Feedback Control Research Articles

Designing a switching law for Mittag-Leffler stability in nonlinear singular fractional-order systems and its applications in synchronization

In this work, the stability and synchronization issue of switched singular continuous-time fractional-order systems with nonlinear perturbation are examined. Using the fixed-point principle and S-procedure lemma, a sufficient condition for the existence and uniqueness of the solution to the switched singular fractional-order system is first stated. Next, using the Lyapunov functional method in combination with some techniques related to singular systems and fractional calculus, a switching rule for regularity, impulse-free, and Mittag-Leffler stability is developed based on the formation of a partition of the stability state regions in convex cones. For synchronizing switched fractional singular dynamical systems, we propose a state feedback controller that ensures regularity, impulse-free, and Mittag-Leffler stable in the error closed-loop system. Finally, the ease of use and computational convenience of our proposed methods are illustrated by two numerical examples and a practical example about DC motor controlling an inverted pendulum accompanied by simulation results.

Stabilization of nonhomogenous semi-Markov jump linear systems with synchronous switching delays via aperiodically intermittent control

A type of nonhomogenous semi-Markov jump linear systems with synchronous switching delays is considered in the present paper. Owing to the existence of time-varying delays and nonhomogenous semi-Markov switching process, the selection of Lyapunov functional and the transformation of some time-varying parameters become more complicated in comparison with many existing works. To overcome these difficulties, a sort of Lyapunov–Krasovskii functional related to switching delays is introduced. Simultaneously, the technique of convex combination is applied to tackle a variety of time-varying terms including transition probabilities (TPs), probability distribution function and probability density function (PDF). Then, a time-invariant stability criterion superior to many relevant works in conservativeness is obtained to ensure mean-square exponential stability (MES). Based on this result, time-invariant conditions for mean square exponential stability of the controlled system are also attained by the continuous feedback control. Subsequently, to stand on the point of cost reduction, an aperiodically intermittent control (AIC) is adopted in such a system and a stability criterion is attained as well under more relaxed restrictions on relative parameters. Finally, a numerical example is provided to verify the effectiveness and correctness of the results.

Cluster lag synchronization in fractional-order community networks with nondelay and delay coupling

Lag synchronization is a special synchronization manner and has wide applications. For community network, in view of the differences among communities, the synchronization lags in different communities may be nonidentical. This paper investigates the cluster lag synchronization in fractional-order community networks with nondelay and delay coupling. The node dynamics in different communities are nonidentical and even have nonidentical dimensions. Based on the Lyapunov stability theory, effective feedback controllers are designed for achieving cluster lag synchronization and several synchronization criteria are derived. Finally, numerical simulations are provided to demonstrate the effectiveness of the theoretical results.

Human-machine Voice and Human-machine Competition: Practical Landscape, Pattern Changes and Industrial Challenges of Generative AI-Enabled Audiobook Publishing

At present, generative artificial intelligence has a profound impact on the audiobook publishing industry chain, and exerts unprecedented transformative power in all aspects of audiobook publishing. This paper reviews the specific technical forms of generative artificial intelligence used in the process of audiobook publishing, including text-to-speech, speech recognition, intelligent speech interaction, voiceprint recognition, voice anonymization, etc., and shows the practical picture of how these technologies influence audiobook publishing in different stages of content creation, sound presentation, recording and editing, quality control, terminal playback and readers feedback. It analyzes the pattern changes of the audiobook publishing industry brought about by generative artificial intelligence, with "human-machine voice" as the core, and then discusses the challenges and reflections faced by the audiobook publishing industry under the trend of "human-machine competition and cooperation" from the aspects of underlying logic, industry pain points, fundamental principle, reading environment and technical ethics. Therefore, generative artificial intelligence technology is like a double-edged sword, audiobook publishers should fully understand its nature, conform to the trend of technology, and use it to enable new quality productive forces of audiobook publishing industry.

Design and Research of Intelligent Valve Control

The design and research of intelligent valve control system aims to improve the efficiency and accuracy of fluid control in industrial process automation. By integrating advanced sensing technology, communication protocols and intelligent algorithms, the precise control of valve position, pressure and flow rate is achieved. STM32 controller and a variety of sensors are used in the system to realize real-time monitoring and feedback control of valve status. The control strategy based on PID algorithm enables the valve to maintain stable operating performance under complex working conditions. In addition, the safety and reliability of the system are also considered, and the robustness of the system is improved by redundant design and fault detection mechanism. The experimental results show that the designed intelligent valve control system has significant advantages in improving response speed, control precision and energy efficiency, and has a wide range of industrial application prospects.

A Novel Method for a Pursuit–Evasion Game Based on Fuzzy Q-Learning and Model-Predictive Control

This paper explores a pursuit–evasion game (PEG) based on quadrotors by combining fuzzy Q-learning (FQL) and model-predictive control (MPC) algorithms. Initially, the FQL algorithm is employed to perceive, make decisions, and predict the trajectory of the evader. Based on the position and velocity information of both players in the game, the pursuer quadrotor determines its action strategy using the FQL algorithm. Subsequently, a state feedback controller is designed using the MPC algorithm, with reference inputs derived from the FQL algorithm. Within each MPC cycle, the FQL algorithm dynamically provides reference inputs to the MPC, thereby enhancing its robust control and dynamic optimization for the quadrotor. Finally, simulation results verify the effectiveness of the proposed algorithm.

Robust static output feedback stabilization of stochastic discrete-time systems using stochastic geometric dissipativity

This paper delves into the realm of geometrically mean square stable in probability (GMSSiP) for stochastic discrete-time systems (SDTS). To achieve this, we construct a stochastic geometric QSR-dissipativity (SG-QSR-D) inequalities and devise a linear static output feedback (SOF) controller. This framework is instrumental in establishing GMSSiP and asymptotical stability in probability. Additionally, we establish sufficient and necessity conditions for GMSSiP of the SDTS by using SG-QSR-D. Expanding on this foundation, a linear SOF controller is developed by establishing connections between GMSSiP and SG-QSR-D. Specifically, we demonstrate that the asymptotical stability in probability of the closed-loop linear time-invariant (LTI) SDTS is ensured through a linear matrix inequality (LMI) condition and a linear SOF controller. Two illustrative examples are given to show the effectiveness of the obtained results.

Memristor-coupled cubic hyperchaotic system, feedback synchronization and its application in image encryption

Discrete memristive chaotic systems play a crucial role in information security, and chaotic synchronization form the bedrock of secure and confidential communication. To enhance the chaotic complexity of the Cubic map, we introduce a memristor-coupled Cubic hyperchaotic system by integrating a discrete absolute memristor model with the Cubic map. The dynamics are thoroughly investigated through attractor phase diagrams, bifurcation diagrams, SE complexity analysis, and diagrams illustrating the distribution of dynamic behaviors. Furthermore, we establish methods for chaos control and nonlinear feedback synchronization for the memristive Cubic map. Leveraging this feedback synchronization, we achieve encrypted transmission of digital images. Simulation results validate the feasibility and robust security of the proposed encrypted transmission scheme.

Disturbance observer-based nonsingular fixed-time fuzzy adaptive event-triggered output feedback control of uncertain nonlinear systems

This article examines the fuzzy adaptive event-triggered output feedback control issue of eliminating fixed-time singularity for a type of uncertain nonlinear systems with disturbance observer structures. By introducing novel bounded time-varying functions (TVFs), a set of more general intermediate-variable-based disturbance observers (IVBDOs) is constructed. Combining the corresponding auxiliary reduced-power function and the quartic Lyapunov method, a co-design scheme for a fuzzy approximation high-order controller and an improved event-triggered mechanism with a judgment indicator threshold is proposed. Especially, the developed framework not only removes the singularity during the fixed-time design process but also further reduces the communication resources. By means of theoretical analysis, the devised solution achieves fixed-time stable control (FTSC) of the system and the tracking error converges to the desired neighborhood around the origin. After verifying the absence of Zeno behavior, the efficacy of the presented methodology is supported by two simulation examples.

An MTCA-based LLRF prototype system of LINAC in Hefei Advanced light facility

The linear accelerator (LINAC) system currently under construction at the Hefei Advanced Light Facility (HALF) employs acceleration tubes to accelerate the electron beam to 2.2 GeV. The RF field within the acceleration tube is controlled by an MTCA.4-based low-level radio frequency (LLRF) prototype system to meet stringent phase stability requirements. This paper introduces the structure of the LLRF system, encompassing both its hardware and software components. The LLRF system uses the non-IQ-sampling method to suppress odd harmonics through digital filters, achieving a signal-to-noise ratio (SNR) of approximately 73 dB. Given that the power sources of most acceleration tubes operate in a saturated state, the radio-frequency (RF) control algorithm within the software is based on a separate amplitude open-loop and phase close-loop to maintain stable phase control during amplitude disturbances. The design of the RF control logic, including digital filters, the REF phase tracking method, the close-loop feedback controller, and the output pulse mode control algorithm, is detailed. Offline pulse-to-pulse test results demonstrate that it achieves a phase stability of 0.0176° rms with a solid-state amplifier, meeting the facility's requirements.

Observer‐based output feedback control for a rotating beam with a general exosystem

Observer‐based output feedback control for a rotating beam with a general exosystem

Adaptive output feedback time‐varying formation tracking of multi‐agent system with a leader of unknown input

AbstractThis paper researches the time‐varying formation tracking (TVFT) problem of linear multi‐agent systems (MASs). By designing a compensator, the problem of time‐varying formation can be considered as the output regulation problem. Thereby, the distributed output feedback controller combined with an adaptive technique is proposed. With this controller, follower agents achieve the desired time‐varying formation and follow the trajectory of the leader agent. Furthermore, extending the designed controller to the case where the leader agent equips with unknown control input. Using Lyapunov stability theory, it is demonstrated that under proper conditions the given protocol is implementable. Simulation example is presented at the end of the paper to illustrate the effectiveness of designed control mechanism.

Tip and vibration control of space robots using estimated flexible coordinates

This paper provides an extension to previous work on end-effector control of flexible space manipulators. Those works considered the use of a special output called the μ-tip rate for feedback control of desired end-effector trajectories with simultaneous vibration control. Implementation of this special output requires measurement of end-effector position or the use of flexible forward kinematics to determine it. For the latter, one requires measurements of the joint angles and flexible coordinates. The second of these is difficult to measure in space scenarios, so this paper looks at the use of an estimation scheme to approximate it and use it in a task-space control law. Multiple simulations are conducted to investigate the use of these approximated elastic coordinates in robustly controlling a one-link and two-link flexible manipulator with a payload mass. The error between desired and actual trajectory is calculated, and the results are juxtaposed with results from a joint-space feedback scheme. There is an emphasis on comparing the estimated elastic coordinates with the actual simulated coordinates. Using the estimated elastic coordinates to determine the end-effector location via forward kinematics, yielded similar results to when the actual elastic coordinates were used. Overall, the estimation equation used is shown to provide reasonable end-effector tracking results with the end-effector being able to track various types of trajectories.

Optimal feedback stabilization of fractional output in semilinear distributed systems

This study explores the stabilization of state–space fractional derivatives in semilinear distributed systems, using the Riemann–Liouville derivative of order α, where α lies within the interval 0,1. The main objective is to develop effective feedback control strategies that ensure both strong and weak stabilization of fractional outputs. Additionally, we tackle a fractional minimization problem to enhance the system’s performance. A numerical simulation example is provided to demonstrate the practical significance of the proposed stabilization theorems.

Active Disturbance Rejection Control of Hydraulic Quadruped Robots Rotary Joints for Improved Impact Resistance

Hydraulic actuated quadruped robots have bright application prospects and significant research values in unmanned area investigation, disaster rescue and other scenarios, due to the advantages of high payload and high power to weight ratio. Among these fields, inevitable collision of robots may occur when contact with unknown objects, step on empty objects, or collapse, all of which have an impact on the working hydraulic system. To overcome the unknown external disturbances, this paper proposes an active disturbance rejection control (ADRC) strategy of double vane hydraulic rotary actuators for the hip joints of the quadruped robots. Considering the order of the valve-controlled actuator model, a three-stage tracking differentiator, a four-stage extended state observer, and a state error feedback controller are designed relatively, and the extended state observer is adopted to observe and compensate the uncertainty of external load torque of the system. The effectiveness of the ADRC method is verified in simulation environment and a single joint experimental platform. Moreover, the impact experiments of the limb leg unit are carried out after introducing the proposed ADRC strategy into hip joint, the limb leg unit of quadruped robots presents better impact resistance ability.

Optimal quantized feedback control for linear quandratic Gaussian systems with input delay

AbstractThis paper is concerned with the optimal quantized feedback linear quadratic Gaussian (LQG) control problem for a discrete‐time stochastic system with input delay as well as the measurements to be quantized before transmitted to the controller. In this scenario, the system is presented with several choices of quantizers, along with the cost of using each quantizer. The objective is to jointly select the quantizers and synthesize the controller to maintain an optimal balance between control performance and quantization cost. It is shown that this problem can be decoupled into two optimization problems when the innovation signal is quantized instead of state: one for optimal controller synthesis and the other for optimal quantizer selection. More specifically, a necessary and sufficient condition is derived for the optimal control problem based on Pontryagin's maximum principle. On the other hand, the optimal quantizer selection policy is established by dealing with a certain Markov decision process (MDP).

Global output feedback control for uncertain nonlinear systems with input quantisation and sensor gain perturbation

A new quantised control method via output feedback is proposed by establishing two pairs of time-varying coupled matrix inequalities. Compared to relevant quantised control results, our control method has the novelty: (i) the sensor gain perturbation is allowed to be large and nondifferentiable; (ii) the growth constraint of system nonlinearities is relaxed to depend on unmeasured states and x 1 -polynomial function; (iii) a dynamic scaling technique, instead of backstepping design, is introduced to reduce the computational complexity of controller design. In particular, by using a symbolic function (or hyperbolic tangent function), global asymptotic stability is achieved under any hysteresis quantiser.

Robust State Feedback Control with D-Admissible Assurance for Uncertain Discrete Singular Systems

This study addresses the state feedback control associated with D-admissible assurance for discrete singular systems subjected to parameter uncertainties in both the difference term and system matrices. Firstly, a refined analysis criterion of D-admissible assurance is presented, where the distinct form embraces multiple slack matrices and has lessened linear matrix inequalities (LMIs) constraints, which may be beneficial for reducing the conservatism of admissibility analysis. In consequence, by hiring the state feedback control, controller design issues with pole locations, which directly dominate the system performance, are mainly treated. For all the presented criteria can be formulated by the strict LMIs, they are thus suitably solved via current LMI solvers to conduct a state feedback controller with specific poles’ locations of system’s performance requirements. Finally, two numerical examples illustrate that the presented results are efficient and practicable.

Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropout

This paper introduces a model-based approach for training feedback controllers for an autonomous agent operating in a highly nonlinear (albeit deterministic) environment. We desire the trained policy to ensure that the agent satisfies specific task objectives and safety constraints, both expressed in Discrete-Time Signal Temporal Logic (DT-STL). One advantage for reformulation of a task via formal frameworks, like DT-STL, is that it permits quantitative satisfaction semantics. In other words, given a trajectory and a DT-STL formula, we can compute the robustness , which can be interpreted as an approximate signed distance between the trajectory and the set of trajectories satisfying the formula. We utilize feedback control, and we assume a feed forward neural network for learning the feedback controller. We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent's task objectives. This poses a challenge: RNNs are susceptible to vanishing and exploding gradients, and naïve gradient descent-based strategies to solve long-horizon task objectives thus suffer from the same problems. To tackle this challenge, we introduce a novel gradient approximation algorithm based on the idea of dropout or gradient sampling. One of the main contributions is the notion of controller network dropout , where we approximate the NN controller in several time-steps in the task horizon by the control input obtained using the controller in a previous training step. We show that our control synthesis methodology, can be quite helpful for stochastic gradient descent to converge with less numerical issues, enabling scalable backpropagation over long time horizons and trajectories over high dimensional state spaces. We demonstrate the efficacy of our approach on various motion planning applications requiring complex spatio-temporal and sequential tasks ranging over thousands of time-steps.

Optical measurement and feedback control of a ferromagnetic mechanical resonator

The measurement and control of mechanical resonators are critical for cavity magnomechanics, which has emerged as an important frontier for hybrid quantum systems based on magnonics. Traditional microwave-based measurements require handling high-frequency signals and cannot achieve field distribution detection. Here, we demonstrate a method for optically measuring and manipulating a ferromagnetic mechanical resonator. This technique allows for direct observation of the response and field distribution of mechanical oscillations during magnomechanically induced transparency/absorption processes, confirming that the mechanical mode S1,2 is coupled with the magnon. The optical measurement not only serves to validate the magnomechanical coupling theory but also reduces the necessity for high-frequency measurements. Furthermore, we utilize the instantaneously measured results to implement feedback control of the self-oscillating mechanical resonator to overcome the dynamical back-action limit, achieving a threefold enhancement of phonon lasing amplitude. This feedback control lays the foundation for the study of quantum cavity magnomechanics, such as the feedback cooling of a magnomechanical resonator.