Adaptive Compensation Technique Research Articles

Adaptive Event-Triggered Fuzzy Control for Stochastic Highly Nonlinear Systems With Time Delay and Nontriangular Structure Interconnections

The problem of adaptive fuzzy control based on the switching event-triggered mechanism for stochastic highly nonlinear system with time-varying delays, actuator faults and non-triangular structure interconnections is investigated in this article. By constructing the corresponding Lyapunov-Krasovskii functional and variable separation method, the complex nonlin- ear functions with time-varying delays and highly nonlinearity can be handled. With the help of the fuzzy logic systems and adaptive compensation technique, the processing difficulty of the unknown nonlinear terms and the parameters of actuator faults is reduced. Simultaneously, the proposed control scheme can greatly decrease the waste of communication resources by a switching event-triggered mechanism. Then, all signals in the system are ensured to be bounded under Lyapunov stability theorem. Ultimately, the simulation example demonstrates the availability of the developed control scheme.

Iterative multi-photon adaptive compensation technique for deep tissue two-photon fluorescence lifetime imaging

Iterative multi-photon adaptive compensation technique for deep tissue two-photon fluorescence lifetime imaging

Adaptive Fuzzy Decentralized Control for Stochastic Nonlinear Interconnected System With Nontriangular Structural Dynamic Uncertainties

This paper constructs an adaptive fuzzy decentralized fault tolerant control strategy for stochastic nonlinear interconnected system with non-triangular structural uncertainties, unmodeled dynamics and actuator faults. During the controller design process, the difficultly of system controller design caused by non-triangular structural uncertainties that mixed unmodeled dynamics are handled by utilizing the variable separation technique and fuzzy logic system (FLS). Then, based on the adaptive compensation technique, a fault tolerant controller is designed to solve the influence of actuator faults for system stability. Finally, the stability of stochastic nonlinear interconnected system can be ensured by stochastic Lyapunov stability theory. In addition, numerical and practical simulations also illustrate the effectiveness of the proposed control strategy.

Fault-tolerant practical tracking control for uncertain robotic system with general reference trajectory and output constraint

This paper is devoted to the fault-tolerant tracking control for a class of uncertain robotic systems under time-varying output constraints. Notably, both actuator fault and the disturbances are present while all the dynamic matrices are not necessarily to be parameterized by unknown parameters or have known nominal parts, and moreover, the reference trajectories as well as the output constraints functions are not necessarily twice continuously differentiable without any time derivatives of them being available for feedback. These remarkable characteristics greatly relax the corresponding assumptions of the related literature and in turn to bring the ineffectiveness of the traditional schemes on this topic. For this, a powerful adaptive control methodology is established by incorporating adaptive dynamic compensation technique into the backstepping framework based on Barrier Lyapunov functions. Then, an adaptive state feedback controller with the smart choices of adaptive law and virtual controls is designed which guarantees that all the states of the closed-loop system are bounded and the system output practically tracks the reference trajectory while not violates the output constraints.

Observer-Based Event-Triggered Composite Anti-Disturbance Control for Multi-Agent Systems Under Multiple Disturbances and Stochastic FDIAs

This article aims to investigate the security consensus and composite anti-disturbance problems for a class of nonlinear multi-agent systems subjected to stochastic false data injection attacks (FDIAs) and multiple disturbances under a directed communication topology. To attenuate and reject of the negative effects of two types of disturbances, a disturbance observer (DO) is designed to counteract the disturbance produced by exogenous system, and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {H}_\infty $ </tex-math></inline-formula> control method is adopted to attenuate the bounded errors and variables caused by the other type of disturbances and FDIAs simultaneously. To ensure the consensus performance of MASs, an observer-based control strategy is designed, and a novel adaptive compensation technique is proposed to not only evaluate the upper bounds of the unknown but bounded disturbances but also improve the accuracy of the state observer. Furthermore, a novel event-triggered mechanism (ETM) without requiring continuous communication among neighboring agents is developed to reduce the controller update frequency and the communication burden. Meanwhile, Zeno behavior is excluded. Finally, numerical simulations are provided to verify the availability of the designed method. Note to Practitioners—In multi-agent systems, network security is very important. For example, in smart power grid systems, it is necessary to use the method of state estimation to observe the system to guarantee its safe operation. However, the measured value of the instrument may be affected by FDIAs in the transmission process, thus changing the result of state estimation and causing misjudgment of the system. Similarly, in multi-vehicle systems, FDIAs may destroy the location information of vehicles and cause serious accidents. In addition, the system will be subjected to different types of disturbances in practice, thus reducing the performance of the system. In view of the threat of FDIAs and disturbances to the MAS, a composite anti-disturbance method and an observer-based control strategy are proposed. Meanwhile, to avoid the limitation of communication bandwidth in reality, a novel ETM is developed to save network resources.

Adaptive Multigradient Recursive Reinforcement Learning Event-Triggered Tracking Control for Multiagent Systems.

This article proposes a fault-tolerant adaptive multigradient recursive reinforcement learning (RL) event-triggered tracking control scheme for strict-feedback discrete-time multiagent systems. The multigradient recursive RL algorithm is used to avoid the local optimal problem that may exist in the gradient descent scheme. Different from the existing event-triggered control results, a new lemma about the relative threshold event-triggered control strategy is proposed to handle the compensation error, which can improve the utilization of communication resources and weaken the negative impact on tracking accuracy and closed-loop system stability. To overcome the difficulty caused by sensor fault, a distributed control method is introduced by adopting the adaptive compensation technique, which can effectively decrease the number of online estimation parameters. Furthermore, by using the multigradient recursive RL algorithm with less learning parameters, the online estimation time can be effectively reduced. The stability of closed-loop multiagent systems is proved by using the Lyapunov stability theorem, and it is verified that all signals are semiglobally uniformly ultimately bounded. Finally, two simulation examples are given to show the availability of the presented control scheme.

Actuator failure compensation-based command filtered control of electro-hydraulic system with position constraint

In this article, the design and experimental evaluation of a fault-tolerant controller are introduced for a double-rod electro-hydraulic actuator subjected to actuator faults and disturbances. The internal leakage fault is captured as a bias fault, whilst the faults in servo-valve and supply failure are considered as a partial loss of effectiveness (LOE) fault. The design obstacles caused by the disturbances and bias fault are suppressed by nonlinear disturbance observers (NDO) while an asymmetric barrier Lyapunov function is used to ensure the non-violated boundary of the output position. To tackle the LOE fault, the development of an enhanced adaptive compensation technique for actuator fault-tolerant control (FTC) is then constructed. Moreover, to mitigate the “explosion of complexity” in the traditional backstepping design, the command-filtered control is utilized to elaborate the FTC scheme. It is shown by theoretical analysis that system stability is ensured under faulty conditions. Finally, simulation/experiment results and comparison studies are performed to further verify the effectiveness of the proposed approach.

Real time experimental investigation of adaptive optics compensation technique for free space optical communication

Real time experimental investigation of adaptive optics compensation technique for free space optical communication

Practical tracking control under actuator saturation for a class of flexible-joint robotic manipulators driven by DC motors

This paper is devoted to the practical tracking control for a class of flexible-joint robotic manipulators driven by DC motors. Different from the related literature where control constraint is neglected and the disturbances are excluded or only exist in one subsystem, actuator saturation is considered in this paper, while the disturbances are present in all the three subsystems. This leads to the incapability of the traditional schemes on this topic. For this, a novel control design scheme is proposed by skillfully incorporating adaptive dynamic compensation technique, constructive methods of command filters and an auxiliary system for the actuator saturation into the backstepping framework, and in turn to design a practical tracking controller which ensures that all the states of the resulting closed-loop system are bounded and the system output practically tracks the reference signal. It is worthwhile strengthening that a more wider class of reference signals can be tracked since they are only first-order continuously differentiable but twice or more in the related literature. Finally, a numerical example is provided to validate the effectiveness of the proposed theoretical results.

Adaptive stabilization for an uncertain reaction–diffusion equation with dynamic boundary condition at control end

This paper is devoted to the stabilization of a class of uncertain reaction–diffusion equations with dynamic boundary condition. Remarkably, the dynamics of boundary condition at control end are considered while unknown parameters are contained in both the intra-domain and the dynamic boundary. This greatly relaxes the restrictions on boundary dynamics and system uncertainties of the related literature where dynamic boundary condition is neglected or considered but unknown parameters are only contained in the dynamic boundary or even completely excluded from the whole system. To solve the control problem, a novel control framework is established by infinite-dimensional backstepping method combining with adaptive compensation technique based on passive identifier. Then, an adaptive state-feedback controller is explicitly constructed which guarantees that all the states of the resulting closed-loop system are bounded while those of the original system converge to zero. A simulation example is provided to validate the effectiveness of the proposed theoretical results.

Adaptive stabilization for a reaction–diffusion equation with uncertain nonlinear actuator dynamics

This paper is devoted to the stabilization of a reaction–diffusion equation with uncertain nonlinear actuator dynamics described by a cascaded parabolic PDE–ODE system. Essentially different from the systems in the related literature, control input appears in the ODE subsystem rather than in the PDE one, and moreover, the actuator dynamics are nonlinear and possess uncertainties rather than linear without uncertainty. Note that the existing control schemes so far are ineffective to handle such systems, and hence the stabilization problem for these systems is still unsolved. For this, backstepping approaches in both infinite and finite-dimension are smartly combined with the adaptive dynamic compensation technique in order to solve the control problem. Specifically, by using an infinite-dimensional backstepping transformation and its inverse, the original PDE subsystem is first changed to a new one from which control design is much convenient. Then, based on the new system together with the actuator dynamics, an adaptive state-feedback controller is explicitly constructed by using a couple of finite-dimensional backstepping transformations, which guarantees that all the states of the resulting closed-loop systems are bounded. Moreover, both the control input and the states of the original system converge to zero ultimately. Finally, a simulation example is provided to validate the effectiveness of the established theoretical results.

Quantized Adaptive Finite-Time Bipartite NN Tracking Control for Stochastic Multiagent Systems.

This article investigates the quantized adaptive finite-time bipartite tracking control problem for high-order stochastic pure-feedback nonlinear multiagent systems with sensor faults and Prandtl-Ishlinskii (PI) hysteresis. Different from the existing finite-time control results, the nonlinearity of each agent is totally unknown in this article. To overcome the difficulties caused by asymmetric hysteresis quantization and PI hysteresis, a new distributed control method is proposed by adopting the adaptive compensation technique without estimating the lower bounds of parameters. Radial basis function neural networks are employed to estimate unknown nonlinear functions and solve the problem of algebraic loop caused by the pure-feedback nonlinear systems. Then, an adaptive neural-network compensation control approach is proposed to tackle the problem of sensor faults. The problem of the "explosion of complexity" caused by repeated differentiations of the virtual controller is solved by using the dynamic surface control technique. Based on the Lyapunov stability theorem, it is proved that all signals of the closed-loop systems are semiglobal practical finite-time stable in probability, and the bipartite tracking control performance is achieved. Finally, the effectiveness of the proposed control strategy is verified by some simulation results.

A High-Efficiency and Fast-Transient Low-Dropout Regulator With Adaptive Pole Tracking Frequency Compensation Technique

An advanced smooth pole tracking technique for a low dropout (LDO) regulator with a ceramic capacitor is presented in this article. Normally, the dominant pole is at the output of an LDO and becomes load dependent, which may cause a loop stability issue during the whole load-current application range. The proposed frequency compensation methodology with adaptive load resistor control of an error amplifier (EA) alleviates the problem and reduces the dependence of equivalent series resistance of an output capacitor. Moreover, combined with this compensation strategy, an ultrafast EA by utilizing a transconductance enhancement technique is proposed to greatly reduce output voltage spikes as well as response time of the LDO during transient. This circuit has been implemented in a 0.18-μm standard CMOS process and occupies an active chip area of 0.017 mm2. Experimental results show that it can deliver 150 mA load current at 200 mV dropout voltage. Good loop stability and transient responses are easily achieved without degrading other important LDO parameters.

Prescribed Performance Adaptive Fuzzy Containment Control for Nonlinear Multiagent Systems Using Disturbance Observer

This article focuses on the containment control problem for nonlinear multiagent systems (MASs) with unknown disturbance and prescribed performance in the presence of dead-zone output. The fuzzy-logic systems (FLSs) are used to approximate the unknown nonlinear function, and a nonlinear disturbance observer is used to estimate unknown external disturbances. Meanwhile, a new distributed containment control scheme is developed by utilizing the adaptive compensation technique without assumption of the boundary value of unknown disturbance. Furthermore, a Nussbaum function is utilized to cope with the unknown control coefficient, which is caused by the nonlinearity in the output mechanism. Moreover, a second-order tracking differentiator (TD) is introduced to avoid the repeated differentiation of the virtual controller. The outputs of the followers converge to the convex hull spanned by the multiple dynamic leaders. It is shown that all the signals are semiglobally uniformly ultimately bounded (SGUUB), and the local neighborhood containment errors can converge into the prescribed boundary. Finally, the effectiveness of the approach proposed in this article is illustrated by simulation results.

A 13.56-MHz Active Rectifier With SAR-Assisted Coarse-Fine Adaptive Digital Delay Compensation for Biomedical Implantable Devices

This letter presents an active rectifier with SAR-assisted coarse-fine adaptive digital delay compensation technique for biomedical implantable devices. Both on- and off-delays are compensated by adjusting the comparator offset. In order to achieve accurate zero-voltage switching against process, voltage, and temperature variations, a two-step coarse-fine digital tuning method is introduced. The coarse-tuning phase and the fine-tuning phase are controlled by a 7-bit SAR controller and a 3-bit bidirectional counter, respectively. Meanwhile, the tuning frequency is designed to be much smaller than the switching frequency to minimize the power consumption. The proposed rectifier has been fabricated in 0.18- $\mu \text{m}$ CMOS process with a core area of 0.203 mm2 and a maximum output power of 54 mW. With the input ac voltage ranging from 1.5 to 2 V, the measurement results show that the peak power conversion efficiency and voltage conversion ratio are 92.6% and 95.7% for the load resistor of 2 $\text{k}\Omega $ , respectively.

Distributed Adaptive Control for Asymptotically Consensus Tracking of Uncertain Nonlinear Systems With Intermittent Actuator Faults and Directed Communication Topology.

In this article, we investigate the output consensus tracking problem for a class of high-order nonlinear systems with unknown parameters, uncertain external disturbances, and intermittent actuator faults. Under the directed topology conditions, a novel distributed adaptive controller is proposed. The common time-varying trajectory is allowed to be totally unknown by part of subsystems. Therefore, the assumption on the linearly parameterized trajectory signal in most literature is no longer needed. To achieve the relaxation, extra distributed parameter estimators are introduced in all subsystems. Besides, to handle the actuator faults occurring at possibly infinite times, a new adaptive compensation technique is adopted. It is shown that with the proposed scheme, all closed-loop signals are globally uniformly bounded and asymptotically output consensus tracking can be achieved.

Adaptive stabilisation for a class of uncertain coupled parabolic equations

ABSTRACT Adaptive stabilisation is investigated for a class of uncertain coupled parabolic equations in this paper. The presence of unknown coefficients can reduce the rigorousness of system modelling and enlarge the scope of the systems, whereas it could give rise to essential difficulties to control problems. Mainly due to the latter, certain new initiative, such as uncertainty compensation, should be considered to solve the stabilisation problem. For this, by infinite-dimensional backstepping method combining with adaptive compensation technique, an adaptive control scheme is proposed. Then, an adaptive state-feedback controller is constructed which guarantees that all the closed-loop system states are bounded while the original system states converge to zero. A numerical example is provided to validate the effectiveness of the theoretical results.

Adaptive Control Based on Passive Identifier for Uncertain Coupled Parabolic Equations

This paper is devoted to the stabilization for a class of uncertain coupled parabolic equations. The presence of the serious unknowns and strong coupling makes the system under investigation essentially different from those of the related literature and results in the ineffectiveness of the traditional methods on this topic. For this, by infinite-dimensional backstepping method combining with adaptive dynamic compensation technique based on passive identifier, an adaptive controller is explicitly constructed which guarantees that all the closed-loop system states are bounded and the original system states converge to zero. A simulation example is provided to validate the effectiveness of the theoretical results.

A stage-scanning two-photon microscope equipped with a temporal and a spatial pulse shaper: Enhance fluorescence signal by phase shaping.

Here, we present a stage-scanning two-photon microscope (2PM) equipped with a temporal pulse shaper and a spatial light modulator enabling full control over spectral and spatial phases of the exciting laser pulse. We demonstrate the capability of correcting wavefronts and temporal pulse distortions without cross-dependencies induced by optical elements at the same time enhancing the fluorescence signal. We implemented phase resolved interferometric spectral modulation for temporal pulse shaping and the iterative feedback adaptive compensation technique for spatial pulse modulation as iterative techniques. Sample distortions were simulated by cover glass plates in the optical path and by chirping the exciting laser pulses. Optimization of the spectral and spatial phases results in a signal increase of 30% and nearly complete recovery of the losses. Applying a measured spatial compensation phase within a real leaf sample shows the enhancement in contrast due to wavefront shaping with local fluorescence increase up to 75%. The setup allows full independent control over spatial and spectral phases keeping or improving the spatial resolution of our microscope and provides the optimal tool for sensitive non-linear and coherent control microscopy.

Numerical study of adaptive optics compensation based on Convolutional Neural Networks

Abstract Adaptive optics compensation technique is needed to improve the image quality when laser is propagating in the atmosphere. A novel compensation technique based on Convolutional Neural Networks (CNN) is proposed in the study. In general, CNN accepts an input image or patch, applies a sequence of transforming layers in order to extract features and finally classifies the inputs. We have tried to extract the features from the intensity images of distortions and obtain the corresponding Zernike coefficients by CNN. The network is trained by using large data sets of two intensity images which are measured in-focal and out-of-focus and Zernike coefficients for the distortions as inputs. The results indicate that the Zernike coefficients of distortions can be predicted by CNN which is mostly used to classify in recent researches. The compensation performance of adaptive optics based on CNN is tested for different strength turbulence scenarios and the robustness of the system is considered under different signal-to-noise ratio conditions.