Compensation Signal Research Articles

A signal compensation-based balance control for the rotary inverted pendulum system

In this paper, we study the balance control for the rotary inverted pendulum system with two joints both subject to uncertainty. To facilitate the experiment, we apply a classical energy-based swing-up control, eschewing compensation for the uncertainty. In balance control, we employ a novel approach in the form of a modified signal compensation-based balance control, which models the uncertainty as its previously sampled term and its variation. The signal compensation-based balance control involves three steps. First, for the linearized discrete-time state-space model around the upright equilibrium point, we design the main control signal by using the pole-placement technique. Second, we estimate the discrete-time model of the system by using the least squares identification method, thereby unveiling key points in the identification procedure. The discrete-time model presents the uncertainty as a previously sampled term and its variation. Third, we design two discrete-time compensation signals for the previously sampled term and the variation. By combining the main control signal and the two compensation signals, we formulate the balance control law. We further optimize the signal compensation-based balance control by adding an extra parameter, successfully circumventing the critical stability problem, and analyze the performance of the closed-loop system. Numerical simulation and comparative experiments demonstrate its effectiveness and strong robustness against the uncertainty in frictions and modeling errors of the system.

Improved shape-from-focus reconstruction for high dynamic range freedom surface

The 3D reconstruction of freedom surface has been a major challenge for computer vision as well as metrology. Complex surface structures such as large undulations and deep grooves, along with surface reflection properties like high dynamic range (HDR) pose many difficulties for 3D reconstruction. Shape from focus (SFF) is widely studied for its low cost, ease of deployment, and effectively avoiding occlusion. However, its inherent disadvantages, such as inaccuracy and sensitivity to noise, cannot be ignored. This article demonstrates an improved depth map optimization method to solve these problems. Firstly, a more comprehensive and reliable depth recovery evaluation method is proposed. Then an efficient optimization algorithm is applied to the filtered depth maps. Comparable experiments confirm our method's effective suppression of noise and the reliable compensation of missing signals when measuring freedom surfaces.

Voltage Sag Detection and Compensation Signal Extraction for Power Quality Mitigation Devices

The importance of voltage quality is continuously increasing in electrical networks due to the rising manufacturing costs resulting from system faults and disturbances in utility dynamics. Researchers generally prefer reference-frame transformation-based methods to detect and mitigate these disturbances. However, these methods are adversely affected during unbalanced loading and disturbances due to their direct dependence on system dynamics (currents and voltages). In this study, a new and simple method based on Clarke transformation is proposed to detect disturbances and generate compensation signals for Power Quality Mitigation Devices. The aim is to address the deficiencies of existing approaches. Firstly, the Clarke transformation is introduced through the vector presentation. Then, the mathematical derivation of the proposed method is provided to enhance readers’ understanding. The voltage sag detection and compensation signal extraction of its control algorithm for a Dynamic Voltage Restorer is illustrated graphically. Subsequently, a simple power system is created using a simulation program. Balanced and unbalanced voltage disturbances are applied to the test system to demonstrate the validation of the proposed method under distorted system conditions. The results of voltage sag detection and compensation signal extraction for both the proposed and existing methods are compared at the end of the case studies.

Adaptive optimized consensus control for a class of nonlinear multi-agent systems with asymmetric input saturation constraints and hybrid faults

This article studies the adaptive optimized leader–follower consensus control problem for a class of discrete-time multi-agent systems with asymmetric input saturation constraints and hybrid faults based on the optimized backstepping technique. Different from the conventional saturation model, we consider an individual asymmetric saturation constraint for each actuator instead of a common upper and lower bound for all actuators. Besides, a set of hybrid faults is also considered, with the main focus on the partial fault and bias fault. To eliminate the effects of saturation and faults, a simplified smooth function is constructed to approximate the asymmetric saturation model, and designed compensation signals are used to cope with the two main types of faults to improve the fault-tolerance and system performance. Subsequently, long-term strategic utility functions and virtual control signals are approximated to the optimal levels by adopting the actor–critic neural network (NN) framework, and the actor–critic NN weights are adjusted in the light of a gradient descent method. According to the forward difference Lyapunov function approach, it is proved that the closed-loop system can be stabilized and all errors are semiglobally uniformly ultimately bounded. Finally, the validity of the proposed control scheme is demonstrated through two simulation examples.

Command filter‐based adaptive control of flexible‐joint manipulator with input saturation and output constraints

AbstractThis paper focuses on the topic of adaptive neural tracking control for flexible‐joint manipulator systems with output restrictions and input saturation. With the aid of the error compensation mechanism, command filter‐based adaptive neural control is proposed for robotic systems driven by permanent magnet synchronous machine (PMSM). An auxiliary signal produced by the first‐order linear system designed by the property of unmodeled dynamics is used to erase the dynamical uncertainties. The input saturation of system is estimated by ‐function with mathematical transformation. Using barrier Lyapunov function (BLF), each component constraint of output is tackled. During the virtual control design phase, radial basis function neural networks (RBFNNs) may reliably assess the unknown nonlinear continuous function. All the variables in the closed‐loop system are proved to be semiglobally uniform ultimate bounded (SGUUB) by integrating all compensation signals into the overall Lyapunov function and using the defined compact set in the stability analysis of dynamic surface control (DSC). An applied example on robotic system is used to verify the effectiveness of the constructed design idea.

An adaptive error constraint line-of-sight guidance and finite-time backstepping control for unmanned surface vehicles

This paper presents a finite-time path following control approach with an adaptive error constraint line-of-sight guidance for marine unmanned surface vehicles (USVs). The proposed guidance and control strategy is capable of keeping the cross-tracking error within the constraint range while the USV follows a desired curved path, and it can guarantee the tracking errors converge in finite time for an underactuated USV with unknown disturbances. Specifically, an adaptive error constraint line-of-sight guidance law is originally proposed to calculate the desired course angle and realize cross-tracking error constraint by combining the control Lyapunov function and adaptive sideslip estimator. Furthermore, a finite-time backstepping control method by using a new virtual control signal and modified tracking error compensation signals is applied to control an underactuated USV convergence to the desired course angle. Then, a finite time surge control law with a modified tracking error compensation dynamic is designed to achieve surge velocity tracking. The proposed finite-time control laws have better transient and steady-state performance, and simplify the design of the controller by not relying on interference observers. Case studies using a physics-based model are made to support the theoretical results.

Simulation of the dependence of the radiation pattern of a digital active phased array antenna on the main destabilizing factors

As for today digital active phased array antennas (DAPAA), based on the principle of functioning using digital diagram formation, are actively used as part of spacecraft and unmanned aerial vehicles in solving radio communication and radar problems. At the same time, for the purposes of radar, DAPAA allows you to perceive all the information contained in the structure of spatiotemporal electromagnetic fields in the opening of the array and transform it into data on the presence and parameters of objects, providing deep compensation for broadband interference signals, which, combined with the expansion of the dynamic range when accumulated during spatiotemporal processing, provides high noise immunity of radar stations. To solve communication problems, DAPAA allows you to form several beams simultaneously, which allows you to simultaneously serve several areas at once with the possibility of dynamically changing the bandwidth in the channels due to variations in partial power.

Data-driven based cross-coupling compensation method for the piezoelectric tube scanner of atomic force microscopes

In this work, a data-driven based cross-coupling compensation (DD-CCC) method is proposed to suppress the cross-coupling effect of the scanner in atomic force microscopy (AFM). Firstly, the phase difference method is employed to accurately estimate the frequencies, amplitudes and phases of the harmonic components of the periodic compensation signal obtained from the fractional model-free repetitive control with the nonsynchronized sampling. Afterwards, the compensation signal is constructed analytically via summing the harmonic components, which requires very little memory units. In AFM scanning, the constructed compensation signal is applied in a feedforward manner, which is combined with a baseline tracking controller to follow the desired staircase trajectories. A series of scanning and imaging tests are conducted on an AFM with a fixed sampling frequency and different scanning rates. The results demonstrate that high performance AFM scanning and imaging can be achieved under the scanning rate of 120 Hz with the DD-CCC method.

Adaptive actor‐critic neural optimal control for constrained nonstrict feedback nonlinear systems via command filter

AbstractThe actor‐critic neural optimal control is investigated for the state‐constrained nonlinear systems in the nonstrict feedback form with unmodeled dynamics in this paper. The filtering errors in the traditional dynamic surface control (DSC) are countervailed by the introduced compensation signals. Two design phases together determine the input: the feedforward input design and the near optimal input design. In the feedforward input design, a mapping rule is established to keep all the states in the finite range, and a first‐order adjunctive signal is designed to treat the unmodeled dynamics. In the near optimal input design, the cost function relying on the reconstructed error system is minimized by the near optimal input via adaptive dynamic programming (ADP). In the whole design processing, the unknown nonlinear uncertain parts are fitted by the radial basis function neural networks (RBFNNs). The stability analysis illustrates all the signals are bounded in the controlled system. Two simulation examples are employed to verify the theoretical findings.

Distributed adaptive cooperative control via command filters for multi-agent systems including input unmodeled dynamics and sensor faults

This study focuses on the topic of distributed adaptive cooperative tracking control (CTC) for non-strict feedback multi-agent systems (MASs) including input unmodeled dynamics, prescribed performance, sensor faults and unknown control directions. An adaptive neural CTC tactic is raised via command filtered backstepping technique. With the help of the property of Gaussian function, the barriers produced by the non-strict feedback terms and partial sensor faults are handled. Moreover, the prescribed performance on measured synchronization error is considered by designing a finite-time performance function. By introducing normalization signals and adjusting parameters, the problems caused by input unmodeled dynamics are resolved. The compensation signals and normalization signals are integrated into the whole Lyapunov function in the stability analysis. All the variables in the controlled system are turned out to be semi-globally uniformly ultimately bounded (SGUUB). Meanwhile, the synchronization errors never exceed to predefined boundary. A simulation example is employed to confirm the viability of the constructed control method.

Cross-correlation Forecast of CSST Spectroscopic Galaxy and MeerKAT Neutral Hydrogen Intensity Mapping Surveys

Cross-correlating the data on neutral hydrogen (H i) 21 cm intensity mapping with galaxy surveys is an effective method to extract astrophysical and cosmological information. In this work, we investigate the cross-correlation of MeerKAT single-dish mode H i intensity mapping and China Space Station Telescope (CSST) spectroscopic galaxy surveys. We simulate a survey area of ∼300 deg2 of MeerKAT and CSST surveys at z = 0.5 using Multi-Dark N-body simulation. The PCA algorithm is applied to remove the foregrounds of H i intensity mapping, and signal compensation is considered to solve the signal loss problem in H i-galaxy cross power spectrum caused by the foreground removal process. We find that from CSST galaxy auto and MeerKAT-CSST cross power spectra, the constraint accuracy of the parameter product ΩH I b H I r H I,g can reach ∼1%, which is about one order of magnitude higher than the current results. After performing the full MeerKAT H i intensity mapping survey with 5000 deg2 survey area, the accuracy can be enhanced to <0.3%. This implies that the MeerKAT-CSST cross-correlation can be a powerful tool to probe the cosmic H i property and the evolution of galaxies and the Universe.

Adaptive command-filtered finite-time consensus tracking control for single-link flexible-joint robotic multi-agent systems

This article presents a command-filtered finite-time consensus tracking control strategy for the considered single-link flexible-joint robotic multi-agent systems. First, each agent system considered in this article is a nonlinear nonstrict-feedback system with unknown nonlinearities, so the traditional backstepping method cannot be directly applied to the design controller. However, by applying the unique structure of the Gaussian function in radial basis function neural networks, the challenges in controller design caused by the aforementioned nonstrict-feedback system have been overcome. Second, the problem of unknown nonlinearities in the system is solved by the approximation property of radial basis function neural network technology. In addition, the traditional backstepping approach often leads to an “explosion of complexity” resulting from repeated derivation of virtual control signals. Our design addresses this issue by employing command filtering technology, which simplifies the controller design process. Meanwhile, new compensation signals are designed, which successfully eliminate the error influence posed by the filters. It is seen that the control strategy presented in this article can guarantee the tracking errors converge to a small neighborhood of origin in a finite time, and all signals in the closed-loop systems remain bounded. Eventually, the simulation results show the validity of the acquired control scheme.

Four-Wavelength Passive Demodulation Technique for the Measurement of Static/Dynamic Composite Signals

A four-wavelength passive demodulation technique for the measurement of static/dynamic composite signals is proposed and experimentally demonstrated. Those previously reported passive demodulation methods for extrinsic Fabry–Perot intererometrics (EFPIs) will collapse if the cavity length changes significantly during signal demodulation. However, the proposed technique can demodulate the measurand even if the cavity length changes significantly. The phase difference between different interferometric signals is firstly calculated by using four interferometric signals. The phase compensation of these interferometric signals is then implemented to eliminate the influence of cavity length variations on the demodulation. The change of the cavity length can then be recovered. Dynamic signals, static signals, and static/dynamic composite signals were interrogated by the demodulation technique with relative errors of ~0.1%. An EFPI with a cavity length ranging from 32.53 μm to 1219.29 μm is detected by the same demodulator. The fast measurement of a cavity length change with an amplitude of ~80 μm is achieved. The technique is applicable to different types of signals applied on EFPIs with an unsteady cavity length.

Adaptive backstepping control of primary permanent magnet linear motor via radial basis function neural network and command filter

This paper studies the displacement and speed control of primary permanent magnet linear motor (PPMLM) under the influence of unmodelled load interference, time-varying parameters and end effects. Adaptive backstepping control is first proposed to ensure system stability in the presence of time-varying parameters during motor movements. Then, radial basis function (RBF) neural network is used to compensate for the effect of the unmodeled load disturbance of the system. Furthermore, the command filter is implemented in the backstepping control to counteract the differential expansion phenomenon. The compensation signal is set to reduce the filtering error induced by the filter. Simulation results are given to verify the good performance of the proposed control in comparison with the adaptive backstepping controller and the command filter adaptive backstepping controller.

Dispersion compensation of Lamb waves based on a convolutional auto-encoder

Lamb wave-based detection has become a promising technique for structural health monitoring in plate-like structures. However, the dispersion effect of Lamb waves makes the wave packets elongated, which degrades the resolution for damage identification. Hence, it is necessary to develop effective methods for the dispersion removal of Lamb waves. Recently, deep learning has drawn much attention for Lamb wave detection due to its powerful feature extraction capability. Most current deep learning models are developed directly for damage classification or location estimation tasks on the Lamb wave signals, rather than signal processing for further manipulation. Therefore, in this paper, a novel approach based on a convolutional auto-encoder is proposed for the dispersion compensation of Lamb waves. The convolutional auto-encoder model is utilized to construct the mapping relationship between the dispersive signal and the time of flight of the wave packet. The dispersion compensated signal is reconstructed by combining the estimated time of flight from the network with the waveform of the excitation signal. Numerical and experimental validations on aluminum and composite plates are implemented to verify the effectiveness of the proposed method. Compared with conventional methods for dispersion compensation, the results demonstrate that our proposed method not only separates the overlapped wave packets but also enables dispersion compensation of multimodal and multi-packet Lamb wave signals. In addition, this method is still applicable in the case of inaccurate dispersion data due to the generalizability of the data-driven model.

Adaptive backstepping control for nonlinear interconnected systems with prespecified-performance-driven output triggering

This paper considers a class of nonlinear large-scale interconnected systems and proposes an event-triggered decentralized controller by using adaptive output feedback backstepping. To save the communication resource, the output measurements are transmitted to the controller based on a novel double-events-triggered (DET) mechanism driven by transient performance request. Due to the use of triggered mechanism, only discontinuous output signals can be utilized such that the derived virtual controls are discontinuous, of which the derivatives cannot be computed as often done in standard backstepping design procedure. To overcome this difficulty, a new error signal decomposition and compensation mechanism is proposed to quantify and compensate for the difference between the virtual controls derived by using the event-triggered measurement signals and the real-time system outputs. Through a nonsmooth stability analysis where the closed-loop stability is considered under different conditions of output data transmissions, it is shown that the tracking error remains within a prespecified performance bound all the time. In contrast to the existing backstepping results addressing event-triggered outputs, the designed DET mechanism does not require the virtual control signals to be reversely transmitted to the sensor side from the controller side and guarantees the communication burden can be reduced under tracking performance guarantees.

Adaptive neural network finite‐time command filter tracking control for nonlinear systems with multiple coupling high‐order terms and disturbances

AbstractThis paper is devoted to the research of adaptive command filter tracking control for a class of uncertain nonlinear systems. The coexistence of multiple coupling high‐order terms, uncertain external disturbances, and unknown time‐varying coefficients makes the studied system more general compared with the existing results. By skillfully combining adaptive technology, command filter control, and backstepping method, a new type of adaptive command filter tracking control algorithm is designed. This controller not only solves the problem of complex explosion but also introduces compensation signals to achieve a higher precision tracking effect. Ultimately, the validity of the control algorithm is verified by a numerical simulation and a practical application model simulation.

Observer-Based Dynamic Event-Triggered Control for Multiagent Systems With Time-Varying Delay

This article is concerned with the dynamic event-triggered-based adaptive output-feedback tracking control problem of nonlinear multiagent systems with time-varying input delay. By utilizing the approximation capability of neural network (NN), a low-gain nonlinear observer is first established to estimate the immeasurable states. To mitigate the effect of time-varying input delay, an auxiliary system with communication information is designed to generate the compensation signals. Then, a distributed adaptive composite NN dynamic surface control (DSC) strategy is proposed to acquire the satisfactory tracking accuracy, where the filter errors are compensated by the introduced serial-parallel estimation model. Moreover, an effective switching dynamic event-triggered mechanism is developed to determine the communication instants and reduce the update frequency of the controller. It is proven that the consensus tracking error converges to a residual set of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed composite NN DSC scheme.

Fixed-time tracking control for high-order nonlinear systems with unknown time-varying input delay

Fixed time tracking control for nonstrict feedback high-order nonlinear systems with unknown time-varying external disturbance and unknown time-varying input delay has been studied in this research. Firstly, the obstacle caused by unknown time-varying input delay has been tackled by introducing a compensation signal. The unknown nonlinear functions are identified by neural networks and the algebraic loop problem for nonstrict feedback systems has been overcome by using the properties of neural networks. Further, a bound estimation mechanism is employed to estimate the identify error and unknown time-varying external disturbance without using the priori knowledge of the boundary. Regarding the obstacle caused by high-order terms and the singularity problem have been tackled by using adding a power integrator mechanism and the inequality transformation technique, respectively. The boundedness of all the signals in the closed-loop systems has been proved by using the Lyapunov stability theorem, and the convergence time is independent of the initial value of the system state. Finally, the simulation results show the effectiveness of the proposed method.

A collaborative generation-side clearing model for generation company in coupled energy, ancillary service and carbon emission trading market in China

Under the current technical conditions and power mix in China, flexible modified thermal power units must provide enough flexibility resources for new power system since the potential of flexible resource has not been fully used. In this paper, a bi-level model is proposed to characterize the decision behavior of the generation company in the case of individual rationality and generation-side coordination. The model fully considers the concrete conditions of China and the technical differences between thermal power units in the multi-market (including energy, ancillary services and carbon emission trading market). The bi-level model could be reconstructed into an equivalent MILP model which can be solved directly. The strategic bidding behavior of small capacity units in the energy market show the urgency to enhance the transmission of deep peak regulation price compensation signals. The coupling of multi market is likely to be the most effective policy package to guide large-scale units to play a supporting role and small-scale units to assume the regulating function to play a role in the transition to a low-carbon future. Long-term policy mechanisms should be developed, combining electricity transition with the mechanisms in the carbon emission trading market to achieve the integral maximization of environmental benefit.