Control Of Dynamic System Research Articles

Controlling the dynamic behavior of decentralized cluster through centralized approaches

How to control the dynamic behavior of large-scale artificial active matter is a critical concern in experimental research on soft matter, particularly regarding the emergence of collective behaviors and the formation of group patterns. Centralized systems excel in precise control over individual behavior within a group, ensuring high accuracy and controllability in task execution. Nevertheless, their sensitivity to group size may limit their adaptability to diverse tasks. In contrast, decentralized systems empower individuals with autonomous decision-making, enhancing adaptability and system robustness. Yet, this flexibility comes at the cost of reduced accuracy and efficiency in task execution. In this work, we present a unique method for regulating the centralized dynamic behavior of self-organizing clusters based on environmental interactions. Within this environment-coupled robot system, each robot possesses similar dynamic characteristics, and their internal programs are entirely identical. However, their behaviors can be guided by the centralized control of the environment, facilitating the accomplishment of diverse cluster tasks. This approach aims to balance the accuracy and flexibility of centralized control with the robustness and task adaptability of decentralized control. The proactive regulation of dynamic behavioral characteristics in active matter groups, demonstrated in this work through environmental interactions, holds the potential to introduce a novel technological approach and provide experimental references for studying the dynamic behavior control of large-scale artificial active matter systems.

Dynamic event-triggered control of large-scale nonlinear systems with sensor uncertainty

In this article, the dynamic event-triggered control problem is investigated for a class of large-scale nonlinear systems subject to sensor uncertainty. The unknown nonlinearities involved in each subsystem are assumed to be bounded by time-varying continuous functions multiplied by unknown constants and unmeasured states. To estimate the unmeasured states of each subsystem, a time-varying high-gain observer is constructed. Then, a dynamic event-triggered mechanism is proposed by introducing an internal dynamic variable in triggering function. Combined with the dual-domination approach, a dynamic event-triggered output feedback controller is developed for each subsystem. Subsequently, it is proved the convergence of all states is ensured based on the Lyapunov theory and the Zeno behavior can be avoided. Eventually, an example is presented to demonstrate the effectiveness of the proposed dynamic event-triggered control scheme.

High-speed fluidic processing circuits for dynamic control of haptic and robotic systems.

Fluidic logic circuits simplify system design for soft robotics by eliminating bulky components while enabling operation in a range of hostile environments that are incompatible with electronics but at the expense of limited computational capabilities and response times on the order of seconds. This paper presents a four-terminal fluidic transistor optimized for fast switching times, reduced component count, low unit cost, and high reproducibility to achieve complex fluidic control circuits while maintaining flow rates of liters per minute. A ring oscillator using three fluidic transistors achieves oscillation frequencies up to a kilohertz with full signal propagation, tolerating billions of cycles without failure. Fundamental processor circuits like a full adder and a 3-bit analog-to-digital converter require just seven transistors each. A decode circuit drives a high-resolution soft haptic display with refresh times below the human perception threshold for latency, and an electronics-free control circuit performs closed-loop position control of a pneumatic actuator with disturbance rejection, demonstrating the value across domains.

Controllability of the time-varying fractional dynamical systems with a single delay in control

In this article, we explored the controllability of fractional dynamical systems with a single delay in the control function with the Caputo fractional derivative. It is the first work in which the author studies the controllability of a time-varying fractional dynamical system with a delay in the control function. We develop the necessary and sufficient criteria for the solution representation of controllability of time-varying fractional linear dynamical systems by utilizing the Grammian matrix. We use Schauder’s fixed point theorem to establish sufficient conditions for the controllability of time-varying nonlinear fractional dynamical systems. With the help of successive approximation techniques, numerical examples validate the theoretical results.

Dynamic simulation and control of dual-evaporator compression refrigeration systems

The need for refrigeration cooling at two different sub-ambient temperature levels is an important problem in some processes. A modern grocery store is an extremely important global example in which there is a need for a moderate temperature heat sink (5 °C) to keep food cool but also a need for a significantly colder heat sink (− 20 °C) for frozen food products. If the two temperatures are not too different, a single refrigerant can be used. The compression refrigeration configuration then consists of two compressors operating in parallel, a single condenser, a single refrigerant liquid surge drum and multiple evaporators. Liquid refrigerant at high pressure is split into a large number of parallel streams to feed individual evaporators in the cooling and freezing compartments. This paper studies how this multi-variable interacting dynamic system can be modeled and controlled using simple conventional controllers.

Process control of chemical dynamic system based on multi-strategy mayfly optimization algorithm

Dynamic optimization problems exist widely in chemical industry, and its operational variables change with the evolution of both space and time. Therefore, dynamic optimization problems have important research significance and challenges. To solve this problem, a multi-strategy mayfly optimization algorithm (MMOA) combined with control variable parameterization method(CVP) is proposed in this paper. MMOA introduces three improvements on the basis of the original algorithm, namely, circle chaos crossover strategy, center wandering strategy and boundary correction strategy. The hybrid strategy can better balance the exploration and exploitation ability of the algorithm. Based on MATLAB simulation environment, MMOA was evaluated. The experimental results show that MMOA has excellent performance in solving precision, convergence speed and stability for the benchmark function. For the six classical chemical dynamic optimization problems, MMOA obtained the performance indexes of 0.61071, 0.4776, 0.57486, 0.73768, 0.11861 and 0.13307, respectively. Compared with the data in the previous literature, MMOA can obtain more accurate control trajectory and better performance indicators. It provides an effective way to solve the dynamic optimization problem.

Guest Editorial: Dynamic analysis, control, and situation awareness of power systems with high penetrations of power electronic converters

Guest Editorial: Dynamic analysis, control, and situation awareness of power systems with high penetrations of power electronic converters

Stages and main tasks of the century-long control theory and system identification development. Part IV. Methods and problems of designing robust control systems

The article provides a review of the most important methods and problems in the design of robust discrete control systems. In this case, the main attention was focused on the problems of suppressing limited external disturbances, information about which is presented only in the form of a limitation on their maximum value. The use of invariant ellipsoids is considered as the first mathematical apparatus for describing the characteristics of the influence of external disturbances on the trajectory of motion of dynamic systems. Theorems on the representation of invariant ellipsoids in the form of linear matrix inequalities (LMI) are formulated, which are further used to synthesize discrete state controllers that suppress external disturbances. The solution to a more general problem of robust suppression of limited disturbances based on the use of LMI in the presence of system uncertainties in the parameters of the mathematical model of the control object is considered. The use of H∞-control theory is considered as a second mathematical apparatus for suppressing external l2-limited external disturbances. In this case, the optimality criterion consists in minimizing the maximum ratio of the l2-norm of the vector of output stabilized coordinates to the l2-norm of the vector of input disturbances. The problem is solved by reducing it to the problem of robust control of a discrete dynamic system in space H∞ based on the Two-Ricatti approach. The standard H∞-optimization problem is also considered.

Robust adaptive dynamic surface control of high-order strict feedback systems based on fully actuated system approach

In this study, the authors propose a robust adaptive dynamic surface control method by using the fully actuated system approach for high-order strict-feedback systems (SFSs) with parameter uncertainty and disturbance. Each subsystem of the SFSs is a high-order system with a full actuation structure. In contrast to the traditional first-order state space method, the proposed control method directly treats each high-order subsystem as a whole without transforming it into a first-order system, which is a concise and efficient treatment. By introducing a series of first-order low-pass filters in each step of the design, the high-order derivatives of the virtual control law are obtained, and the complex and multiple derivation operations are transformed into simple algebraic operations. Adaptive control and robust control are combined to address parameter uncertainty and external disturbances in the system. The Lyapunov stability theory is utilised to demonstrate that all signals in the closed-loop system are uniformly ultimately bounded. The system output can effectively track the desired reference signal under specific constraints, and the tracking error can be adjusted by tweaking the parameters to converge to a sufficiently small neighbourhood around zero. Ultimately, the efficiency of the proposed control method is validated through simulations on a flexible joint manipulator system.

Investigation of controllability and stability of fractional dynamical systems with delay in control

The primary objective of this research is to investigate the controllability and Hyers–Ulam stability of fractional dynamical systems represented by ψ-Caputo fractional derivative with delay in control. To establish the necessary and sufficient conditions for assessing the controllability of linear fractional systems, which are notably distinguished by the presence of Mittag-Leffler functions, we employ the positivity of the Grammian matrix. Also, we present sufficient conditions for the controllability requirements for nonlinear fractional systems utilizing the fixed-point technique. The Hyers-Ulam stability technique is used to determine the sufficient condition for the stability of fractional nonlinear systems with delay in control. Numerical instances are provided to enhance comprehension of the theoretical findings.

An echo state network-based feedforward feedback controller for application in dynamic systems control

An echo state network-based feedforward feedback controller for application in dynamic systems control

An echo state network-based feedforward feedback controller for application in dynamic systems control

An echo state network-based feedforward feedback controller for application in dynamic systems control

Security-aware analytical framework : A mathematical model and machine learning for dynamical system control in secure environments

This study presents a Security-Aware Analytical Framework (SAAF) that is meant to make dynamic system control better in safe places. The framework uses a new mathematical model and advanced machine learning methods to make vital systems more resistant to possible security threats. The mathematical model gives a defined picture of how the system works and where its security holes are. This makes it possible to measure risks and come up with proactive control strategies. Using machine learning techniques, the system changes with changing danger scenarios, allowing for identification and reaction to threats in real time. A risk evaluation tool, a dynamic danger prediction model, and an adaptable control system are some of the most important parts of the SAAF. The risk assessment tool checks for weaknesses in the system, and the dynamic threat prediction model uses machine learning to guess when security might be broken. These guesses are used by the adaptive control method to change system settings on the fly, which improves security without lowering working efficiency. The suggested framework works well by being simulated in a number of safe settings. These settings show how it can reduce security risks and make sure that dynamic systems are strong even as threats change. This study helps to improve methods that focus on security for protecting key assets.

Neuro-Evolution of Frequency Controllers in the Power-Electricity System Island

We present a methodology for designing frequency controllers in a power-electricity isolated island that is based on artificial neural networks (ANN). A less commonly employed machine learning approach based on neuro-evolution (NE) is proposed for learning the ANN, demonstrating its efficiency in dynamic system control. The power-electricity island consists of four water turbogenerators, two steam-gas cycle units, one nuclear unit, a distribution network, and consumers. The proposed architecture of neural controllers and the NE-based learning method, using a genetic algorithm, is described. The results are compared with commonly used PI controllers using different criteria functions. The comparison shows that the proposed approach achieves significantly better control performance compared to the conventional approach using PI controllers.

Data-Driven Dynamic Periodic Event-Triggered Control of Cyber-Physical Systems Under Packet Dropouts and DoS Attacks

Data-Driven Dynamic Periodic Event-Triggered Control of Cyber-Physical Systems Under Packet Dropouts and DoS Attacks

Unbalanced Feature Identification of Rotor System Based on Fused Cross‐Correlation Fast Fourier Transform

Rotor system unbalance is one of the most important factors that affects the operating accuracy and stability in aerospace engineering. The extraction and identification of unbalanced features is the premise for the dynamic balance of rotor system. In order to accurately identify the unbalanced vibration feature, based on the fused cross‐correlation fast Fourier transform (FC‐CFFT) method, an unbalanced feature extraction method of the rotor system is proposed. The convolution window weighs the data of the vibration sequence and carries out delay processing. Through spectrum calculation, the phase information of the vibration signal is obtained. And the amplitude information of the vibration feature can be acquired through cross‐correlation calculation. The unbalanced features of the rotor system vibration signals are identified under different working conditions through simulation and experiment. The results show that this method is more accurate than FFT, the cross‐power method, and the sine‐approximation method in extracting different unbalanced vibration features of the rotor system. The extraction accuracy of unbalanced feature phase and amplitude reaches 98.61% and 97.27%, respectively. After the unbalanced feature is extracted, the dynamic balance experiment can be carried out. The unbalanced feature is used for dynamic balance compensation. In this way, the average amplitude decreased by 76.35%. The high accuracy of the FC‐CFFT method for extracting the unbalanced feature has been verified. This research provides a theoretical foundation for the vibration signal processing and dynamic balance control of the rotor system.

Dynamic Event-Sampled Control of Interconnected Nonlinear Systems Using Reinforcement Learning.

We develop a decentralized dynamic event-based control strategy for nonlinear systems subject to matched interconnections. To begin with, we introduce a dynamic event-based sampling mechanism, which relies on the system's states and the variables generated by time-based differential equations. Then, we prove that the decentralized event-based controller for the whole system is composed of all the optimal event-based control policies of nominal subsystems. To derive these optimal event-based control policies, we design a critic-only architecture to solve the related event-based Hamilton-Jacobi-Bellman equations in the reinforcement learning framework. The implementation of such an architecture uses only critic neural networks (NNs) with their weight vectors being updated through the gradient descent method together with concurrent learning. After that, we demonstrate that the asymptotic stability of closed-loop nominal subsystems and the uniformly ultimate boundedness stability of critic NNs' weight estimation errors are guaranteed by using Lyapunov's approach. Finally, we provide simulations of a matched nonlinear-interconnected plant to validate the present theoretical claims.

О некоторых задачах программного управления пучком траекторий. Часть II

The paper considers the problems of macroparameter control in a linear dynamic system. In the authors’ previous paper, such problems in controlling nonlinear systems were investigated. Variations of the considered functionals and necessary optimality conditions were obtained. This paper focuses on the trajectory beam control in the linear case. In this case, the problem of beam control can be reduced to the problem of the individual trajectory control. The developed approach may be of interest in the study of charged particle dynamics in accelerating and focusing structures, as well as in the construction of the velocity field in automatic image processing.

Dynamic analysis and optimal control of co-infection system under different outbreak times of mutant strains

<p>In epidemic prevention efforts, the emergence of new virus strains due to mutations greatly complicates the prediction and management of epidemics. Most of the current mathematical models of infectious diseases assume that the mutant strain and the original strain have the same outbreak time, which is obviously an ideal situation. In order to make the study more practical, we consider the general situation of outbreaks of mutated strains. At the same time, the optimal control strategy under different emergence time of mutant strains was proposed by using the optimal control theory and numerical simulation. This study provides a new theoretical framework for the dual strain competition model with different outbreak times. The final theoretical results and numerical simulation showed that although the emergence time of the mutant did not affect the final trend of the epidemic, it would affect the cost of prevention and control during the control period.</p>

A Novel Composite Observer based Approach for Dynamic Event-Triggered Adaptive Fuzzy Control of Nonlinear Systems Under DoS Attacks

A Novel Composite Observer based Approach for Dynamic Event-Triggered Adaptive Fuzzy Control of Nonlinear Systems Under DoS Attacks