Adaptive Feedforward Control Research Articles

System Integration Design of High-Performance Piezo-Actuated Fast-Steering Mirror for Laser Beam Steering System.

This paper presents an innovative piezo-actuated fast-steering mirror (FSM) that integrates control design and system operation to improve the tracking performance of laser beam steering (LBS) systems. The proposed piezoelectric FSM is centered on two pairs of stacked actuators functioning in the tip-tilt direction via novel flexible hinges with strain-gauge sensors for position measurement. The suggested flexible hinge scheme allows the first fundamental resonance mode with the optical mirror to exceed 400 Hz while achieving an actuation range of ±5 mrad. Thus, the design offers a wider mechanical actuation range than conventional piezoelectric FSMs. Moreover, LBS systems that use fast-steering motion controllers should be robust against dynamic disturbances, such as periodic external vibrations. Such disturbances, inherently associated with the operating conditions for LBS systems, typically reduce the stability of the tip-tilt motion. To attenuate the effects of such disturbances, a high-precision control system is necessary for the tip-tilt motion. Therefore, a control method integrating a proportional-integral controller with an adaptive feedforward control (AFC) algorithm is outlined to enhance tip-tilt tracking performance during high-speed scanning, compared with conventional LBS systems. Based on experimental findings, the AFC algorithm boosted control performance under dynamic disturbances, such as sinusoidal vibrations with multiple frequencies.

Dual-reference adaptive feedforward control of a magnetostrictive device for synchronous micropositioning and microvibration isolation

In this paper, we propose a novel dual-reference adaptive feedforward controller to realize synchronous micropositioning and microvibration isolation on a magnetostrictive device. The scheme of the proposed adaptive feedforward controller and its differences from the traditional single-reference controller are briefly introduced. The desired trajectory and available external disturbance as two input references are utilized to develop the proposed controller. The dynamics compensator is constructed based on the modified filtered-x normalized least mean square (MFxNLMS) algorithm with the discrete cosine transform (DCT) technique. The asymmetric hysteresis compensator is modeled via the arctangent-polynomial modified Prandtl–Ishlinskii (APMPI) model. The experimental setup is built, and the closed-loop control experiment is carried out according to the designed experimental process. Comparison of experimental results show that the proposed dual-reference DCT-MFxNLMS controller behaves better than the single-reference DCT-MFxNLMS and proportional-derivative-derivative (PID) integrated controller for the synchronous micropositioning and vibration isolation cases. Moreover, by the dual-reference DCT-MFxNLMS controller, the vibration isolation ratio enhances and the tracking bandwidth increases within the interest of frequency bandwidth, compared with those of open-loop system, respectively.

Improved Adaptive Feedforward Controller Based on Internal Model Principle with Disturbance Observer for Laser-Beam Steering Systems

This study presents an effective control algorithm to improve the robustness of fast steering mirror (FSM)-based laser-beam steering systems against dynamic disturbances, such as repetitive disturbances resulting from operating conditions. A stable control system must be able to maintain the required high-precision control, even when dynamic disturbances affect the FSM system. In this study, an improved control method is proposed using an internal model principle (IMP)-based nonlinear controller with a disturbance observer (DOB) for the FSM system. This IMP-based controller with DOB can attenuate the residual control-error signal under dynamic disturbance conditions.

Adaptive compound control of laser beam jitter in deep-space optical communication systems.

In deep-space optical communication systems, precise pointing and aiming of the laser beam is essential to ensure the stability of the laser link. In this paper, an adaptive compound control system based on adaptive feedforward and Proportional-Integral-Differentiation (PID) feedback is proposed. The feedforward controller is stabilized using Youla-Kucera (YK) parameterization. In the YK parameterized structure, the free parameter Q(z) consists of an adaptive filter. The proposed method constitutes an adaptive feedforward control algorithm through the adaptive filter Q(z). The problem of suppressing laser jitter is transformed into a problem of minimizing a sensitivity function containing the adaptive filter. The stability of the compound control system is ensured by configuring the individual parameters of the YK parameterized feedforward controller and the PID controller, and the adaptive regulation of the controller is realized on the premise of system stability. To verify the effectiveness of the compound control system, we established an experimental platform for laser beam stabilization control. We experimentally compare the effectiveness of the proposed control method with the classical method. The experimental results show that the method proposed in this paper can effectively suppress the complex laser beam jitter consisting of narrow-band sinusoidal and broad-band continuous vibrations.

Backstepping based neural [formula omitted] optimal tracking control for nonlinear state constrained systems with input delay and disturbances

This paper focuses on the problem of H∞ optimal tracking control for a class of nonlinear state constrained systems with input delay and disturbances. With the aid of Pade approximation, an auxiliary variable is devised to eliminate the effects of input delay. Combining barrier Lyapunov functions (BLFs) with backstepping design technique, a feedforward adaptive controller is designed to transform the tracking control problem of nonlinear state constrained system into an equivalent H∞ control problem of input-affine error system without state constraints, wherein neural networks (NNs) are employed to approximate unknown system dynamics. Then based on single-network adaptive dynamic programming (ADP), an H∞ optimal feedback controller is developed by utilizing a single critic network to learn the Nash equilibrium related to Hamilton–Jacobi–Isaacs (HJI) equation. Therefore, the whole tracking controller can be constructed by integrating the feedforward adaptive controller with the optimal feedback controller. Moreover, it is proven by Lyapunov’s theory that all signals within the closed-loop system are uniformly ultimately bounded (UUB), and the tracking error converges to a small neighborhood of the origin without violating any state constraints. Two simulation examples are also presented to validate the effectiveness of the proposed approach.

Research on adaptive feedforward control method for Tiltrotor Aircraft/Turboshaft engine system based on radial basis function neural network

The paper proposes an adaptive feedforward control approach based on a radial basis function neural network to address the issue of poor disturbance rejection capability in conventional integrated control methods for tiltrotor aircraft propulsion systems. Firstly, a comprehensive simulation model of the tiltrotor aircraft and turboshaft engine is established, and its accuracy is validated against the results of the General Tiltrotor Integrated Simulation Code. Building upon this, a cascade Proportional-integral control parameter design method based on separation principles is proposed to tackle the problem of designing control parameters for the Proportional Integral main loop structure. Subsequently, an adaptive feedforward control method that takes into account both aircraft-induced disturbances and model uncertainties for turboshaft engines is presented using a Radial Basis Function neural network. This approach includes a nonlinear estimator capable of estimating load disturbances and a Radial Basis Function adaptive feedforward controller. Furthermore, stability proof of the closed-loop system is provided based on Lyapunov function analysis. Finally, digital simulations and hardware-in-the-loop experiments are conducted based on a typical tiltrotor flight mission. The results demonstrate that the proposed method effectively mitigates the power turbine drop compared to the conventional Proportional Integral controller with collective pitch feedforward.

Precision positioning based on temperature dependence self-sensing magnetostrictive actuation mechanism

The self-sensing actuator that integrates both actuation and sensing functions is an effective way to simplify the structure of electromechanical systems. This paper proposes a temperature-dependent self-sensing actuation strategy based on a self-sensing giant magnetostrictive actuator (SSGMA) by sensing online stiffness. Utilizing the developed model considering temperature-dependent hysteresis nonlinearity, the self-sensing output characteristics of SSGMA are first evaluated. The self-sensing based control system is then designed in detail. The relationship between the self-sensing signal and the output displacement of SSGMA at different temperatures is constructed by General Regression Neural Network (GRNN) model for fast recognition by the controller. On this basis, a composite control scheme that takes into account rate-dependent asymmetric hysteresis nonlinearities and combines an adaptive feedforward controller with PID feedback controller is proposed for precision actuation of SSGMA. Finally, a temperature-controlled experimental platform is assembled for verification. Experimental results demonstrate that, based on the developed model, the SSGMA is capable of accurate self-sensing output at temperatures of 22 °C, 40 °C, and 70 °C, respectively. The SSGMA with the proposed control method enables the synchronous and precise self-sensing positioning of step and multiple-frequency sinusoidal expectations at different temperatures.

Adaptive feedforward control of sinusoidal disturbances with applications to electric propulsion systems

Generalized adaptive feedforward cancellation with a reference sensor is considered to specifically suppress second harmonic torque oscillations with an ac fed propulsion system for an electric train. A single complex-valued design parameter is tracked through gradient-type adaptation. Both Cartesian and polar parameter representations are considered, resulting in quite varying convergence properties. Three different adaptation algorithms are proposed and evaluated using power lab experiments. At fixed operating conditions, a Cartesian form parameter adaptation is shown to be more robust to the choice of initial conditions, whereas a polar form representation shows better performance when covering a wide range of operating points.

Active noise control modeling for an electric bus driving position and its effect on sound quality

Current electric bus has become an important public transport for people in China, and its interior sound quality directly affects the driver’s physical and mental health for a long working time and the comprehensive performance of the vehicle. Therefore, it is an inevitable and urgent trend to research electric bus interior sound quality. Aiming at the problem of subjective uncomfortable auditory sensation caused by different speeds, the noise signals near the driver’s ears were collected under two constant speed conditions. On the basis of principle analysis of feed-forward adaptive control system, the primary and secondary path transfer functions were presented using Simulink module in MATLAB to construct three active noise control models for an electric bus based on least mean square (LMS), normalized LMS (NLMS) and filtered-x LMS (FxLMS). Numerical simulation results indicate that the established control models are convergent, and four acoustic parameters are effectively reduced after controls to achieve the effect of improving the sound quality.

Reduction in high frequency disturbance in hard disk drives using adaptive feed-forward control

Reduction in high frequency disturbance in hard disk drives using adaptive feed-forward control

A Parallel Analog and Digital Adaptive Feedforward Controller for Active Noise Control

A Parallel Analog and Digital Adaptive Feedforward Controller for Active Noise Control

Active vibration hybrid control strategy based on multi-DOFs piezoelectric platform

With the increasing requirements for vibration isolation in multiple degrees of freedom (multi-DOFs), active control strategy is becoming more meaningful. However, the vibration isolation performance is limited by the time delay of feedback control, and cannot meet higher requirements. Therefore, this paper proposes a multi-DOFs active vibration hybrid control (AVHC) strategy based on a piezoelectric platform. The AVHC integrates the adaptive feedforward control based on the modified recursive least squares (MRLS) algorithm, and the feedback control based on the integral force feedback (IFF) algorithm. To achieve advanced response, the ground-based vibration signal is offset by the MRLS algorithm. To further reduce the coupling of multi-DOFs, the feedback and feedforward coordinates are fused through the matrix transformation, and the signals are linearly superimposed by the AVHC. The experimental results show that the AVHC can further reduce the resonance peaks of the three translational directions ( X/ Y/ Z) compared with the feedback (FB) control. The resonance peaks are reduced from 14.6 dB (FB) to 3.11 dB (AVHC), from 14.56 dB (FB) to 5.14 dB (AVHC), and from 12.44 dB (FB) to 3.78 dB (AVHC) in X/ Y/ Z directions, respectively. The attenuation rates are improved by 73.36%, 66.19%, and 63.10% in X/ Y/ Z directions, respectively.

An adaptive feed-forward active vibration isolation algorithm for input disturbance unbalanced MIMO systems

Multi-axis active vibration isolation systems often encounter order-of-magnitude differences in the perturbations in each input direction. This causes adaptive feedforward control systems to converge at a relatively slow rate, or possibly even diverge. To solve this problem, we propose an improved adaptive feedforward vibration isolation technique based on the classical FxLMS algorithm. Our approach constructs a weighted transformation matrix based on singular value decomposition, attenuates the uneven influence of the input perturbations, and effectively avoids the problems of slow convergence and poor stability faced by existing adaptive algorithms. This paper first describes the parameter update algorithm and the structure of adaptive feedforward algorithms. The convergence of the proposed algorithm is then analyzed, and the method for constructing the weighting matrix is introduced. Finally, the feasibility of the algorithm is verified using a multi-axis vibration isolation system simulation model. The simulation results show that the power spectrum of acceleration in the frequency domain from 0.1 to 100 Hz is attenuated by up to 70 dB under the condition of no excitation in a specific direction, demonstrating the vibration isolation effect of the algorithm. The relative deviation between the predicted and actual parameter error is no more than 6 %, which shows that the algorithm achieves good convergence. The proposed algorithm has potential applications in the active vibration isolation of space telescopes and high-precision optical equipment.

Adaptive analog and digital active noise control

Digital adaptive controllers are widely used for active noise control. The secondary path delay, including the anti-aliasing, sampling, and reconstruction effects, must be shorter than that in the primary path to maintain good broadband performance. To eliminate the added delay of the sampling, a mixed analog and digital adaptive feedforward controller is developed. The analog controller is based on a state-filtered adaptive linear combiner, while the digital one uses an adaptive finite-impulse-response filter. The adaptation of the analog controller is derived based on a sampled version of the normalized projection algorithm, while the digital controller adaptation is based on the filtered-reference least-mean-squares algorithm. Two configurations are considered for the analog combiner. One aims to minimize the electrical noise by introducing individual state filters, all driven by the same reference signal. The other gives a superior bandwidth by driving each component filter with the previous filter output. The performance of the proposed controller is assessed and compared with separate analog or digital controllers. The results highlight that the suggested controller obtains significant attenuation levels close to the causality limit using the analog controller but also allows long impulse responses using the sampled controller.

Adaptive Feed-Forward Control for Gust Load Alleviation on a Flying-Wing Model Using Multiple Control Surfaces

Based on measured gust information, a multi-input multi-output (MIMO) adaptive feed-forward control scheme for gust load alleviation (GLA) on a semi-span flying-wing aircraft using multiple control surfaces is proposed. In order to remedy weight drift and biased estimation problems that are commonly encountered in adaptive control, the circular leaky LMS (CLLMS) algorithm is employed, which utilizes gust measurement information, filtered reference signals, and error signals to update controller parameters online. The results demonstrate that good load reductions are achieved in both continuous and discrete gust environments. For instance, the designed GLA control system leads to an 80.72% reduction in the root-mean-square (RMS) values of wing-root bending moment in the Dryden gust environment and a 77.59% reduction of its maximum value in the 1-cos discrete gust condition. Based on the limited power of the actuator and the limited authority for control surface deflections when integrating GLA into the flight control system, a weight-updating algorithm with deflection angle and rate constraints on control surfaces is proposed. The simulation results show that the strict constraints on control surface deflections will degrade the GLA performance. Finally, the influence of the partial jamming fault of actuators on GLA performance is studied. It is found that good GLA performance can be preserved despite the degraded performance during the initial stage of the actuator jamming fault. This is due to the robustness brought about by multiple control surfaces and the adaptability of the control algorithm.

Feasibility Study of Reduction in Sound-Pressure-Induced Vibration in Hard Disk Drives Using Adaptive Feed-Forward Control

Feasibility Study of Reduction in Sound-Pressure-Induced Vibration in Hard Disk Drives Using Adaptive Feed-Forward Control

An adaptive feedforward method in active vibration control for the propulsion shafting system

This paper proposes a new adaptive feedforward control method for the rejection of multiple periodic disturbances in the propulsion shafting system. The active vibration control scheme of the propulsion shafting system is established by employing the equivalent-input-disturbance principle which reduces the complexity of the control system in applications. The adaptive control algorithm is designed by taking the scale transformation procedure and the frequency-tracking technique into consideration. The scale transformation normalizes the iso-surface of the optimization function by the frequency response characteristics of the control channel, which immensely improves the convergence rate of the control algorithm for multi-harmonic disturbance. Moreover, the adaptive controller tracks the fluctuating frequencies in a direct method, enhancing the efficiency and robustness of the algorithm for eliminating time-varying disturbances. Finally, numerical simulation and experimental validation are carried out to show that the proposed method has good performance on the application of the active vibration control of the propulsion shafting system.

On‐line instrumental variable‐based feedforward tuning for non‐resetting motion tasks

SummaryData‐driven feedforward control for tracking of varying and non‐resetting point‐to‐point references requires continuous updating of feedforward parameters instead of task‐by‐task updating. The aim of this paper is to develop an adaptive feedforward controller for non‐resetting point‐to‐point motion tasks by a data‐driven feedforward controller. An approximate optimal instrumental variable (IV) estimator with real‐time bootstrapping is employed in a closed‐loop setting to update the feedforward parameters. A case study on a wafer‐stage and experimental validation on a benchmark motion system show the performance benefit.

Predicting the performance and optimizing the control parameters of an active vibration control system by pre-identifying the secondary path

In an active vibration control (AVC) system, the secondary path can generally only be identified when the system has been built. The performance of a typical adaptive feedforward control system using the filtered-x least mean square (FxLMS) algorithm is heavily dependent upon the dynamic characteristics of the secondary path. It would, therefore, be helpful for engineers to have knowledge of this behavior early in the design process. In this paper, pre-identification of the secondary path is proposed, which means its offline identification model can be obtained without installing the AVC equipment. This can save both time and money. In the pre-identification process, the secondary path is split into three parts. The first part includes the actuator and related electrical equipment. The frequency response function of this part can be identified after the hardware has been selected. The second part involves the dynamic behavior of the structure to be controlled, which is identified by impact excitation using a specially developed pneumatic impact harmer. The third part concerns the analogue/digital and digital/analogue converters whose effect on the secondary path is offset by a phase shift corresponding to half the sampling time. In this paper, the pre-identification process of the secondary path components is described, and some experiments are conducted to verify the feasibility of the approach and to demonstrate the significance of the pre-identification results. This mainly involves predicting the control performance and optimizing the parameters of the control system. Combined with the simulation of the control system, the approach proposed can improve the work efficiency of large-scale engineering projects and reduce the cost of testing.

Active vibration control using nonlinear auto-regressive neural network to identify secondary channel

The power unit on board the ship generates periodic low-frequency vibration that affects the normal operation of the equipment on board, and the adaptive feedforward control algorithm can effectively suppress such harmful vibration noise. But the adaptive feedforward control algorithm needs to obtain the identification model of the secondary channels, and the frequency domain least squares method based on the linear Extended auto-regressive model (ARX) is difficult to obtain the identification model with nonlinear characteristics. The nonlinear auto-regressive model (NARX) adds nonlinear mapping layers to the topology of the ARX model to enhance the identification capability of the NARX model for complex systems. In this paper, a block diagram of the Fx-LMS feedforward control algorithm based on the NARX model is proposed, then the initial parameters of the NARX neural network are optimized using the Quantum Particle Swarm Optimization (QPSO) algorithm and the secondary channel is identified, and the identification results show that the accuracy of identifying the secondary channel using the NARX neural network is higher than that of the ARX model. The simulation and experimental results show that the vibration damping effect of the proposed method is better than the traditional Fx-LMS method for both single-line spectrum and multi-line spectrum periodic low-frequency disturbances, which provides a new method for the suppression of periodic low-frequency disturbances.