A Tutorial on Quantized Feedback Control

In this tutorial paper, we explore the field of quantized feedback control, which has gained significant attention due to the growing prevalence of networked control systems. These systems require the transmission of feedback information, such as measurements and control signals, over digital networks, presenting novel challenges in estimation and control design. Our examination encompasses various topics, including the minimal information needed for effective feedback control, the design of quantizers, strategies for quantized control design and estimation,achieving consensus control with quantized data, and the pursuit of high-precision tracking using quantized measurements.

Performance enhancement of a viscoelastic bistable energy harvester using time-delayed feedback control

This paper focuses on the stochastic analysis of a viscoelastic bistable energy harvesting system under colored noise and harmonic excitation, and adopts the time-delayed feedback control to improve its harvesting efficiency. Firstly, to obtain the dimensionless governing equation of the system, the original bistable system is approximated as a system without viscoelastic term by using the stochastic averaging method of energy envelope, and then is further decoupled to derive an equivalent system. The credibility of the proposed method is validated by contrasting the consistency between the numerical and the analytical results of the equivalent system under different noise conditions. The influence of system parameters on average output power is analyzed, and the control effect of the time-delayed feedback control on system performance is compared. The output performance of the system is improved with the occurrence of stochastic resonance(SR). Therefore, the signal-to-noise ratio expression for measuring SR is derived, and the dependence of its SR behavior on different parameters is explored.

A feedback control method for phase signal demodulation in fber-optic hydrophones

In the realm of acoustic signal detection,the identification of weak signals,particularly in the presence of negative signal-to-noise ratios,poses a significant challenge.This challenge is further heightened when signals are acquired through fiber-optic hydrophones,as these signals often lack physical significance and resist clear systematic modeling.Conventional processing methods,e.g.,low-pass filter(LPF),require a thorough understanding of the effective signal bandwidth for noise reduction,and may introduce undesirable time lags.This paper introduces an innovative feedback control method with dual Kalman filters for the demodulation of phase signals with noises in fiber-optic hydrophones.A mathematical model of the closed-loop system is established to guide the design of the feedback control,aiming to achieve a balance with the input phase signal.The dual Kalman filters are instrumental in mitigating the effects of signal noise,observation noise,and control execution noise,thereby enabling precise estimation for the input phase signals.The effectiveness of this feedback control method is demonstrated through examples,showcasing the restoration of low-noise signals,negative signal-to-noise ratio signals,and multi-frequency signals.This research contributes to the technical advancement of high-performance devices,including fiber-optic hydrophones and phase-locked amplifiers.

A NOTE ON THE GENERAL STABILIZATION OF DISCRETE FEEDBACK CONTROL FOR NON-AUTONOMOUS HYBRID NEUTRAL STOCHASTIC SYSTEMS WITH A DELAY

Discrete feedback control was designed to stabilize an unstable hybrid neutral stochastic differential delay system(HNSDDS) under a highly nonlinear constraint in the H_∞ and exponential forms.Nevertheless,the existing work just adapted to autonomous cases,and the obtained results were mainly on exponential stabilization.In comparison with autonomous cases,non-autonomous systems are of great interest and represent an important challenge.Accordingly,discrete feedback control has here been adjusted with a time factor to stabilize an unstable non-autonomous HNSDDS,in which new Lyapunov-Krasovskii functionals and some novel technologies are adopted.It should be noted,in particular,that the stabilization can be achieved not only in the routine H_∞ and exponential forms,but also the polynomial form and even a general form.

Asynchronous Learning-Based Output Feedback Sliding Mode Control for Semi-Markov Jump Systems: A Descriptor Approach

This paper presents an asynchronous output-feed-back control strategy of semi-Markovian systems via sliding mode-based learning technique.Compared with most literature results that require exact prior knowledge of system state and mode information,an asynchronous output-feedback sliding sur-face is adopted in the case of incompletely available state and non-synchronization phenomenon.The holonomic dynamics of the sliding mode are characterized by a descriptor system in which the switching surface is regarded as the fast subsystem and the system dynamics are viewed as the slow subsystem.Based upon the co-occurrence of two subsystems,the sufficient stochastic admissibility criterion of the holonomic dynamics is derived by utilizing the characteristics of cumulative distribution functions.Furthermore,a recursive learning controller is formulated to guarantee the reachability of the sliding manifold and realize the chattering reduction of the asynchronous switching and sliding motion.Finally,the proposed theoretical method is substantia-ted through two numerical simulations with the practical contin-uous stirred tank reactor and F-404 aircraft engine model,respectively.

Fixed-Time Stabilization of a Class of Strict-Feedback Nonlinear Systems via Dynamic Gain Feedback Control

This paper presents a novel fixed-time stabilization control(FSC)method for a class of strict-feedback nonlinear systems involving unmodelled system dynamics.The key feature of the proposed method is the design of two dynamic parameters.Specifically,a set of auxiliary variables is first introduced through state transformation.These variables combine the original system states and the two introduced dynamic parameters,facilitating the closed-loop system stability analyses.Then,the two dynamic parameters are delicately designed by utilizing the Lyapunov method,ensuring that all the closed-loop system states are globally fixed-time stable.Compared with existing results,the“explosion of complexity”problem of backstepping control is avoided.Moreover,the two designed dynamic parameters are dependent on system states rather than a time-varying function,thus the proposed controller is still valid beyond the given fixedtime convergence instant.The effectiveness of the proposed method is demonstrated through two practical systems.

Feedback control and quantum error correction assisted quantum multi-parameter estimation

Quantum metrology provides a fundamental limit on the precision of multi-parameter estimation,called the Heisenberg limit,which has been achieved in noiseless quantum systems.However,for systems subject to noises,it is hard to achieve this limit since noises are inclined to destroy quantum coherence and entanglement.In this paper,a combined control scheme with feedback and quantum error correction(QEC)is proposed to achieve the Heisenberg limit in the presence of spontaneous emission,where the feedback control is used to protect a stabilizer code space containing an optimal probe state and an additional control is applied to eliminate the measurement incompatibility among three parameters.Although an ancilla system is necessary for the preparation of the optimal probe state,our scheme does not require the ancilla system to be noiseless.In addition,the control scheme in this paper has a low-dimensional code space.For the three components of a magnetic field,it can achieve the highest estimation precision with only a 2-dimensional code space,while at least a4-dimensional code space is required in the common optimal error correction protocols.

Fuzzy Feedback Control for Electro-Hydraulic Actuators

Electro-hydraulic actuators(EHA)have recently played a significant role in modern industrial applications,especially in systems requiring extremely high precision.This can be explained by EHA’s ability to precisely control the position and force through advanced sensors and innovative control algorithms.One of the promising approaches to improve control accuracy for EHA systems is applying classical to modern control algorithms,in which the proportional–inte-gral–derivative(PID)algorithm,fuzzy logic controller,and a hybrid of these methods are popular options.In this paper,we developed a novel version of the fuzzy control algorithm and linear feedback control method,namely fuzzy lin-ear feedback control,to improve the control performance.To achieve the highest performance,wefirst designed a mathematical EHA model based on the Matlab/Simulink software packages thanks to the selected parameters,which are similar to a real EHA system.Then,we respectively applied PID,fuzzy PID(FPID),and fuzzy linear feedback control(FLFC)before comparing them to have a full view of the outstanding advantages of the proposed algorithm.The simulation results showed that the proposed FLFC algorithm is approximately 99%and 77%super-ior in performance to the PID and feedback control algorithms,respectively.

pth-Moment Stabilization of Hybrid Stochastic Differential Equations by Discrete-Time Feedback Control

The given unstable hybrid stochastic differential equation is stabilized in the sense of p th-moment exponential stability.We achieve the results by feedback controls based on the discrete-time state and mode observations.The upper bound on the duration between two consecutive observations is obtained as well.Finally,a numerical example is given to verify the validity of the theoretical conclusions.

Adaptive recurrent neural network for uncertainties estimation in feedback control system

In this paper,a recurrent neural network(RNN)is used to estimate uncertainties and implement feedback control for nonlinear dynamic systems.The neural network approximates the uncertainties related to unmodeled dynamics,parametric variations,and external disturbances.The RNN has a single hidden layer and uses the tracking error and the output as feedback to estimate the disturbance.The RNN weights are online adapted,and the adaptation laws are developed from the stability analysis of the controlled system with the RNN estimation.The used activation function,at the hidden layer,has an expression that simplifies the adaptation laws from the stability analysis.It is found that the adaptive RNN enhances the tracking performance of the feedback controller at the transient and steady state responses.The proposed RNN based feedback control is applied to a DC–DC converter for current regulation.Simulation and experimental results are provided to show its effectiveness.Compared to the feedforward neural network and the conventional feedback control,the RNN based feedback control provides good tracking performance.

Switching Control Method for Optimal State Feedback Controller of Nuclear Reactor Power System

Due to the nonlinearity of the reactor power system, the load tracking situation is closely related to the initial steady-state power and the final steady-state power after the introduction of the state feedback controller. Therefore, when the initial power and the final stable power are determined, the particle swarm optimization algorithm is used to find the optimal controller parameters to minimize the load tracking error. Since there are many combinations of initial stable power and final stable power, it is not possible to find the optimal controller parameters for all combinations, so the neural network is used to take the final stable power and the initial stable power as input, and the optimal controller parameters as the output. This method obtains the optimal state feedback controller switching control method can achieve a very excellent load tracking effect in the case of continuous power change, in the power change time point, the response is fast, in the controller parameter switching time point, the actual power does not fluctuate due to the change of controller parameters. .

Versatile and controlled quantum teleportation network

A quantum teleportation network involving multiple users is essential for future quantum internet.So far,controlled quantum teleportation has been demonstrated in a three-user network.However,versatile and controlled quantum teleportation network involving more users is in demand,which satisfies different combinations of users for practical requirements.Here we propose a highly versatile and controlled teleportation network that can switch among various combinations of different users.We use a single continuous-variable six-partite Greenberger-Horne-Zeilinger(GHZ)state to realize such a task by choosing the different measurement and feedback operations.The controlled teleportation network,which includes one sub-network,two sub-networks and three sub-networks,can be realized for different application of user combinations.Furthermore,the coherent feedback control(CFC)can manipulate and improve the teleportation performance.Our approach is flexible and scalable,and would provide a versatile platform for demonstrations of complex quantum communication and quantum computing protocols.

Towards engineering a portable platform for laparoscopic pre-training in virtual reality with haptic feedback

Background Laparoscopic surgery is a surgical technique in which special instruments are inserted through small incision holes inside the body.For some time,efforts have been made to improve surgical pre training through practical exercises on abstracted and reduced models.Methods The authors strive for a portable,easy to use and cost-effective Virtual Reality-based(VR)laparoscopic pre-training platform and therefore address the question of how such a system has to be designed to achieve the quality of today's gold standard using real tissue specimens.Current VR controllers are limited regarding haptic feedback.Since haptic feedback is necessary or at least beneficial for laparoscopic surgery training,the platform to be developed consists of a newly designed prototype laparoscopic VR controller with haptic feedback,a commercially available head-mounted display,a VR environment for simulating a laparoscopic surgery,and a training concept.Results To take full advantage of benefits such as repeatability and cost-effectiveness of VR-based training,the system shall not require a tissue sample for haptic feedback.It is currently calculated and visually displayed to the user in the VR environment.On the prototype controller,a first axis was provided with perceptible feedback for test purposes.Two of the prototype VR controllers can be combined to simulate a typical both-handed use case,e.g.,laparoscopic suturing.A Unity based VR prototype allows the execution of simple standard pre-trainings.Conclusions The first prototype enables full operation of a virtual laparoscopic instrument in VR.In addition,the simulation can compute simple interaction forces.Major challenges lie in a realistic real-time tissue simulation and calculation of forces for the haptic feedback.Mechanical weaknesses were identified in the first hardware prototype,which will be improved in subsequent versions.All degrees of freedom of the controller are to be provided with haptic feedback.To make forces tangible in the simulation,characteristic values need to be determined using real tissue samples.The system has yet to be validated by cross-comparing real and VR haptics with surgeons.

Feedforward and Feedback Optimal Control for Linear Systems with Sinusoidal Disturbances

The linear systems affected by additive external sinusoidal disturbances is studied. The problem is to damp this forced oscillation in an optimal fashion. The main result of this paper is a new design approach is proposed of realizable feedforward and feedback optimal control law for a linear time invariant system with sinusoidal disturbances. The algorithm of solving the optimal control law is given. It is shown that the control law is easily realized and is robust with respect to errors produced by the external sinusoidal disturbances through simulation results.

Feedback and Feedforward Optimal Control for Offshore Jacket Platforms

The optimal control is investigated for linear systems affected by external harmonic disturbance and applied to vibration control systems of offshore steel jacket platforms. The wave-induced force is the dominant load that offshore structures are subjected to, and it can be taken as harmonic excitation for the system. The linearized Morison equation is employed to estimate the wave loading. The main result concerns the existence and design of a realizable optimal regulator, which is proposed to damp the forced oscillation in an optimal fashion. For demonstration of the effectiveness of the control scheme, the platform performance is investigated for different wave states. The simulations are based on the tuned mass damper and the active mass damper control devices. It is demonstrated that the control scheme is useful in reducing the displacement response of jacket-type offshore platforms.

Controlling and synchronizing spatiotemporal chaos of the coupled Bragg acousto-optical bistable map system using nonlinear feedback

In this paper we present the control and synchronization of a coupled Bragg acousto-optic bistable map system using nonlinear feedback technology. This nonlinear feedback technology is useful to control a temporally chaotic system as well as a spatiotemporally chaotic system. It can be extended to synchronize the spatiotemporal chaos. It can work in a wide range of the controlled and synchronized signals, so it can decrease the sensitivity down to a noise level. The synchronization can be obtained by the analysis of the largest conditional Lyapunov exponent spectrum, and easily implemented in practical systems just by adjusting the coupled strength without any pre-knowledge of the dynamic system required.

Adaptive output feedback control for nonlinear time-delay systems using neural network

This paper extends the adaptive neural network （NN） control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backstepping technique. NNs are used to approximate unknown functions dependent on time delay, Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the NN approximation errors. Based on Lyapunov- Krasovskii functional, the semi-global uniform ultimate boundedness of all the signals in the closed-loop system is proved, The feasibility is investigated by two illustrative simulation examples.

Nonlinear feedback control of a novel hyperchaotic system and its circuit implementation

This paper reports a new hyperchaotic system by adding an additional state variable into a three-dimensional chaotic dynamical system. Some of its basic dynamical properties, such as the hyperchaotic attractor, Lyapunov expo- nents, bifurcation diagram and the hyperchaotic attractor evolving into periodic, quasi-periodic dynamical behaviours by varying parameter k are studied. An effective nonlinear feedback control method is used to suppress hyperchaos to unstable equilibrium. Furthermore, a circuit is designed to realize this new hyperchaotic system by electronic workbench （EWB）. Numerical simulations are presented to show these results.

Resilient Fixed-Order Distributed Dynamic Output Feedback Load Frequency Control Design for Interconnected Multi-Area Power Systems

The paper proposes a novel H∞ load frequency control(LFC) design method for multi-area power systems based on an integral-based non-fragile distributed fixed-order dynamic output feedback(DOF) tracking-regulator control scheme. To this end, we consider a nonlinear interconnected model for multiarea power systems which also include uncertainties and timevarying communication delays. The design procedure is formulated using semi-definite programming and linear matrix inequality(LMI) method. The solution of the proposed LMIs returns necessary parameters for the tracking controllers such that the impact of model uncertainty and load disturbances are minimized. The proposed controllers are capable of receiving all or part of subsystems information, whereas the outputs of each controller are local. These controllers are designed such that the resilient stability of the overall closed-loop system is guaranteed. Simulation results are provided to verify the effectiveness of the proposed scheme. Simulation results quantify that the distributed(and decentralized) controlled system behaves well in presence of large parameter perturbations and random disturbances on the power system.

Nonlinear feedback synchronisation control between fractional-order and integer-order chaotic systems

This paper focuses on the synchronisation between fractional-order and integer-order chaotic systems. Based on Lyapunov stability theory and numerical differentiation, a nonlinear feedback controller is obtained to achieve the synchronisation between fractional-order and integer-order chaotic systems. Numerical simulation results are presented to illustrate the effectiveness of this method.