is used to obtain the lateral stability region instead of the linear two-degree-of-freedom (2-DOF) vehicle states as the stability tracking target to ensure that the vehicle states are in the stability region. The SCALSC includes an active steering controller and a direct-yaw-moment controller (DYC). An active steering controller is used for collision avoidance in emergency conditions, while DYC is used for stability control. An intervention criterion for the DYC system is proposed by using the Hurwitz criterion. Finally, a simulation test was carried out based on MATLAB/Simulink. The simulation results show that the proposed coordinated control method ensures stability, improves the safety margin of collision avoidance, and realizes multiobjective coordinated control of collision avoidance and autonomous vehicle stability control in emergency situations.

Path tracking fault-tolerant control of intelligent distributed drive electric vehicle based on sliding mode observer

In this paper, an intelligent distributed drive electric vehicle is used as the research object. The research aims to solve the problem of path-tracking control of intelligent vehicles when the single-side in-wheel motor of the front axle fails. A fault-tolerant control method based on sliding mode observer is proposed. Firstly, an overall vehicle dynamics model considering the failure factor is established, and the failure of the in-wheel motor is monitored by the sliding mode observer. Torque coordination compensation control is activated according to the observed failure. The control system mainly consists of two parts: the torque-coordinated lateral stability controller and the prediction fault tolerant deviation compensation controller. The torque-coordinated lateral stability controller controls the torque of the other wheels to balance the front wheel torque, and the prediction tolerant deviation compensation controller uses Model Predictive Control (MPC) to control the front wheel angle and rear wheel torque of the vehicle to reduce the lateral deviation. Finally, under double-lane change conditions, dynamics simulations and hardware-in-loop experiments are performed to validate the effectiveness of the proposed control method.

A finite-time path following scheme of unmanned surface vessels with an optimization strategy

The path-following problem of unmanned surface vessels (USVs) is solved in this paper, considering unknown interference, energy-saving strategy, input constraints, and input rate of change constraints. Firstly, the USV model considering lumped interference is given, while an interference observer based on finite-time theory is used for interference estimation where the convergence time is specified. Then, to accelerate the USV convergence to a given path, a novel improved adaptive finite-time line-of-sight (IAFTLOS) scheme is developed. The proposed IAFTLOS scheme makes the cross-path error with finite-time convergence. At the same time, an adaptive time-varying looking distance law is designed to improve the convergence performance further. Next, nonlinear model predictive control is introduced to deal with rates of change and input constraints. In addition, an energy-saving strategy is incorporated into the design of the controller. Finally, stable control of the whole cascade system is achieved, and comparative simulation cases verify the feasibility and robustness of the entire system.

Nonlinear Predictive Control of Diesel Engine DOC Outlet Temperature

In the regeneration mode, precise control of the Diesel Oxidation Catalyst (DOC) outlet temperature is crucial for the complete combustion of carbon Particulate Matter (PM) in the subsequent Diesel Particulate Filter (DPF) and the effective conversion of Nitrogen Oxides (NOx) in the Selective Catalytic Reduction (SCR). The temperature elevation process of the DOC involves a series of intricate physicochemical reactions characterized by high nonlinearity, substantial time delays, and uncertainties. These factors render effective and stable control of the DOC outlet temperature challenging. To address these issues, this study proposes an approach based on Long Short-Term Memory (LSTM) neural networks for Model Predictive Control (MPC), emphasizing precise control of the Diesel Oxidation Catalyst’s outlet temperature during the regeneration mode. To tackle the system’s nonlinear characteristics, LSTM is employed to construct a predictive model for the outlet temperature of the Diesel Oxidation Catalyst, thereby enhancing prediction accuracy. Simultaneously, model predictive control is applied to mitigate the significant time delays inherent in the system. The gradient descent algorithm is utilized within a rolling optimization cycle to optimize the objective function, enabling the rapid determination of the control law. To validate the performance of the proposed control strategy, tracking performance and disturbance rejection tests are conducted. Simulation results demonstrate that, compared to the traditional Proportional Integral Derivative (PID) controller, this control strategy exhibits superior tracking performance and disturbance rejection capabilities. In the regeneration mode, the adoption of this control strategy enables more effective and precise control of the Diesel Oxidation Catalyst’s outlet temperature.

Servo navigators: Linear regression and feedback control for rigid-body motion correction.

Navigator-based correction of rigid-body motion reconciling high precision with minimal acquisition, minimal calibration and simple, fast processing. A short orbital navigator (2.3 ms) is inserted in a three-dimensional (3D) gradient echo sequence for human head imaging. Head rotation and translation are determined by linear regression based on a complex-valued model built either from three reference navigators or in a reference-less fashion, from the first actual navigator. Optionally, the model is expanded by global phase and field offset. Run-time scan correction on this basis establishes servo control that maintains validity of the linear picture by keeping its expansion point stable in the head frame of reference. The technique is assessed in a phantom and demonstrated by motion-corrected imaging in vivo. The proposed approach is found to establish stable motion control both with and without reference acquisition. In a phantom, it is shown to accurately detect motion mimicked by rotation of scan geometry as well as change in global B0 . It is demonstrated to converge to accurate motion estimates after perturbation well beyond the linear signal range. In vivo, servo navigation achieved motion detection with precision in the single-digit range of micrometers and millidegrees. Involuntary and intentional motion in the range of several millimeters were successfully corrected, achieving excellent image quality. The combination of linear regression and feedback control enables prospective motion correction for head imaging with high precision and accuracy, short navigator readouts, fast run-time computation, and minimal demand for reference data.

Enhanced sliding mode controller design via meta‐heuristic algorithm for robust and stable load frequency control in multi‐area power systems

AbstractThis article introduces a novel approach named HBA‐dHoSMO, which combines a continuous decentralized higher‐order sliding mode controller‐based observer (dHoSMO) with the honey badger algorithm (HBA), specifically designed for load frequency control in multi‐area power systems (MAPSs). Traditional sliding mode controllers (SMCs) employed in load frequency control of MAPSs often face challenges related to chattering and oscillations, leading to decreased robustness and stability. Additionally, tuning the parameters for these SMC designs to achieve optimal performance in MAPSs can be challenging. The HBA‐dHoSMO is proposed to address the issues of chattering and oscillations, while the optimal parameters for SMC design are obtained using HBA. The stability analysis of the entire system is conducted using linear matrix inequality and the Lyapunov stability theory, affirming the reliability and feasibility of the approach. A comprehensive set of case studies is performed under various configurations and conditions. Additionally, particle swarm optimization and tuna swarm optimization, in conjunction with SMC‐based and proportional–integral–derivative controllers, are examined for performance comparison. Simulation results demonstrate the superior performance of the proposed controller across all case studies. This is evidenced by the lowest integral time absolute error values recorded as 0.0133, 6.45 × 10−4, and 0.0167 for single‐, two‐, and three‐area power systems, respectively.

Human serum albumin as the carrier to fabricate STING-activating peptide nanovaccine for antitumor immunotherapy

Tumor vaccines are emerging as one of the most promising therapeutic strategies for cancer treatment. With the advantages of low toxicity, convenient production and stable quality control, peptide vaccines have been widely used in preclinical and clinical trials involving various malignancies. However, when used alone, they still suffer from significant challenges including poor stability and immunogenicity as well as the low delivery efficiency, leading to limited therapeutic success. Herein, the STING-activating peptide nanovaccine based on human serum albumin (HSA) and biodegradable MnO2 was constructed, which can improve the stability and immunogenicity of antigenic peptides as well as facilitate their uptake by dendritic cells (DCs). Meanwhile, Mn2+ degraded from the nanovaccine can activate the STING pathway and further promote DCs maturation. In this way, the prepared nanovaccine can efficiently mediate T-cell immune responses, thereby exerting the effects of tumor prevention and therapy. Moreover, the prepared nanovaccine possesses the advantages of low cost, convenient preparation and good biocompatibility, showing great potential for practical applications.

Finite‐time robust simultaneous stabilization control for two nonlinear singular time‐delay systems

AbstractIn this paper, the finite‐time simultaneous stabilization problem of two nonlinear singular time‐delay Hamiltonian systems is investigated. Firstly, the two general nonlinear singular time‐delay Hamiltonian systems are transformed into their Hamiltonian differential‐algebraic forms by equivalent transformation, respectively, they are then converted to two strictly dissipative Hamiltonian forms via the state feedback method, and two strictly dissipative Hamiltonian systems are extended into a Hamiltonian one. Based on which, a suitable Hamiltonian function is constructed to analyze the finite‐time simultaneous stability and finite‐time robust simultaneous stabilization problem. Finally, an illustrative example is given to show the effectiveness of the results obtained. It should be pointed out that since there does not exist a finite‐time stability criterion on the nonlinear singular delay system, we adopt a new double compound asymptotic method.

Decoding the Link Between XPC and Lung Cancer Susceptibility: A Study of Cigarette Smoke-Induced DNA Damage in the Setting of XPC Deficiency

Background/Objective:
 Despite the known mutagenicity of cigarette smoke, only 10-15% of smokers will develop lung cancer in their lifetimes. What determines a smoker’s susceptibility to lung cancer is poorly understood. We identify the nucleotide excision repair (NER) protein Xeroderma pigmentosum Complementation Group C (XPC) as a tumor suppressor that may contribute to lungtumorigenesis when mutated and combined with cigarette smoke. Micronuclei, which are chromosome fragments and/or lagging chromosomes separated from the main nucleus, occur in many cancers, and indicate genomic instability. We hypothesize that cigarette smoke and XPC knockdown will cause genomic instability that will activate the DNA Damage Response (DDR) and increase the frequency of micronuclei and nuclear aberrancies.
 Methods:
 A human bronchial epithelial cell line (Beas-2B), and two lung adenocarcinoma cell lines (H1299 and A549) with stable XPC lentiviral knock-down (shXPC) or control shRNA (shCtrl) were treated with cigarette smoke extract (CSE) or air control (AC). DNA Damage Response (DDR) proteins were analyzed via immunoblot (Western). Micronuclei and nuclear aberrancies were quantified through cytokinesis-block micronucleus assay (CBMT) using immunofluorescence microscopy (DAPI).
 Results:
 Both CSE and XPC knockdown independently amplify expression of phospho-ATM (pATM), a DDR protein, in H1299 cells. Nuclear aberrancies increased significantly (p<0.05) with CSE in all three cell lines. Micronucleus frequency increased significantly with CSE in H1299 and Beas-2B cells (p<0.05) and with XPC knockdown in Beas-2B cells compared to shCtrl (p<0.001).
 Scientific Impact and Implications:
 We identified a previously uncharacterized role of XPC deficiency in augmenting cigarette smoke induced chromosomal breaks manifesting as micronuclei, particularly in non-cancerous Beas-2B cells. These findings offer insight into tumorigenesis in cigarette smoking and shed light on mechanisms of continued DNA damage in cancer cells. Future research should clarify the mechanisms of micronucleus formation in human translational specimens and pinpoint additional functions of XPC beyond NER, including in replication repair.

Optimal control of three-dimensional unsteady partial differential equations with convection term in continuous casting

In this paper, we investigate an optimal control problem (OCP) for three-dimensional (3D) unsteady partial differential equations with convection term (UPDECT), extensively applied to the solidification process of billets. Firstly, we introduce the mathematical model of OCP and adopt a life cycle model method (LCMM) to numerically solve the 3D UPDECT. Secondly, the Lipschitz continuity of the cost function gradient is proved using the adjoint approach as a numerical method for designing optimal control systems. The cost function gradient of OCP for 3D UPDECT is analyzed based on a Hamiltonian costate system. Thirdly, we propose a modified trinomial conjugate gradient method (MTCGM) to solve the OCP for 3D UPDECT and prove the global convergence of MTCGM. Finally, the MTCGM is applied to the experimental simulation of continuous casting. According to experiment results, the optimizer based on MTCGM can significantly accelerate the convergence speed and reduce the iterative numbers, and the MTCGM provides a more stable temperature control effect.

Decomposition of Equations of Nonlinear Affine Control Systems and its Application to the Synthesis of Regulators

The article deals with the decomposition of nonlinear differential equations based on the group-theoretic approach. At the beginning, the decomposition of differential equations of linear systems using a transition matrix of state is presented, and then, based on the theory of continuous groups (Lie groups), the process of decomposition of differential equations of nonlinear systems is shown. The decomposition approach is based on the isomorphism theorem of the space of vector fields and Lie derivatives, which allows us to consider vector fields as differential operators of smooth functions. A formula is derived about the adjoin representation of a Lie group in its Lie algebra, which actually determines the finding of a vector field that characterizes the interaction of two or more vector fields. The Lie algebra of derivatives makes it possible to determine the infinitesimal action of the Lie group, i.e. the linearization of this action is carried out (transformation of the points of the trajectory space of the original system in a small neighborhood). Decomposition allows, as in the linear case, to separate the finding of an action (only locally) of a group of transformations from the transformed points themselves. For linear systems, this separation is global. It is also shown that the decomposition of linear equations is a particular case of the decomposition of nonlinear equations. An algorithm of the method of model predictive control with Gramian weighting using this decomposition is presented. A practical example of decomposition and application of the model predictive control for stabilization of a nonstationary nonlinear system is considered.

An individualized robust stability control strategy for active front steering vehicles

To improve the vehicle stability and driver steering performance, this paper presents an individualized yaw stability control strategy based on H∞ robust control for active front steering (AFS) vehicles. A driver-vehicle system, including a driver steering model and a vehicle dynamics model with AFS, is formed. To analyze the steering characteristics of different drivers, a set of driving data of 36 drivers is collected, and the driver’s characteristics parameters are identified by using the particle swarm optimization (PSO) algorithm. A general evaluation function considering the trajectory tracking performance, vehicle stability, driver workloads, and driver’s characteristics parameters are established to evaluate the comprehensive steering performance. To accomplish the personalized control of vehicle yaw stability, an individualized H∞ robust yaw stability controller is presented by adjusting the gain of the weighting function according to the general evaluation of each driver. Driver-in-the-loop experiment is conducted based on the Matlab/Simulink-CarSim®-Prescan co-simulation platform, and the results demonstrates that the proposed control strategy can provide driver with individualized driving assistance while improving the overall driving performance and reducing the driver’s workloads.

Design and Control of a Reconfigurable Robot with Rolling and Flying Locomotion

Given the continual rise in mission diversity and environmental complexity, the adept integration of a robot’s aerial and terrestrial locomotion modes to address diverse application scenarios has evolved into a formidable challenge. In this paper, we design a reconfigurable airframe robot endowed with the dual functionalities of rolling and flying. This innovative design not only ensures a lightweight structure but also incorporates morphing capabilities facilitated by a slider-crank mechanism. Subsequently, a land-to-air transformation strategy for the robot is introduced, achieved through the coordinated movement of the robotic arm and the servo motor. To ensure stable control of the robot amid external wind disturbances, we leverage the collaboration between a Generative Adversarial Network (GAN)and a Nonlinear Model Predictive Control (NMPC) controller. After the wind force magnitude is predicted through the neural network, the robot’s adeptness in flexible trajectory tracking is verified. Under simulated wind conditions of 12.1 m/s, the trajectory error consistently remains within the range of 10–15 cm, affirming the effectiveness of this control method.

Model predictive control for pose synchronization of satellite proximity via the Takagi–Sugeno fuzzy modeling method

On-orbit servicing has become one of the challenging frontiers of space operations. Most of the on-orbit missions require an important technology, pose synchronization, to prepare or implement the tasks. The nonlinearity caused by the couple between orbit and attitude often brings inconvenience when designing series of control strategies. This paper addresses a novel reformulation for pose dynamics based on dual quaternion, in which the problem of the nonlinearity of pose coupled dynamics for rigid spacecraft is mainly concerned. In this research, we intend to use the Takagi–Sugeno (TS) fuzzy modeling method to remodel the pose dynamics based on dual quaternion. Based on the reconstructed model, the original nonlinear system can be equivalently linearized in the fuzzy field. Subsequently, a global stabilized controller for system stabilization as a feasible control scheme is proposed. For further research, a feedback-based model predictive control (MPC) strategy is proposed to optimize the fuel consumption during the stabilization of the system. Finally, numerical simulations are provided to verify the effectiveness of the proposed modeling and control strategies.

Experimental investigation on the characteristics of a controllable separate heat pipe-based cold storage temperature control system

PCM cold storage systems have been widely employed in household refrigerators due to their economy and efficiency. However, the PCM heat transfer process is out of effective control, limiting the accurate temperature control ability of the system. Herein, a controllable separated heat pipe that can regulate heat transfer is introduced into the PCM cold storage system to resolve its issues. A novel controllable separate heat pipe-based cold storage temperature control system is proposed. The dynamic temperature control ability and cold storage characteristics of the system under various operating conditions are revealed through experiments. As a result, the spatial inhomogeneity of the temperature field of the PCM is discovered which is related to the unsteady heat transfer between the PCMs in different regions and to the on–off state of the controllable separate heat pipe. In the discharging mode, the stable temperature control time of the compartment decreases by 53% when the temperature decreases from 7 °C to 1 °C. In addition, a lower inlet temperature of the ethylene glycol solution results in faster cold storage rates and greater temperature differences of the PCMs in different regions (the maximum temperature difference is 3.47 °C when the inlet temperature is −26 °C). Moreover, the results of door opening experiments show that the temperature can be controlled within an accuracy of 2.5 °C when the door opening time is 60 s, demonstrating the temperature regulation ability of the system. The results verify the feasibility of the technical route and provide a valuable reference for the optimization of precise temperature control systems.

State-of-the-art of cold energy storage, release and transport using CO2 double hydrate slurry

CO2 hydrate slurry is a promising cold storage and transport medium due to the large latent heat, favorable fluidity and environmental friendliness, and the CO2 utilization can also be simultaneously achieved. However, the phase change pressure of CO2 hydrate is too high for applications in refrigeration system, thus the thermodynamic promoters are used to moderate the phase change conditions by forming CO2 double hydrate. In this review, a comprehensive summarization of research progresses of CO2 double hydrate is presented, encompassing the aspects of thermodynamic and kinetic characteristics, cold storage and release, cold transport and system operation. Quaternary salts, tetrahydrofuran, and cyclopentane are concluded to be the main thermodynamic promoters used to alleviate hydrate formation conditions. The cold storage capacity of CO2 double hydrate slurry highly depends on CO2 absorptivity which can be enhanced by increasing pressure, adding kinetic promoters and external disturbance. The non-Newtonian behaviors of most CO2 double hydrate slurries exhibit shear-thinning characteristics and addition of anti-agglomerates can effectively reduce their apparent viscosity. There is an optimal hydrate mass fraction and phase change temperature that make the system energy efficiency the highest due to the reduced pumping power consumption and improved primary refrigeration system efficiency. On the way to practical applications, the research challenges remain in elucidation of underlying mechanism of CO2 absorption and release with the presence of kinetic promoters, stable control of cold storage and release processes, and the detailed heat and mass transfer characteristics. In addition, operation strategy, optimization techniques, economic analysis and CO2 management method of the entire system are potential research topics.

Asynchronous sampled-data control for connected vehicles with heterogeneous delays

This study investigates an asynchronous sampled-data control problem of vehicular platoons, with heterogeneous sampling, subjected to actuator delays. Without a synchronized clock, a completely asynchronous sampled-data controller is designed for each follower, where the state of the ith follower itself and its neighboring vehicles are sampled at their own sampling time instants. The caused closed-loop tracking error dynamics for the entire platoon considering the effect of the nonuniform sampling time intervals, heterogeneous vehicle dynamics, inter-vehicle topology and heterogeneous time delays. To simplify the stability analysis and controller design, the tracking-error dynamics of the entire platoon are decomposed into individual subsystems with reduced-order dynamics. Based on Lyapunov stability theory, the optimal conditions are explored to design an asynchronous sampled-data controller to guarantee the desired stability performance. Moreover, the exact values for the maximum allowable sampling interval and time delay are calculated for each follower using the designed feedback controller gain. The proposed asynchronous sampled-data control method is extended to a vehicular platoon using an event-based sampling scheme. Numerical examples are used to verify the effectiveness of the proposed sampled-data control method.

Saturated H∞ control of networked systems under stochastic transmission delays: The random sampling periods case

AbstractIn this paper, the saturated control problems of networked systems with random sampling periods and stochastic transmission delays are investigated. The main issue is to design an saturated controller such that the controlled close‐loop system is exponentially mean‐square stable with a desired performance. A matrix exponential which is different from some existing literature is used to reconstruct the discrete‐time stochastic system to an equivalent yet tractable augmented model without any additional conditional constraint. In virtue of reduced‐order confluent Vandermonde matrix approach and Kronecker product operation, the stability condition in terms of linear matrix inequality is derived and then the desired saturated controller is designed. It should be emphasized that the improved method can also be used for the stability analysis and controller design when the system matrix contains complex roots. Finally, two numerical examples and a three‐axis milling machine system are utilized to manifest the reliability and practicality of the designed strategy.

Leveraging digital twin for autonomous docking of a container truck with stabilization control

AbstractThis paper describes the design, development, and implementation of a high‐precision autonomous docking control system for a container truck based on a digital twin approach. The digital twin is used to simulate the dynamic behavior of the physical truck and to design the controller by providing a virtual platform to test, validate, and optimize control strategies and algorithms before their deployment in the actual system. To this end, a cascade of a nonlinear observer and an unscented Kalman filter is used to estimate the state variables of the physical truck for point‐stabilization and orientation controls during the autonomous docking process. The docking motion involves two stabilization problems: point stabilization for smooth motion from the initial configuration to the docking slot, and orientation control to deliver the container truck to the final docking position with a margin of error of 5 cm for position and 0.0087 rad for orientation. The stability of both controllers is investigated, and simulations and experiments are conducted to demonstrate the accuracy of the proposed method in a container terminal environment.

Toward Stabilizable Large-Scale Boolean Networks by Controlling the Minimal Set of Nodes

This paper studies <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">structural</i> controllers and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed</i> pinning controllers for the global stabilization of Boolean networks (BNs) by integrating the information on their network structures with nodal dynamics. The main contribution is that several computationally efficient procedures are presented to reduce the number of controlled nodes and to determine a minimal set of controlled nodes without using the brute-force searches. The primary objective is to identify a minimal set of nodes that need to be controlled in the structural controllers for the strong structural stabilization of BNs when network structures is available yet nodal dynamics are unknown. To this end, a theorem shows this minimum controlled node problem can be addressed by seeking the minimum feedback vertex set of network structure. The subsequent part concentrates on designing distributed pinning controllers that merely rely on the node-to-node information exchanges for the global stabilization of BNs with the full knowledge of the nodal dynamics. Several sufficient conditions are provided by utilizing the irreducibility and activation-inhibition network structures to reduce the conservatism. Notably, we claim that, for regulatory BNs without positive cycles, the minimal set of pinned nodes can be determined with a linear amount of time subject to the total number of logical operators in the nodal dynamics. Finally, the effectiveness of these results are validated by three case studies.