Adaptive Feedforward Control Research Articles

Adaptive control strategy of solid state transformer with fast dynamic response and enhanced balance performance

Solid state transformer (SST) is designed as a power transmission device between AC grids and DC grids. However, the power fluctuation causes unbalanced power sharing among dual active bridge (DAB) converters and large voltage fluctuations on dc buses. Here, an adaptive virtual impedance control (AVIC) strategy is proposed to improve the balance performance of SST. Adaptive virtual impedance controller gets the same dynamic response for DAB converters, which matches dynamic impedance of DAB converters. Thereby the power balance can be achieved even the load changes. Meanwhile, an adaptive feedforward control scheme is proposed to reduce the influence of load fluctuation on DC bus voltage. The adaptive feedforward scheme also considers the parameter mismatch, thereby the balance of SST will not be destroyed. Moreover, the balanced power sharing achieved by adaptive controller eliminates the difference between actual transmission power and compensation power in each rectifier, which further suppresses the intermediate capacitor voltage fluctuations. Finally, simulation and StarSim experimental results verified the correctness and effectiveness of the proposed control strategy.

Adaptive feedforward control of closed orbit distortion caused by fast helicity-switching undulators.

A new correction algorithm for closed orbit distortion based on an adaptive feedforward control (AFC) has been developed. At SPring-8, two helicity-switching twin-helical undulators (THUs) had been implemented with conventional feedforward corrections. However, the validity of these corrections turned out to be expiring due to unforeseen variation in the error magnetic fields with time. The developed AFC system has been applied to the THUs dynamically updating the feedforward table without stopping the helicity switching amid user experiments. The error sources in the two THUs are successfully resolved and corrected even while the two THUs are switching simultaneously with the same repetition period. The actual operation of the new AFC system enables us to keep the orbit variations suppressed with an accuracy at the sub-micrometre level in a transparent way for light source users.

Adaptive feedforward RBF neural network control with the deterministic persistence of excitation

Adaptive feedforward RBF neural network control with the deterministic persistence of excitation

Summarisation, Simulation and Comparison of Nine Control Algorithms for an Active Control Mount with an Oscillating Coil Actuator

With the further development of the automotive industry, the traditional vibration isolation method is difficult to meet the requirements for wide frequency bands under multiple operating conditions, the active control mount (ACM) is gradually paid attentions, and the control algorithm plays a decisive role. In this paper, the ACM with oscillating coil actuator (OCA) is taken as the object, and the comparative study of the control algorithms is performed to select the optimal one for ACM. Through the modelling of ACM, the design of controller and the system simulations, the force transmission rate is used to compare the vibration isolation performance of the nine control algorithms, which are least mean square (LMS) adaptive feedforward control, recursive least square (RLS) adaptive feedforward control, filtered reference signal LMS (FxLMS) adaptive control, linear quadratic regulator (LQR) optimal control, H2 control, H∞ control, proportional integral derivative (PID) feedback control, fuzzy control and fuzzy PID control. In summary, the FxLMS adaptive control algorithm has the better performance and the advantage of easier hardware implementation, and it can apply in ACMs.

Active vibration control of a gyroscopic rotor using experimental modal analysis

A gyroscopic rotor exposed to unbalance is controlled with an active piezoelectrical bearing in this paper. A model is required in order to design a suited controller. Due to the lack of related publications utilizing piezoelectrical bearings and obtaining a modal model purely exploiting experimental modal analysis, this paper reveals a method to receive a modal model of a gyroscopic rotor system with an active piezoelectrical bearing. The properties of the retrieved model are then incorporated into the design of an originally model-free control approach for unbalance vibration elimination, which consists of a simple feedback control and an adaptive feedforward control. After the discussion on the limitations of the model-free control, a modified controller using the priorly identified modal model is implemented on an elementary rotor test-rig comparing its performance to the original model-free controller.

An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation

In this paper, we present a novel adaptation rule to optimize the exoskeleton assistance in rehabilitation tasks. The proposed method adapts the exoskeleton contribution to user impairment severity without any prior knowledge about the user motor capacity. The proposed controller is a combination of an adaptive feedforward controller and a low gain adaptive PD controller. The PD controller guarantees the stability of the human-exoskeleton system during feedforward torque adaptation by utilizing only the human-exoskeleton joint positions as the sensory feedback for assistive torque optimization. In addition to providing a convergence proof, in order to study the performance of our method we applied it to a simplified 2-DOF model of human-arm and a generic 9-DOF model of lower limb to perform walking. In each simulated task, we implemented the impaired human torque to be insufficient for the task completion. Moreover, the scenarios that violate our convergence proof assumptions are considered. The simulation results show a converging behavior for the proposed controller; the maximum convergence time of 20 s is observed. In addition, a stable control performance that optimally supplements the remaining user motor contribution is observed; the joint angle tracking error in steady condition and its improvement compared to the start of adaptation are as follows: shoulder 0.96±2.53° (76%); elbow −0.35±0.81° (33%); hip 0.10±0.86° (38%); knee −0.19±0.67° (25%); and ankle −0.05±0.20° (60%). The presented simulation results verify the robustness of proposed adaptive method in cases that differ from our mathematical assumptions and indicate its potentials to be used in practice.

A novel ship path following method in inland waterways based on adaptive feedforward PID control

Given the complex and time-varying external disturbances of inland waterways, designing an accurate path following controller is challenging. Based on the traditional PID controller, combined with the servo system model and the lead compensator, an adaptive feedforward PID controller for path following of ships in inland waterways is designed considering ship maneuverability and external disturbances. Simulations of a ship in a curved channel in different scenarios are carried out to illustrate the effectiveness of the proposed path following method. Compared with the traditional path following controller, the proposed one based on adaptive feedforward PID control has favorable relative stability, anti-interference ability and high steady-state precision in inland waterways.

Two-Degree-of-Freedom based Model Reference Adaptive Control of Non-minimum Phase System

This paper presents the design of the Two-Degree-of-Freedom (2DOF) based Model Reference Adaptive Control (MRAC) as a tracking control system for the Non-minimum Phase (NMP) system. Perfect set point tracking is possible by using a cascade-connected inverse and non-inverse transfer function model of the dynamic system. But this method is restricted to the minimum phase system only, whereas, NMP system produces unbounded output response using this inverse model technique. Though, adaptive inverse feed-forward control technique is an effective tracking control system, it also requires an inverse model of the system. To overcome this problem, a novel feed-forward MRAC scheme for the inverse NMP model and the State feedback controller of the non-inverse model has been proposed, and both the controller are decoupled in a 2DOF framework. The feed-forward control technique guaranteed the tracking performance using the Lyapunov stability theory, and the feedback counterpart enhances the steady-state performance of the NMP plant. The robustness of the asymptotic stability as well as tracking performance of the closed-loop NMP system has been ensured by the proposed 2DOF control based MRAC technique. The effectiveness of the proposed control structure initially has been verified by the MATLAB Simulink toolbox and, then the demand has been strengthened by the implementation of the same control logic in the real-time environment using analog simulation with Op-amp based hardware model.

Adaptive feedforward control for crosswind landing with variable step size

In this article, a modified Steiglitz–McBride (SM) structure with variable step size is proposed for aircraft feedforward control to reduce lateral landing deviation in the presence of crosswind disturbance. Currently, most of the aircraft landing studies focus on the trajectory tracking problems in the longitudinal plane and seldom focus on lateral disturbances. This article develops a modified SM structure–based feedforward control system to online estimate both the primary path and the control path (secondary path), where a fuzzy logic–based variable step size strategy is implemented to ensure fast convergence rate and strong robustness under complex crosswind scenarios. As a consequence, the lateral deviation during crosswind landing could be largely reduced as fast as possible while the stability of the flight control system is maintained. A Boeing 747 model is used as a test bed, and the simulations are carried out on two different crosswind conditions to demonstrate the feasibility and effectiveness of the proposed method.

Composite adaptive dynamic programming for missile interception systems with multiple constraints and less sensor requirement

The adaptive integrated guidance and control issue of missiles with less sensor requirement is investigated in the optimal stabilization problem of an uncertain nonlinear system subjected to state and input constraints. The nonlinear system with partially unmeasurable states is transformed into the non-strict feedback form, firstly. Then, an adaptive observer is designed to approximate the full states, where a disturbance estimator is incorporated to suppress the unmatched external disturbances. Next, by employing a Barrier Lyapunov Function (BLF) and an auxiliary system to tackle the multiple constraints, an adaptive feedforward controller is raised to reduce the stabilization issue of the nonlinear system in non-strict feedback form to the equivalent control problem of an affine nonlinear system. Subsequently, an optimal controller is derived by utilizing adaptive dynamic programming (ADP) theory. The system stability is rigorously proved by using Lyapunov theory. Finally, simulations are performed to validate the effectiveness of the proposed control strategy.

Evasion-Faced Fast Adaptive Neural Attitude Control for Generic Hypersonic Vehicles with Structural and Parametric Uncertainties

In general, the evasion task requires the hypersonic vehicles (HSVs) to quickly complete the attitude maneuver in a short time. Moreover, the rapid variation of the flight modes often induces the structural and parametric uncertainties as well as the highly dynamic disturbances of the HSVs. The peculiar and complex characteristics of the evasion process make it difficult to design the evasion-faced flight control systems. In this work, we investigate the fast adaptive control design problem for the generic HSVs under the evasion task. By introducing several especial nonlinear functional vectors and properly designing the adaptive laws, the high dynamic disturbances and uncertainties can be suppressed. To deal with the completed unknown parts of the structural uncertainties and aerodynamic uncertainties caused by evasion maneuver, two radial basis function neural networks (RBFNNs) are introduced as real-time approximators. Furthermore, to improve the response speed of the flight control system, a super-twisting (STW) algorithm-based predictor is used as a feed-forward term of the controller. Consequently, a novel evasion-faced fast adaptive feed-forward control structure has been established for the HSVs. It has been proven that all the signals of the closed-loop system are bounded with satisfactory tracking velocity. Finally, the simulation experiment has been set up to show the effectiveness and advantages of the proposed control method.

Robust performance of virtual sensing methods for active noise control

This paper investigates the effect of changes in the environment on the performance of two widely-used virtual sensing methods for active noise control (ANC): the remote-microphone method and the additional-filter method. Robust performance of adaptive feedforward control algorithms incorporating such virtual sensing techniques is essential to achieving noise attenuation at the designated locations in practice, when subject to uncertainties in the control environment. Off-line simulations using the data measured with a headrest ANC system in a running car are initially conducted, to evaluate the performance of the two virtual sensing methods under practical conditions. The differences between the two methods are further studied by using an analytical model and numerical simulations of the headrest ANC system. It is shown that in general the additional-filter method is sensitive to uncertainties in the properties of the reference signals used for feedforward control, whereas the remote-microphone method is sensitive to changes in the plant responses related to the monitoring microphones. This study, therefore, can be used to guide the choice of virtual sensing methods in different applications.

Adaptive Feedforward Control of a Pressure Compensated Differential Cylinder

This paper presents the design, simulation and experimental verification of adaptive feedforward motion control for a hydraulic differential cylinder. The proposed solution is implemented on a hydraulic loader crane. Based on common adaptation methods, a typical electro-hydraulic motion control system has been extended with a novel adaptive feedforward controller that has two separate feedforward states, i.e, one for each direction of motion. Simulations show convergence of the feedforward states, as well as 23% reduction in root mean square (RMS) cylinder position error compared to a fixed gain feedforward controller. The experiments show an even more pronounced advantage of the proposed controller, with an 80% reduction in RMS cylinder position error, and that the separate feedforward states are able to adapt to model uncertainties in both directions of motion.

Adaptive Feedforward and Feedback Compensation Method for Real-time Hybrid Simulation Based on a Discrete Physical Testing System Model

ABSTRACT This study proposed a general framework for adaptive delay compensation in real-time hybrid simulation(RTHS). This framework is composed of two main parts: an adaptive feedforward controller to handle variable time delay and amplitude discrepancies, and a feedback controller to enhance the robustness of compensation. In particular, the adaptive feedforward controller is determined using a discrete physical testing system (PTS) model, whose parameters are estimated using the least-squares method. Moreover, the feedback controller is introduced to reduce dependency on the PTS model of the feedforward controller. Results of virtual and actual RTHS alongside five other compensation strategies revealed the superiority of the proposed compensation method.

Adaptive feedforward control of a collaborative industrial robot manipulator using a novel extension of the Generalized Maxwell-Slip friction model

Collaborative industrial robots often use strain-wave transmissions which display a highly nonlinear behavior. In particular, the friction torque depend on the load torque and the hysteresis characteristics were recently found to depend on the joint angular position.This paper presents a novel extension of the Generalized Maxwell-Slip friction model to describe said phenomena in a combined framework. The method overcomes the discontinuity around zero velocity of existing models. Experiments on the Universal Robots UR5e manipulator show superior performance in terms of torque prediction accuracy and tracking performance of the proposed method.An adaptive feedforward friction compensator is proposed based on the extended Generalized Maxwell-Slip friction model to compensate the time-variations of the Coulomb and viscous friction due to, respectively, wear and mispredictions of the lubricant temperature. The adaptive estimator relies on the sensing hardware readily available in the joints of the Universal Robots manipulators, i.e. two absolute rotary encoders; one at each side of the transmission, current sensing for the electric actuator, and a temperature sensor. Results show a considerable reduction of the torque prediction error and tracking error.

Adaptive feedforward thickness control in hot strip rolling with oil lubrication

The application of oil lubrication in hot strip rolling has many advantages for the process, i.e., reduced roll force, reduced energy consumption, better strip surface quality, and reduced roll wear. On the other hand, oil lubrication may also induce severe disturbances for several control loops in a tandem hot rolling mill, which can reduce the final strip quality or might even jeopardize a stable operation of the plant. For example, the widely used automatic gauge control (AGC) has a limited bandwidth to ensure stability in the whole operating range and thus cannot (dynamically) reject the thickness deviations caused by switching on and off the oil lubrication. For this reason, the effect of lubrication is analyzed in detail and mathematically modeled in this work. Based on this dynamic lubrication model together with a hydrodynamic roll gap model and a mill stand model, two feedforward control approaches for the strip thickness are presented. These controllers counteract the negative effects of varying lubrication. Moreover, the feedforward controllers are extended by a moving horizon estimator to track changes of uncertain parameters of the underlying models based on the measured roll force. The estimated parameters are instantly used by the feedforward controller. Simulation studies using an experimentally validated simulation model of a whole mill stand show that the proposed adaptive feedforward control approach provides a significant improvement of the strip exit thickness accuracy for strips with oil lubrication compared to conventional controllers.

An active hybrid control approach with the Fx-RLS adaptive algorithm for active-passive isolation structures

With improvements in the performance of optical equipment on spacecraft, the requirements for vibration isolation have become increasingly stringent. An efficient and reliable active hybrid control (AHC) approach used for an active-passive isolation single strut (APISS) is proposed in this paper. The APISS adopts an active-passive architecture in series as one single leg of the Stewart platform. To improve performance, the AHC approach includes a modified proportional integration force (PIF) feedback control algorithm and an improved filtered-x recursive least squares (Fx-RLS) adaptive feedforward control algorithm. The PIF is applied to establish a sky-hook damping and variable mass matrix system, and Fx-RLS is employed to compensate the vibration error caused by the base platform to the payload platform. Then, a single-DOF vibration isolation system consisting of a real-time active control system and a spectrum testing and analysis system is adopted to verify the proposed approach. The experimental results indicate that the AHC approach can effectively reduce the natural frequency by 5.02 Hz. Additionally, the resonance amplitude of the natural frequency decreases by 41.84 dB, the vibration attenuation rate reaches 99.2%, and the amplitude of the mid-frequency band is further attenuated. The experimental results highlight the effectiveness of the proposed method.

Simultaneous Fault Detection and Multivariable Adaptive Hybrid Control for Unbalanced Vibrations of Cracked Flexible Rotor Systems

Abstract For the reduction of unbalanced vibrations in a multi-input and multi-output flexible rotor system with electromagnetic actuators (EAs), conventional adaptive feedforward controllers (AFFCs) are very sensitive for changes in rotor spin frequencies. Although frequency updating is used in these controllers, a small variation in the rotor spin frequency can completely reduce their effectiveness. An adaptive notch filter is used in this research for the frequency estimation. By using this external frequency estimation, the performance of the conventional AFFCs can be enhanced. During changes in the rotor spin frequency, fundamental harmonics of the flexible rotor are also excited. Their amplitude is much higher compared to steady-state unbalanced vibrations, which can accelerate the wear and tear of components of EAs. By using feedback controllers, the amplitude of these fundamental harmonics can be reduced significantly. In real rotors with flexible bearing supports, any looseness of bolts and presence of transverse cracks can change system parameters significantly. Multiple harmonics are generated corresponding to even single spinning speed of the rotor. Robust stability as well as performance can be achieved in the presence of uncertainty and rotor crack nonlinearities using feedback controllers designed by mu-synthesis. By using the multiharmonic hybrid control, the higher harmonics can be compensated efficiently in case of a crack in rotor systems. The fast Fourier transform of the control signal can indicate the presence of a transverse crack in an online manner. In this way, active vibration control as well as rotor crack fault detection can be done simultaneously.

Adaptive feedforward vibration compensation control strategy of bearingless induction motor

In order to solve the unbalanced vibration problem of bearingless induction motor caused by the rotor mass eccentricity, an adaptive feedforward vibration compensation control strategy is researched. Firstly, the inverse dynamic decoupling control me

Payload-agnostic decoupling and hybrid vibration isolation control for a maglev platform with redundant actuation

Payload-specific vibration control may be suitable for a particular task but lacks generality and transferability required for adapting to the various payload. Self-decoupling and robust vibration control are the crucial problem to achieve payload-agnostic vibration control. However, there are problems still unsolved.In this article, we present a maglev vibration isolation platform (MVIP), which aims to attenuate vibration in payload-agnostic task under dynamic environment. Since efforts trying to suppress disturbance will encounter inevitable coupling problems, we analyzed the reasons resulting in it and proposed unique and effective solutions.To achieve payload-agnostic vibration control, we proposed a new control strategy, which is the main contribution of this article. It consists of self-construct radial basis function neural network inversion (SRBFNNI) decoupling scheme and hybrid adaptive feed-forward internal model control (HAFIMC). The former one enables the MVIP creating a self inverse model with little prior knowledge and achieving self-decoupling. For the unique structure of MVIP, the vibration control problem is stated and addressed by the proposed HAFIMC, which utilizes the adaptive part to deal with the periodical disturbance and the internal mode part to deal with the stability.