Iterative Feedforward Research Articles

Optimal iterative feedforward parameter tuning for varying trajectory under actuator constraints

AbstractThis paper presents a feedforward control algorithm that combines the benefits of optimal iterative learning control (OILC) and model‐based feedforward control (MFC) using iterative feedforward tuning and input shaping filter (IFT‐ISF) for industrial motion systems. OILC effectively compensates for tracking errors in repeating tasks under actuator constraints. However, its performance deteriorates when the trajectory changes. In contrast, MFC can achieve high performance for varying trajectory tracking tasks, but its performance may degrade for constrained systems if the control force exceeds the actuator saturation boundary. The proposed algorithm aims to overcome these limitations to achieve optimal trajectory tracking performance for varying trajectories under actuator constraints. Simulation and experimental results demonstrate that the proposed algorithm achieves optimal tracking performance while complying with the actuator constraints. The algorithm provides a data‐driven approach without requiring the tedious process of model identification. By combining the benefits of OILC and IFT‐ISF, the proposed algorithm can achieve high‐performance trajectory tracking for both repeating and varying tasks under actuator constraints, making it suitable for industrial motion systems.

An Adaptive Data-Driven Iterative Feedforward Tuning Approach Based on Fast Recursive Algorithm: With Application to a Linear Motor

The feedforward control can effectively improve the servo performance in applications with high requirements of velocity and acceleration. The iterative feedforward tuning method (IFFT) enables the possibility of both removing the need for prior knowledge of the system plant in model-based feedforward and improving the extrapolation capability for varying tasks of iterative learning control. However, most IFFT methods require to set the number of basis functions in advance, which is inconvenient to the system design. To tackle this problem, an adaptive data-driven IFFT based on a fast recursive algorithm (IFFT-FRA) is developed in this article. Explicitly, based on FRA, the proposed approach can adaptively tune the feedforward structure, which significantly increases the intelligence of the approach. Additionally, a data-based iterative tuning procedure is introduced to achieve the unbiased estimation of parameters optimization in the presence of noise. Comparative experiments on a linear motor confirm the effectiveness of the proposed approach.

Data-driven fractional order feedback and model-less feedforward control of a XY reluctance-actuated micropositioning stage.

This paper proposes a compound data-driven control method to solve the problems of low damping resonance, different dynamic properties, and hysteresis in the large-range compliant micropositioning stage driven by a Maxwell reluctance actuator. First, in order to verify the proposed control algorithm, a reluctance-actuated, XY compliant micropositioning stage is constructed according to the principle of operation of a reluctance actuator. Second, in order to eliminate the influence of complex dynamics on the controller design, a fractional order proportional-integral feedback controller is designed using a data iterative feedback turning algorithm. Third, the finite impulse response feedforward filter is optimized using experimental data, and the on-line inverse estimation of the system frequency response function and its iterative feedforward compensation are carried out to further eliminate the influence of light damping resonance. Finally, the proposed control method is used for tracking the experiment and compared with other methods. The experimental results show that the proposed control method can better meet the requirements of high precision, fast speed, and strong anti-interference ability for large stroke micro/nanopositioning and tracking.

Iterative Feedforward Demodulation and Threshold Detection

Iterative demodulation and decoding has been introduced in 2005 by Colavolpe, Barbieri, and Caire to cope with channels affected by phase noise. However, iterations between the channel decoder and the demodulator increase system's complexity. In this letter, we propose an iterative carrier recovery scheme where the current iteration is based on the hard decisions taken after the previous iteration. The resulting algorithm is easy-to-implement and offers excellent phase noise tracking performance, as demonstrated by the numerical results presented in the letter that are obtained with phase noise generated by linewidths of practical interest and a highly spectral efficient modulation scheme as 256-QAM.

Data-Driven Feedforward Learning With Force Ripple Compensation for Wafer Stages: A Variable-Gain Robust Approach.

To meet the increasing demand for denser integrated circuits, feedforward control plays an important role in the achievement of high servo performance of wafer stages. The preexisting feedforward control methods, however, are subject to either inflexibility to reference variations or poor robustness. In this article, these deficiencies are removed by a novel variable-gain iterative feedforward tuning (VGIFFT) method. The proposed VGIFFT method attains: 1) no involvement of any parametric model through data-driven estimation; 2) high performance regardless of reference variations through feedforward parameterization; and 3) especially high robustness against stochastic disturbance as well as against model uncertainty through a variable learning gain. What is more, the tradeoff in which preexisting methods are subject to between fast convergence and high robustness is broken through by VGIFFT. Experimental results validate the proposed method and confirm its effectiveness and enhanced performance.

Combined Feedforward Control and Disturbance Rejection Control Design for a Wafer Stage: A Data-Driven Approach Based on Iterative Parameter Tuning

This article presents a data-driven algorithm that combines the advantages of iterative feedforward tuning and disturbance rejection control to satisfy the precision requirements and ensure extrapolation capability of wafer scanning. The proposed algorithm differs from pre-existing algorithms in terms of its low requirement of system model, high extrapolation capability for non repetitive trajectory tracking tasks, and high tracking precision. The feedforward controller is tuned based on instrumental variables. It utilizes tracking errors from past iterations to eliminate reference-induced errors without requiring a system model. Meanwhile, the system inverse is approximated during iterative process, and then a disturbance rejection control based on iterative tuning is constructed to compensate for disturbance-induced errors. The proposed algorithm is applied to a wafer stage. The experimental results validate the effectiveness and superiority of the proposed algorithm.

Kalman-Filtering-Based Iterative Feedforward Tuning in Presence of Stochastic Noise: With Application to a Wafer Stage

Iterative feedforward tuning (IFFT) enables high performance for motion systems that perform varying tasks without the need for system models. In this paper, IFFT is employed for a wafer stage to achieve good trajectory tracking performance and excellent disturbance compensation ability. Recently, the instrumental variable (IV) approach has been introduced into IFFT algorithms (IV-IFFT), enabling unbiased estimates for the parameters of a feedforward controller in the presence of stochastic noise. However, the estimation variances achievable with IV-IFFT are larger than zero. The aim of this paper is to develop an IFFT algorithm that enables unbiased estimates with zero asymptotic variances, which can be achieved by the simultaneous use of the Kalman filtering (KF) approach and the IV approach in IFFT, yielding the KF-IV-IFFT algorithm. The different roles of KF and IV approaches to improve the noise-tolerant capability of IFFT are also revealed. Experimental results obtained on a wafer stage confirm the practical relevance of the proposed KF-IV-IFFT algorithm.

Data-Driven Iterative Feedforward Tuning for a Wafer Stage: A High-Order Approach Based on Instrumental Variables

The feedforward controller plays an important role in the achievement of high servo performance of wafer scanning. In this paper, a novel data-driven feedforward tuning method is developed in the presence of noise. Three distinguished features make it different from the existing methods: first, high extrapolation capability to tasks; second, low requirement on the system model; and especially, third, high noise tolerant capability. These superiorities are achieved by a high-order iterative feedforward tuning algorithm based on instrumental variables. It utilizes error data from all past iterations via an integrator in the learning law, yet without the need of the plant model or the sensitivity function. Furthermore, $H_2$ optimization with specified convergence speed constraint is proposed to design the learning gain. Connections and differences between the proposed algorithm and the existing ones are discussed. Experimental results validate the proposed method and confirm its effectiveness and superiority.

Wind tunnel tests with combined pitch and free-floating flap control: data-driven iterative feedforward controller tuning

Abstract. Wind turbine load alleviation has traditionally been addressed in the literature using either full-span pitch control, which has limited bandwidth, or trailing-edge flap control, which typically shows low control authority due to actuation constraints. This paper combines both methods and demonstrates the feasibility and advantages of such a combined control strategy on a scaled prototype in a series of wind tunnel tests. The pitchable blades of the test turbine are instrumented with free-floating flaps close to the tip, designed such that they aerodynamically magnify the low stroke of high-bandwidth actuators. The additional degree of freedom leads to aeroelastic coupling with the blade flexible modes. The inertia of the flaps was tuned such that instability occurs just beyond the operational envelope of the wind turbine; the system can however be stabilised using collocated closed-loop control. A feedforward controller is shown to be capable of significant reduction of the deterministic loads of the turbine. Iterative feedforward tuning, in combination with a stabilising feedback controller, is used to optimise the controller online in an automated manner, to maximise load reduction. Since the system is non-linear, the controller gains vary with wind speed; this paper also shows that iterative feedforward tuning is capable of generating the optimal gain schedule online.

Achieving sub-shot-noise sensing at finite temperatures

We investigate sensing of magnetic fields using quantum spin chains at finite temperature and exploit quantum phase crossovers to improve metrological bounds on the estimation of the chain parameters. In particular, we analyze the $ XX $ spin chain and show that the magnetic sensitivity of this system is dictated by its adiabatic magnetic susceptibility, which scales extensively (linearly) in the number of spins $ N $. Next, we introduce an iterative feedforward protocol that actively exploits features of quantum phase crossovers to enable super-extensive scaling of the magnetic sensitivity. Moreover, we provide experimentally realistic observables to saturate the quantum metrological bounds. Finally, we also address magnetic sensing in the Heisenberg $ XY $ spin chain.

Iterative Feedforward Tuning for Residual Vibration Reduction

An iterative approach for the determination of an input-output inversion feedforward control law for residual vibration reduction is proposed in this paper. In particular, point-to-point motion planning of vibratory servosystems is considered. The method aims at estimating recursively the parameters of the system in order to determine the exact command input to be applied to the control system in order to achieve a predefined motion without oscillations. In this context, a gradient based minimisation of the integrated square error cost function is performed. Simulation results show the effectiveness of the methodology.

Comparison of Different Multivariable Control Design Methods Applied on Half Car Test Setup

The multivariable tracking accuracy on an automotive vibration test rig can be improved by extending the current industrial off-line iterative feedforward procedure with a real time feedback controller. This article compares three MIMO feedback control design procedures with respect to design complexity and obtained performance: (1) DK-iteration, the most common μ-synthesis method to design robust MIMO-controllers, (2) a control design based on static decoupling, which is a combination of a static input – output transformation to decouple the system, and independent SISO-controllers designed for the diagonal elements of the transformed system and (3) an inverse-based control design, which is also based on a decoupling transformation, but the decoupling is performed by a dynamic precompensator. The performance of these controllers is compared experimentally on a half car test setup. The tracking performance of the controller designs based on static or dynamic decoupling is comparable with the performance of the controller based on μ-synthesis. The decoupling-based controller designs, using standard SISO H∞ techniques, are however much simpler and more straightforward.

Multivariable feedback design to improve tracking performance on tractor vibration test rig

The multivariable tracking accuracy on an automotive vibration test rig can be improved by extending the current industrial off-line iterative feedforward procedure with a real-time multiple-input-multiple-output (MIMO) feedback controller. This paper shows that for the considered 4 × 4 application, the two most common approaches, namely full MIMO identification and μ -synthesis on one hand and decentralised control on the other hand fail to design a robust MIMO controller with acceptable performance. Therefore, a control design based on static decoupling is applied. This is a combination of (1) a transformation of the inputs and the outputs to decouple the system, and (2) independent single-input-single-output (SISO) controllers designed for the diagonal elements of the transformed system, using standard SISO identification and H ∞ -techniques. To optimise the closed-loop performance, a new decoupling algorithm is developed. The new presented algorithm outperforms the existing decoupling algorithms, yielding satisfactory multivariable closed-loop performance.

Experimental validation of different MIMO-feedback controller design methods

The multivariable tracking performance of an automotive vibration test rig can be improved by extending the current industrial off-line iterative feedforward procedure with a real-time feedback controller. This paper tests and validates three different MIMO-control schemes on an industrial test rig. Firstly decentralized (or diagonal) control is used, which is a combination of independently designed SISO-controllers based on the diagonal elements of the MIMO-system model. Secondly full MIMO H ∞ -control, based on a MIMO-model is tested. Finally inverse-based control is applied to the test rig, this is a combination of a pre-compensator dealing with the interaction in the system and a decentralized controller, designed for the compensated system. The performance of these controllers is compared experimentally, and the best controller is combined with the off-line feedforward control, showing a significant reduction of the tracking error.