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.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Feedback linearization and equivalent-disturbance compensation control strategy for piezoelectric stage

Piezoelectric stages use piezoelectric actuators and flexure hinges as driving and amplifying mechanisms,respectively.These systems have high positioning accuracy and high-frequency responses,and they are widely used in various precision/ultra-precision positioning fields.However,the main challenge with these devices is the inherent hysteresis nonlinearity of piezoelectric actuators,which seriously affects the tracking accuracy of a piezoelectric stage.Inspired by this challenge,in this work,we developed a Hammerstein model to describe the hysteresis nonlinearity of a piezoelectric stage.In particular,in our proposed scheme,a feedback-linearization algorithm is used to eliminate the static hysteresis nonlinearity.In addition,a composite controller based on equivalent-disturbance compensation was designed to counteract model uncertainties and external disturbances.An analysis of the stability of a closed-loop system based on this feedback-linearization algorithm and composite controller was performed,and this was followed by extensive comparative experiments using a piezoelectric stage developed in the laboratory.The experimental results confirmed that the feedback-linearization algorithm and the composite controller offer improved linearization and trajectory-tracking performance.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

Dynamic Constraint-Driven Event-Triggered Control of Strict-Feedback Systems Without Max/Min Values on Irregular Constraints

This work proposes an event-triggered adaptive control approach for a class of uncertain nonlinear systems under irregular constraints.Unlike the constraints considered in most existing papers,here the external irregular constraints are considered and a constraints switching mechanism(CSM)is introduced to circumvent the difficulties arising from irregular output constraints.Based on the CSM,a new class of generalized barrier functions are constructed,which allows the control results to be independent of the maximum and minimum values(MMVs)of constraints and thus extends the existing results.Finally,we proposed a novel dynamic constraint-driven event-triggered strategy(DCDETS),under which the stress on signal transmission is reduced greatly and no constraints are violated by making a dynamic trade-off among system state,external constraints,and inter-execution intervals.It is proved that the system output is driven to close to the reference trajectory and the semi-global stability is guaranteed under the proposed control scheme,regardless of the external irregular output constraints.Simulation also verifies the effectiveness and benefits of the proposed method.

A finite-time fuzzy adaptive output-feedback fault-tolerant control for underactuated wheeled mobile robots systems

This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.

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.

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.

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.

Research on Narrowband Line Spectrum Noise Control Method Based on Nearest Neighbor Filter and BP Neural Network Feedback Mechanism

Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.

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.

Practical prescribed-time tracking control for uncertain strict-feedback systems with guaranteed performance under unknown control directions

In this paper,we consider the practical prescribed-time performance guaranteed tracking control problem for a class of uncertain strict-feedback systems subject to unknown control direction.Due to the existence of unknown nonlinearities and uncertainties,it is challenging to design a controller that can ensure the stability of closed-loop system within a predetermined finite time while maintaining the specified transient performance.The underlying problem becomes further complex as the control directions are unknown.To deal with the above problems,a special translation function as well as Nussbaum type function are introduced in the prescribed performance control(PPC)framework.Finally,a PPC as well as preset finite time tracking control scheme is designed,and its effectiveness is confirmed by both theoretical analysis and numerical simulation.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

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.