Adaptive sensitivity control for a feedback loop and a consensus model with unknown delay

Due to the limitation of traditional controller circuit integration, the traditional DC motor speed control method has the problem of low control accuracy, which greatly affects the stability of DC motor operation. Therefore, an adaptive control method of DC motor speed based on STC MCU is proposed. Combined with the basic structure and working principle of DC motor, the mathematical model of DC motor is established. Under this model, the running law of motor speed is analyzed, and STC single chip microcomputer is used to optimize and refit the traditional controller. The speed value of DC motor is measured in real time, and the adaptive control value of speed is determined according to the control target of speed, which is used as the input of controller to realize the adaptive control of DC motor speed. Through the control test experiment, the conclusion is drawn: compared with the traditional control method, the speed control error of the design method is reduced by 8.75r/min, and the design method has more advantages in the control accuracy.

In this paper, we develop a Direct Model Reference Adaptive Tracking Controller for linear systems with unknown delays, sometimes known as retarded equations. This controller can also reject bounded disturbances of known wave form but unknown amplitude, e.g. steps or sinusoids. We prove that the controller produces global asymptotic tracking with bounded controller gains when the delay-free open-loop plant is almost strictly positive real (equivalently, is minimum phase and has CB positive definite) and the internal delay terms satisfy a linear matrix inequality. The adaptive controller has no knowledge of the delay. An illustrative example is provided.

It is well known that noise is inevitable in gene regulatory networks due to the low-copy numbers of molecules and local environmental fluctuations. The prediction of noise effects is a key issue in ensuring reliable transmission of information. Interlinked positive and negative feedback loops are essential signal transduction motifs in biological networks. Positive feedback loops are generally believed to induce a switch-like behavior, whereas negative feedback loops are thought to suppress noise effects. Here, by using the signal sensitivity (susceptibility) and noise amplification to quantify noise propagation, we analyze an abstract model of the Myc/E2F/MiR-17-92 network that is composed of a coupling between the E2F/Myc positive feedback loop and the E2F/Myc/miR-17-92 negative feedback loop. The role of the feedback loop on noise effects is found to depend on the dynamic properties of the system. When the system is in monostability or bistability with high protein concentrations, noise is consistently suppressed. However, the negative feedback loop reduces this suppression ability (or improves the noise propagation) and enhances signal sensitivity. In the case of excitability, bistability, or monostability, noise is enhanced at low protein concentrations. The negative feedback loop reduces this noise enhancement as well as the signal sensitivity. In all cases, the positive feedback loop acts contrary to the negative feedback loop. We also found that increasing the time scale of the protein module or decreasing the noise autocorrelation time can enhance noise suppression; however, the systems sensitivity remains unchanged. Taken together, our results suggest that the negative/positive feedback mechanisms in coupled feedback loop dynamically buffer noise effects rather than only suppressing or amplifying the noise.

An output feedback model reference adaptive controller is developed for a class of linear systems with multiple unknown time-varying state delays and in the presence of actuator failures. The adaptive controller is designed based on SPR-Lyapunov approach and is robust with respect to multiple unknown time-varying plant delays and to an external disturbance with unknown bounds. Closed-loop system stability and asymptotic output tracking are proved using suitable Lyapunov-Krasovskii functional and Simulation results are provided to demonstrate the effectiveness of the proposed controller.

The electro-hydraulic proportional position servo system is a kind of highly non-linear and time-varying system as well as parameter uncertainties. It is often difficult to achieve satisfactory control results with the traditional control methods. Considering that the adaptive neural network PID control method has the characteristics of online learning and adjustment PID control parameters adaptively, this paper puts forward to adopt BP-PID control method for the position servo control of electro-hydraulic proportional system. An online adaptive BP-PID controller is designed. The simulation model of the system is established based on Simulink platform. The step response and Sinusoidal tracking characteristics are researched for electro-hydraulic proportional position servo system by using traditional PID and BP-PID control algorithms through simulations and experiments. The results show that BP-PID has a strong ability of online learning and self-tuning PID parameters. Compared with the traditional PID control method, the BP-PID control has strong self-adaptability and robustness, and can significantly improve the step response speed and tracking control precision.

The traditional aircraft controller design is usually based on the off-line aerodynamic model. Due to the deviation of the off-line aerodynamic model, the flight quality is difficult to meet the requirements when the aircraft is flying in the real atmosphere. To solve this problem, this paper proposes a frequency domain identification-based improved adaptive nonlinear dynamic inversion (NDI) control method (FDI-ANDI). In this paper, an online recursive aerodynamic parameter identification method in the frequency domain is first designed, and then an adaptive dynamic inversion control method based on the online aerodynamic parameter identification results is established. Finally, aiming at the problem of the slow response speed of the NDI controller, an improved adaptive dynamic inversion control law is designed by using the method of series lead correction. Compared with the traditional control method, the adaptive dynamic inversion method based on online aerodynamic identification has stronger robustness and a faster response speed in the face of model uncertainty. The final simulation analysis shows that the method has a better control effect than the traditional control method.

Feedback modules, which appear ubiquitously in biological regulations, are often subject to disturbances from the input, leading to fluctuations in the output. Thus, the question becomes how a feedback system can produce a faithful response with a noisy input. We employed multiple time scale analysis, Fluctuation Dissipation Theorem, linear stability, and numerical simulations to investigate a module with one positive feedback loop driven by an external stimulus, and we obtained a critical quantity in noise attenuation, termed as “signed activation time”. We then studied the signed activation time for a system of two positive feedback loops, a system of one positive feedback loop and one negative feedback loop, and six other existing biological models consisting of multiple components along with positive and negative feedback loops. An inverse relationship is found between the noise amplification rate and the signed activation time, defined as the difference between the deactivation and activation time scales of the noise-free system, normalized by the frequency of noises presented in the input. Thus, the combination of fast activation and slow deactivation provides the best noise attenuation, and it can be attained in a single positive feedback loop system. An additional positive feedback loop often leads to a marked decrease in activation time, decrease or slight increase of deactivation time and allows larger kinetic rate variations for slow deactivation and fast activation. On the other hand, a negative feedback loop may increase the activation and deactivation times. The negative relationship between the noise amplification rate and the signed activation time also holds for the six other biological models with multiple components and feedback loops. This principle may be applicable to other feedback systems.

With the widespread application of fiber laser technology in industries, communications, medical fields, and beyond, the demand for controlling the spatial modes of their output beams has been increasingly growing. Traditional mode control methods are constrained by factors such as device power thresholds, system complexity, and cost, making it difficult to meet the requirements for high-power, high-purity, and rapidly switchable multimode regulation. This paper reviews adaptive mode control technology based on photonic lanterns (PLs). By integrating ideas from adaptive optics and photonics, this technology utilizes photonic lanterns to achieve efficient mode evolution from single-mode to multimode fibers. Combined with optimization algorithms, it enables real-time regulation of input phases, thereby producing stable, high-purity target modes or mode superposition fields at the multimode output end. The paper systematically introduces the structural classifications, propagation characteristics, and fabrication processes of photonic lanterns, as well as the mode evolution mechanisms in different types of photonic lanterns. It elaborates in detail on the structural design, algorithm implementation, and experimental validation of the adaptive control system based on photonic lanterns. Furthermore, it explores the application prospects of this technology in areas such as suppressing transverse mode instability, mode-division multiplexing communications, particle manipulation, and high-resolution spectral measurements. The results demonstrate that the all-fiber adaptive mode control system based on photonic lanterns offers advantages such as compact structure, low loss, fast response, and strong scalability.

In order to overcome the problems of low temperature monitoring accuracy and high control error rate existing in traditional cutting temperature control methods, a new adaptive temperature control method for CNC machine tools is proposed in this paper. In order to improve the adaptive control accuracy of cutting temperature of CNC machine tools, a fuzzy monitor of cutting temperature is designed to realize the real-time monitoring of cutting temperature of CNC machine tools. Based on the real-time monitoring results of cutting temperature of CNC machine tools, Gauss heat source model is used to describe the heat flow distribution of cutting heat source. In two-dimensional space, through the heat source density and boundary conditions, the cutting temperature adaptive control function is constructed to complete the cutting temperature adaptive function of CNC machine tool. The experimental results show that, compared with the traditional control method, this method can accurately monitor the cutting temperature of CNC machine tools, and can accurately carry out the adaptive control of cutting temperature, and the maximum control error rate is less than 3%.

Aiming at the attitude control problem of heavy-lift launch vehicle, an adaptive control method based on super-twisting algorithm is proposed in this paper, which avoids the high frequency chattering of traditional adaptive control, and has a simple structure. Firstly, the attitude dynamic model of heavy launch vehicle is described as an attitude control system based on quaternion. Then, an adaptive super-twisting controller is designed for the system, and the stability of the closed-loop system is proved. Finally, the proposed control method is compared with the traditional adaptive control method, and the simulation results show that the proposed algorithm can make the system error converge to zero more quickly, while effectively suppressing chattering.

A wide range of cellular processes require molecular regulatory pathways to convert a graded signal into a discrete response. One prevalent switching mechanism relies on the coexistence of two stable states (bistability) caused by positive feedback regulations. Intriguingly, positive feedback is often supplemented with negative feedback, raising the question of whether and how these two types of feedback can cooperate to control discrete cellular responses. To address this issue, we formulate a canonical model of a protein–protein interaction network and analyze the dynamics of a prototypical two-component circuit. The appropriate combination of negative and positive feedback loops can bring a bistable circuit close to the oscillatory regime. Notably, sharply activated negative feedback can give rise to a bistable regime wherein two stable fixed points coexist and may collide pairwise with two saddle points. This specific type of bistability is found to allow for separate and flexible control of switch-on and switch-off events, for example (i) to combine fast and reversible transitions, (ii) to enable transient switching responses and (iii) to display tunable noise-induced transition rates. Finally, we discuss the relevance of such bistable switching behavior, and the circuit topologies considered, to specific biological processes such as adaptive metabolic responses, stochastic fate decisions and cell-cycle transitions. Taken together, our results suggest an efficient mechanism by which positive and negative feedback loops cooperate to drive the flexible and multifaceted switching behaviors arising in biological systems.

Molecular timekeeping in Neurospora crassa is robust even under severe limitation of carbon sources and the efficient adaptation to changing nutrient availability is dependent on the positive component of the circadian clock.

Interferons (IFN) play a pivotal role in innate immunity, orchestrating a cell-intrinsic anti-pathogenic state and stimulating adaptive immune responses. The complex interplay between the primary response to IFNs and its modulation by positive and negative feedback loops is incompletely understood. Here, we implement the combination of high-resolution gene-expression profiling of nascent RNA with translational inhibition of secondary feedback by cycloheximide. Unexpectedly, this approach revealed a prominent role of negative feedback mechanisms during the immediate (≤60 min) IFNα response. In contrast, a more complex picture involving both negative and positive feedback loops was observed on IFNγ treatment. IFNγ-induced repression of genes associated with regulation of gene expression, cellular development, apoptosis and cell growth resulted from cycloheximide-resistant primary IFNγ signalling. In silico promoter analysis revealed significant overrepresentation of SP1/SP3-binding sites and/or GC-rich stretches. Although signal transducer and activator of transcription 1 (STAT1)-binding sites were not overrepresented, repression was lost in absence of STAT1. Interestingly, basal expression of the majority of these IFNγ-repressed genes was dependent on STAT1 in IFN-naïve fibroblasts. Finally, IFNγ-mediated repression was also found to be evident in primary murine macrophages. IFN-repressed genes include negative regulators of innate and stress response, and their decrease may thus aid the establishment of a signalling perceptive milieu.

High standards and efficiency of deep hole drilling used for hard-to-machine metals and alloys could not be achieved if chips are not segmented in the cutting zone. Chip control could be achieved through transmission of harmonic vibrations to the drill in direction of its rotating axis. One way to maintain these vibrations is to replace a drill chuck with a special self-vibratory drilling head, which includes an elastic element that allows axial movement of the tool. The right stiffness value of an elastic element and appropriate machining conditions could lead to oscillation self-excitation of a drill due to the regenerative mechanism. It is advisable to support this mechanism with a control action defined within the feedback loop, which provides process quality necessary for chip control in the broad range of process parameters. This work analyzes adaptive control algorithm for vibratory drilling process dynamics where control action over an oscillating system is proportional to drill axial vibrational velocity, and a feedback gain is determined in the adaptation loop. Dynamics modeling of a closed-loop nonlinear system “elastic system-machining process-control system” is carried out for cases with or without control proving effectiveness of control algorithm. The model was used to analyze how control system analog-to-digital conversion parameters influence vibratory process quality. Requirements, which ensure control system achieves a control aim, were approximately stated for capacity and response limit for analog-to-digital conversion.

As a specific task for unmanned tracked vehicles, leader-following imposes high-precision requirements on the vehicle’s motion control, especially the steering control. However, due to characteristics such as the frequent changes in off-road terrain and steering resistance coefficients, controlling tracked vehicles poses significant challenges, making it difficult to achieve stable and precise leader-following. This paper decouples the leader-following control into speed and curvature control to address such issues. It utilizes model reference adaptive control to establish reference models for the speed and curvature subsystems and designs corresponding parameter adaptive control laws. This control method enables the actual vehicle speed and curvature to effectively track the response of the reference model, thereby addressing the impact of frequent changes in the steering resistance coefficient. Furthermore, this paper demonstrates significant improvements in leader-following performance through a series of simulations and experiments. Compared with the traditional PID control method, the results shows that the maximum following distance has been reduced by at least approximately 12% (ensuring the ability to keep up with the leader), the braking distance has effectively decreased by 22% (ensuring a safe distance in an emergency braking scenario and improving energy recovery), the curvature tracking accuracy has improved by at least 11% (improving steering performance), and the speed tracking accuracy has increased by at least 3.5% (improving following performance).