Dynamic Control Scheme Research Articles

Dynamic performance-guaranteed adaptive event-triggered trajectory tracking control for underactuated surface vehicles

This paper investigates the trajectory tracking control problem of underactuated surface vehicles in the presence of model uncertainties and external disturbances. A dynamic performance-guaranteed adaptive event-triggered control scheme is proposed to transform the original constrained control problem into an equivalent unconstrained problem and ensure that the trajectory tracking is achieved with prescribed performance. By developing a dynamic event-triggered mechanism with adjustable thresholds that can be updated in an adaptive way, the communication burden and actuator wear can be effectively alleviated. To avoid differential explosions that may increase system complexities, an improved transformation-adapted dynamic surface control (DSC) is designed to eliminate adverse effects induced by performance functions and make the DSC applicable to performance-transformed systems. By utilizing the parameter compression algorithm, model uncertainties and external disturbances can be effectively addressed, and the adaptive laws can be integrated into a concise form, with only two online updating parameters required. Comprehensive analysis is provided to verify that all error signals are semi-globally uniformly ultimately bounded (SGUUB). Compared to existing approaches, the proposed control scheme exhibits lower update frequency.

Dynamic mater-slave control strategy for transient coordination of inverter based microgrids under asymmetric faults

The ever-expanding inverter-based microgrids (MGs) require the grid-forming distributed renewable energy resources (DERs) to enhance the system safety. However, the different types of grid-forming DERs are prone to have transient conflicts with each other under short circuit faults, leading to unexpected outage or even system collapse. The fast response and poor overcurrent capability of DERs make transient coordination a challenge. This paper proposes a dynamic master–slave architecture for transient coordination control in islanded MGs under asymmetric faults. A dynamic reconfigurable voltage reference unit (DVRU) is designed according to the real faults. In this manner, the other DERs could comply with this DVRU to achieve both system voltage support, inrush current restraining, and power oscillation suppression. It is action signals instead of phase signals need to be transferred by the communication bus with the proposed local fault current differential time delay method (FCDDM). Thus, the low-bandwidth and the enhanced robust communication could be met under transient state. Additionally, the feasibility of the proposed method to combined with relay protection is analyzed. Finally, case study results on islanded MGs consisting of various grid-forming and grid-following DERs demonstrate the effectiveness and robustness of the proposed method under short circuit faults as well as communication failures.

Modeling the Transmission Dynamics and Optimal Control Strategy for Huanglongbing

Huanglongbing (HLB), also known as citrus greening disease, represents a severe and imminent threat to the global citrus industry. With no complete cure currently available, effective control strategies are crucial. This article presents a transmission model of HLB, both with and without nutrient injection, to explore methods for controlling disease spread. By calculating the basic reproduction number (R0) and analyzing threshold dynamics, we demonstrate that the system remains globally stable when R0<1, but persists when R0>1. Sensitivity analyses reveal factors that significantly impact HLB spread on both global and local scales. We also propose a comprehensive optimal control model using the pontryagin minimum principle and validate its feasibility through numerical simulations. Results show that while removing infected trees and spraying insecticides can significantly reduce disease spread, a combination of measures, including the production of disease-free budwood and nursery trees, nutrient solution injection, removal of infected trees, and insecticide application, provides superior control and meets the desired control targets. These findings offer valuable insights for policymakers in understanding and managing HLB outbreaks.

Neural learning control for sampled‐data nonlinear systems based on Euler approximation and first‐order filter

AbstractThe primary focus of this research paper is to explore the realm of dynamic learning in sampled‐data strict‐feedback nonlinear systems (SFNSs) by leveraging the capabilities of radial basis function (RBF) neural networks (NNs) under the framework of adaptive control. First, the exact discrete‐time model of the continuous‐time system is expressed as an Euler strict‐feedback model with a sampling approximation error. We provide the consistency condition that establishes the relationship between the exact model and the Euler model with meticulous detail. Meanwhile, a novel lemma is derived to show the stability condition of a digital first‐order filter. To address the non‐causality issues of SFNSs with sampling approximation error and the input data dimension explosion of NNs, the auxiliary digital first‐order filter and backstepping technology are combined to propose an adaptive neural dynamic surface control (ANDSC) scheme. Such a scheme avoids the ‐step time delays associated with the existing NN updating laws derived by the common ‐step predictor technology. A rigorous recursion method is employed to provide a comprehensive verification of the stability, guaranteeing its overall performance and dependability. Following that, the NN weight error systems are systematically decomposed into a sequence of linear time‐varying subsystems, allowing for a more detailed analysis and understanding. In order to ensure the recurrent nature of the input variables, a recursive design is employed, thereby satisfying the partial persistent excitation condition specifically designed for the RBF NNs. Meanwhile, it can verify that the NN estimated weights converge to their ideal values. Compared with the common ‐step predictor technology, there is no need to redesign the learning rules due to the designed NN weight updating laws without time delays. Subsequently, after capturing and storing the convergence weights, a novel neural learning dynamic surface control (NLDSC) scheme is specifically formulated by leveraging the acquired knowledge. The introduced methodology reduces computational complexity and facilitates practical implementation. Finally, empirical evidence obtained from simulation experiments validates the efficacy and viability of the proposed methodology.

Dynamic event-triggered neuro-optimal control for uncertain nonlinear systems with unknown dead-zone constraint

In this article, we propose a dynamic event-triggered neuro-optimal control scheme (DETNOC) for uncertain nonlinear systems subject to unknown dead-zone and disturbances through the design of a composite control law. An integral sliding mode-based discontinuous control law is utilized to compensate for the effects of unknown dead-zone, disturbance, and a component of uncertainties. As a result, a system dynamics that evolves free of these effects during the sliding mode is obtained. Then, an adaptive dynamic programming-based dynamic event-triggered optimal control law is designed to stabilize the sliding mode dynamics with the help of critic-only neural network architecture. Finally, stability analysis of the closed-loop system is provided and the effectiveness of the developed DETNOC scheme is verified.

An adaptive framework for trajectory following in changing-contact robot manipulation tasks

We describe an adaptive control framework for changing-contact robot manipulation tasks that require the robot to make and break contacts with objects and surfaces. The piecewise continuous interaction dynamics of such tasks make it difficult to construct and use a single dynamics model or control strategy. Also, the nonlinear dynamics during contact changes can damage the robot or the domain objects. Our framework enables the robot to incrementally improve its prediction of contact changes in such tasks, efficiently learn models for the piecewise continuous interaction dynamics, and to provide smooth and accurate trajectory tracking based on a task-space variable impedance controller. We experimentally compare the performance of our framework against that of representative control methods to establish that the adaptive control, prediction, and incremental learning capabilities of our framework are essential to achieve the desired smooth control of changing-contact robot manipulation tasks.

Disclosing effects of Boolean network reduction on dynamical properties and control strategies

Disclosing effects of Boolean network reduction on dynamical properties and control strategies

Dynamic event-driven optimal consensus control for state-constrained multiagent zero-sum differential graphical games

In this paper, a dynamic event-driven optimal control scheme is proposed for the zero-sum differential graphical games in nonlinear multiagent systems with full-state constraints. Initially, to address the dual demands of optimality and state constraints, a set of system transformation functions are introduced to satisfy the state constraints of the agents. Then, by applying the principle of differential game theory, the distributed optimal control problem affected by external disturbances is formulated as a zero-sum differential graphical game, and the performance index function related to neighbor informations and disturbances is designed for each follower. Afterwards, to enhance the utilization of communication resource, a novel dynamic event-triggered mechanism characterized by a dynamic threshold parameter and an auxiliary dynamic variable is developed, which not only exhibits greater flexibility but also diminishes the frequency of triggers. Furthermore, the approximate optimal control strategies are obtained by employing an event-driven adaptive dynamic programming algorithm. Ultimately, a simulation example is presented to verify the applicability of the proposed control approach.

Cell factories for biosynthesis of D-glucaric acid: a fusion of static and dynamic strategies.

D-glucaric acid is an important organic acid with numerous applications in therapy, food, and materials, contributing significantly to its substantial market value. The biosynthesis of D-glucaric acid (GA) from renewable sources such as glucose has garnered significant attention due to its potential for sustainable and cost-effective production. This review summarizes the current understanding of the cell factories for GA production in different chassis strains, from static to dynamic control strategies for regulating their metabolic networks. We highlight recent advances in the optimization of D-glucaric acid biosynthesis, including metabolic dynamic control, alternative feedstocks, metabolic compartments, and so on. Additionally, we compare the differences between different chassis strains and discuss the challenges that each chassis strain must overcome to achieve highly efficient GA productions. In this review, the processes of engineering a desirable cell factory for highly efficient GA production are just like an epitome of metabolic engineering of strains for chemical biosynthesis, inferring general trends for industrial chassis strain developments.

A Flexible Electrochromic Device for All-Season Thermal Regulation on Curved Transparent Building Envelopes.

As one of the least energy-efficient components in buildings, transparent building envelopes are responsible for approximately 60% of the total energy losses. Although controlling solar transmittance through electrochromic modulation is an effective method for temperature management in these structures, a dynamic control strategy for solar light on curved transparent building envelopes is still lacking. In this study, we introduce a dual-mode flexible electrochromic device based on reversible silver deposition for curved transparent building envelopes. The device operates by reversibly depositing and dissolving silver on a flexible polyethylene terephthalate-indium tin oxide (PET-ITO) substrate, controlled through the application and removal of pulsed voltage. This mechanism enables rapid switching between radiative cooling and solar heating modes, leading to modulation of solar reflectance from 89.1% to 15.7% and solar transmittance from 0.02% to 72.9%. Under approximately 700 W/m2 of solar irradiance, the device achieves an average temperature reduction of 1.6 °C (with a maximum reduction of 4.3 °C) compared to ambient temperature in radiative cooling mode. In solar heating mode, the device achieves an average temperature increase of 17.1 °C (with a maximum increment of 23.7 °C) compared to ambient temperature. Simulation results show that the dual-mode flexible electrochromic device could offer all-season thermal regulation for curved transparent building envelopes and achieve a maximum of over 50% annual HVAC energy savings.

Dynamic wake steering control for maximizing wind farm power based on a physics-guided neural network dynamic wake model

Wake effect is a significant factor contributing to power loss in wind farms. Studies have shown that wake steering control can mitigate this power loss. Currently, wind farm wake control strategies primarily utilize fixed yaw control due to limitations in the accuracy and efficiency of dynamic wake models. However, fixed yaw control fails to fully exploit the power improvement potential of wake steering control. Therefore, in this study, we first propose a dynamic wake model for wind farms based on the physics-guided neural network (PGNN) approach. This model can predict the dynamic wake flow field within wind farms in real time using instantaneous inflow wind speed and turbine operational states. Then, by employing the PGNN dynamic wake model as the predictive model, a wind farm dynamic wake control strategy based on the model predictive control method is proposed. To quantify the advantages of the proposed control strategy, both fixed yaw control and dynamic yaw control are tested on a wind farm with a 3 × 2 layout. Results from large eddy simulations demonstrate that the proposed dynamic wake control strategy increases the power output of the wind farm by 11.51% compared to a 6.56% increase achieved with fixed yaw control.

Parameters identification and contact interaction control of redundant robot based on dynamic model

Human-redundant robot contact interaction control based on joint current is conducted. This is a sensorless control method. The key issues involved are dynamic parameters identification and interaction control strategy of robot. Firstly, kinematics model and linear dynamic model of robot are established and dynamic parameter matrix to be identified is obtained. Pattern search method is adopted to optimize motion trajectory of robot. Dynamic parameters is obtained by using least square method. The results indicate that the calculated current has a consistent trend with actual measured value, and the current error is small. In order to achieve independent control in null space, motion decoupling model of robot is established, and impedance control is applied to null space. When the force is applied to robot, based on impedance control and joint current changes, robot can move along the force direction, so the motion in null space can be controlled. Aiming at null space contact control during end force control task, comprehensive interaction control based on force sensor and joint current is conducted. The results show that robot can also move in compliance with human hand and achieve compliant interaction of null space when force is applied to the joint space.

Theoretical and experimental investigations on a data-driven trajectory planning scheme for dynamic shape control of piezo-actuated compliant morphing structures

Piezo-actuated compliant morphing structures can transmit motion and force via elastic deformation with lower weight, simplified design, and higher accuracy, a typical example is the variable camber wing. However, the rapid shape change of compliant structures is a challenging control problem because it often results in a high level of vibration due to the structural flexibility necessary to achieve the morphing requirement. This study presents a model-free data-driven trajectory planning approach based on spline curves and surrogate-based optimization (SBO) to obtain smooth “rest-to-rest” morphing trajectories. The macro-fiber composite (MFC) patches are used for piezoelectric actuators to generate elastic deformation. The flexible beam and variable camber wing are used for both linear numerical simulation and experimental tests with various nonlinearities and uncertainties. An optimization criterion is proposed to minimize both transient and residual vibrations during the “rest-to-rest” motion. An extraordinarily terminal displacement error function is added to eliminate the effects of the hysteresis and creep of the actuator. The control inputs, i.e., voltage profiles for the MFCs, are defined using cubic spline curves via only a few control points. Then, the optimization problem is solved by employing a Kriging surrogate model-based algorithm integrated with the expected improvement (EI) infilling-sampling criterion, which constructs an efficient algorithm similar to reinforcement learning. The optimal morphing trajectories can be highly efficiently obtained by using only 4 control points and less than 150 samplings. The experimental results of both the plate model and the variable camber wing model show that the proposed method can not only achieve a smooth dynamic morphing trajectory with minimized vibration but also automatically compensate for hysteresis and creep. The proposed method provides an effective means for the rapid realization of an optimized morphing trajectory for compliant morphing structures.

Wearable accelerometers reveal objective assessment of walking symmetry and regularity in idiopathic scoliosis patients.

Scoliosis is a multifaceted three-dimensional deformity that significantly affects patients' balance function and walking process. While existing research primarily focuses on spatial and temporal parameters of walking and trunk/pelvic kinematics asymmetry, there remains controversy regarding the symmetry and regularity of bilateral lower limb gait. This study aims to investigate the symmetry and regularity of bilateral lower limb gait and examine the balance control strategy of the head during walking in patients with idiopathic scoliosis. The study involved 17 patients with idiopathic scoliosis of Lenke 1 and Lenke 5 classifications, along with 17 healthy subjects for comparison. Three-dimensional accelerometers were attached to the head and L5 spinous process of each participant, and three-dimensional motion acceleration signals were collected during a 10-meter walking test. Analysis of the collected acceleration signals involved calculating five variables related to the symmetry and regularity of walking: root mean square (RMS) of the acceleration signal, harmonic ratio (HR), step regularity, stride regularity, and gait symmetry. Our analysis reveals that, during the walking process, the three-dimensional motion acceleration signals acquired from the lumbar region of patients diagnosed with idiopathic scoliosis exhibit noteworthy disparities in the RMS of the vertical axis (RMS-VT) and the HR of the vertical axis (HR-VT) when compared to the corresponding values in the healthy control (RMS-VT: 1.6±0.41 vs. 3±0.47, P<0.05; HR-VT: 3±0.72 vs. 3.9±0.71, P<0.05). Additionally, the motion acceleration signals of the head in three-dimensional space, including the RMS in the anterior-posterior and vertical axis, the HR-VT, and the values of step regularity in both anterior-posterior and vertical axis, as well as the values of stride regularity in all three axes, are all significantly lower than those in the healthy control group (P<0.05). The findings of the analysis suggest that the application of three-dimensional accelerometer sensors proves efficacious and convenient for scrutinizing the symmetry and regularity of walking in individuals with idiopathic scoliosis. Distinctive irregularities in gait symmetry and regularity manifest in patients with idiopathic scoliosis, particularly within the antero-posterior and vertical direction. Moreover, the dynamic balance control strategy of the head in three-dimensional space among patients with idiopathic scoliosis exhibits a relatively conservative nature when compared to healthy individuals.

Two channels event‐triggered robust H‐infinity control of Luire industrial cyber physical systems under data injection attacks

AbstractBy fully taking the effects of data injection attacks, nonlinear disturbances and limited communication resources, the stability of Lurie industrial cyber physical systems with the time‐varying delays is investigated. The two channels event‐triggered dynamic robust control (ERHIC) strategy is proposed for achieving the stability of systems. On one hand, a dynamic output feedback controller is designed to ensure the stable operation of the system under attacks. On other hand, the measurement channel and the control channel (two channels) event‐triggered mechanisms are designed to improve the utilization rate of system resources. The simulation results with the size of capacitor voltage and inductor current under data injection attacks as the object verity the correctness and the effectiveness of the proposed two channels event‐triggered robust H‐infinity control method and the feasibility of the designed dynamic output feedback controller are verified by the simulation results.

Edge-based event-triggered consensus control of multi-agent systems against DoS attacks

This article focuses on the security consensus control for general linear multi-agent systems against denial of service (DoS) attacks. In particular, an edge-based event-triggered protocol is proposed to reduce unnecessary information transmission through energy-limited and fragile networks, where the dynamic event-triggered condition is designed by introducing internal dynamic variables. The state of agents is dynamically estimated, and the control inputs based on estimated value can offer better control performance. It is demonstrated that under DoS attacks, the security consensus of multi-agent systems can be realised without Zeno behaviour by adopting the dynamic edge-based event-triggered control strategy. Finally, a numerical simulation example is included to prove the effectiveness of the proposed method.

Dynamic pH regulation drives Nitrosomonas for high-rate stable acidic partial nitritation

How to intensify the ammonia oxidation rate (AOR) is still a bottleneck impeding the technology development for the innovative acidic partial nitritation because the eosinophilic ammonia-oxidizing bacteria (AOB), such as Nitrosoglobus or Nitrosospira, were inhibited by the high-level free nitrous acid (FNA) accumulation in acidic environments. In this study, an innovative approach of dynamic acidic pH regulation control strategy was proposed to realize high-rate acidic partial nitritation driven by common AOB genus Nitrosomonas. The acidic partial nitrification process was carried out in a laboratory-scale sequencing batch moving bed biofilm reactor (SBMBBR) for long-term (700 days) to track the effect of dynamic acidic pH on nitrifying bacterial activity. The results indicated that the influent NH4+-N concentration was about 100 mg/L, the nitrite accumulation ratio was exceeding 90%, and the maximum AOR can reach 14.5 ± 2.6 mg N L−1h−1. Although the half-saturation inhibition constant of NOB (KI_FNA(AOB)) reached 0.37 ± 0.10 mg HNO2N/L and showed extreme adaptability in FNA, the inactivation effect of FNA (6.1 mg HNO2N/L) for NOB was much greater than that of AOB, with inactivation rates of 0.61 ± 0.08 h−1 and 0.06 ± 0.01 h−1, respectively. The effluent pH was gradually reduced to 4.5 by ammonia oxidation process and the periodic FNA concentration reached 6.5 mg HNO2N/L to inactivate nitrite-oxidizing bacteria (NOB) without negatively affecting Nitrosomonas during long-term operation. This result provides new insights for the future implementation of high-rate stabilized acidic partial nitritation by Nitrosomonas.

Assembly/disassembly control of gold nanorods with uniform orientation on anionic polymer brush substrates

Abstract Sophisticated control of the spatial arrangement of gold nanorods provides significant advantages in the design of plasmonic systems. However, dynamic modulation of the gold nanorod spatial arrangements remains challenging. Here, we present a novel strategy for dynamic control of thermo-responsive gold nanorods with uniform alignment on a solid substrate using polymer brushes. In this system, cationic and thermo-responsive gold nanorods were immobilized into anionic polymer brushes via moderate electrostatic interactions, providing vertically aligned gold nanorod arrays. Upon heating, the gold nanorods were assembled while maintaining their vertical orientation within the polymer brushes. They returned to the original state upon cooling, indicating reversible assembly/disassembly. It is noticeable that this system exhibits rapid changes in nanostructure arrangement even when immobilized in the polymer brush substrate on a solid substrate rather than those dispersed in solution. Importantly, the gold nanorods showed good adhesion stability in polymer brushes without any significant detachment during washing and thermal cycling processes but performed assembly formation even at largely separated conditions, indicating the traveling of considerable distances similar to the lateral diffusion of membrane proteins in cell membranes. In addition to providing unprecedented control over gold nanorod spatial configurations, our approach introduces a versatile platform for developing advanced plasmonic devices.

Cascade control system design for a space nuclear reactor

Space nuclear reactors with high power density and long operational life are suitable for deep space exploration missions. The design and dynamics of space nuclear reactors are different from those of conventional reactors. Thus, it is necessary to develop a space reactor control system design and validation simulation system to study its dynamic characteristics and control strategy. In this study, a simulation system of the space reactor was established, and steady-state and dynamic calculations were performed to obtain the operating characteristics of the space reactor. Based on the operating characteristics of the space reactor, the control strategy of the space reactor was proposed, and the cascade control strategy of electric power was designed. The control performance was tested under typical operating conditions. The correctness and effectiveness of the control method was verified, and the proposed method provided a reference for space nuclear reactor control studies.

Auditable and dynamic access control scheme with behavior and identity tracing

Ciphertext policy attribute-based encryption (CP-ABE) is a potential solution to security sharing issues since it offers one-to-many encryption and fine-grained access control. To prevent users from disclosing their access permissions, traceable CP-ABE schemes are emerging. However, the current traceable CP-ABE schemes still have some security problems, such as impromptu attribute retracting, unreliable tracking, and easy disclosure of access policy privacy, making it essential to implement dynamic data security sharing and malicious user auditing. To address these issues, we propose an auditable and dynamic access control scheme with behavior and identity tracing. The scheme first uses an access tree with hidden leaf nodes to express the access policy, improving the flexibility and security of access control. Secondly, the Blockchain and InterPlanetary File System (IPFS) frameworks perform the audit authentication and store the ciphertext, policy, and public keys to avoid tampering with shared data. Furthermore, to prevent users from leaking their decryption keys, a tracing algorithm based on white-box traceability is developed. On this basis, an authority update algorithm is proposed to revoke the malicious user’s permission immediately and update the decrypted key for unrevoked users. An auditable smart contract based on blockchain is proposed to prevent user denial. Finally, theoretical analyses prove that the proposed scheme not only achieves traceability, revocation, authority updating, policy hiding, and audit, but also guarantees security under a chosen plaintext attack model. Experiments demonstrate that our proposal reduces the key generation time by 22.6%, the encryption and decryption time by 39.2% and 59.8%, and the storage costs of ciphertext and private key are reduced by 12.1% and 5.6% over existing schemes.