Feedforward Tuning Research Articles

Data‐driven tuning of feedforward controller structured with infinite impulse response filter via iterative learning control

Iterative learning control (ILC) is an effective approach for tracking control system that performs repeating tasks. However, the performance of ILC is significantly deteriorated when the reference is changed. To obtain high tracking performance for both repeating and varying tasks, a novel data-driven tuning method of feedforward controller structured with infinite impulse response (IIR) filter via ILC is developed in this study. Global optimal parameters of the feedforward controller are obtained by linear least-squares method based on the optimal feedforward control force obtained by ILC, while model information is not required. Additionally, to deal with the possible instability problem of the feedforward controller structured with IIR filter, a stable approximation approach on the basis of zero-phase-error tracking algorithm is presented. The stable approximation approach can convert the approximation problem to a convex optimisation problem. Finally, the proposed approach is compared with the standard ILC and a data-driven feedforward control structured with finite impulse response filter by two simulation studies. Simulation results demonstrate that the proposed data-driven feedforward tuning method can achieve high tracking performance and is insensitive to reference variations.

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.

Batch-to-Batch Rational Feedforward Control: From Iterative Learning to Identification Approaches, With Application to a Wafer Stage

Feedforward control enables high performance for industrial motion systems that perform nonrepeating motion tasks. Recently, learning techniques have been proposed that improve both performance and flexibility to nonrepeating tasks in a batch-to-batch fashion by using a rational parameterization in feedforward control. This paper aims to unify these approaches through a single framework that provides transparent connections and clear differences between the alternatives. Experimental results on an industrial motion system confirm the theoretical findings and illustrate benefits of rational feedforward tuning in motion systems, including preactuation and postactuation.

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.

Robust design methodology for simultaneous feedforward and feedback tuning

This study presents a control design methodology to address simultaneous robust feedforward and feedback tuning. A tuning guideline including tuning rules based on the time-domain response is introduced. Then, an optimisation algorithm is used in the frequency domain to find the optimal feedback–feedforward controller. Detailed simulations covering a wide range of industrial process models are carried out to demonstrate the validity of the proposed methodology and its advantages over other approaches.

Influence of recurrent interactions on texture processing in networks with different visual map organizations

The functional architecture of the visual cortex displays marked differences across mammalian species: in stark contrast to primates, in which the preferred stimulus orientation forms an almost smooth map across the cortical surface, in rodents a 'salt-and-pepper' organization has been observed [1]. It is conceivable that the organization of preferred orientation has an impact on the processing of visual input. Recently [2], we found that in a biologically inspired object recognition system with a pure feed forward network architecture, smooth orientation maps outperform the salt-and-pepper organization in a texture recognition task. Here, we extend this work to study the effect of recurrent connections on neuronal response properties and texture recognition, comparing the two types of cortical architectures. Our model is a recurrent single-layer rate network. The feed forward connections to each neuron are properly oriented Gabor-filters. Recurrent connections between two neurons depend both on the spatial distance between these neurons and on their orientation selectivities. Inspired by [3], the angle-dependent interaction function is weighted by the product of selectivities of both neurons and enables excitation between cells with similar preferred orientations and inhibition between cells with orthogonal ones. We also study the effects of purely distant-dependent recurrent connectivity of Mexican-hat type, with spatial extent related to local column spacing in case of smooth map layout. We design a network for the salt-and-pepper organization in an analogous way, assuming the same spatial extent of connectivity and its tuning-dependence as observed in mouse visual cortex [4]. Varying the strength and selectivity of recurrent versus feed-forward connections, we first explore the influence of recurrence on the orientation selectivity. For that purpose, we drive the network with oriented gratings to reconstruct the selectivity from the activities. In agreement with [5] we observe sharpening of the orientation tuning of the network by recurrent interactions, such that oriented stimuli can be well discriminated even with weak feed-forward tuning. We further study the role of recurrent connections in processing more complex stimuli. We present visual textures to the model, then feed the responses into a classifier (linear SVM) that learns to predict a class label. This allows us to study how differences in feed-forward and recurrent connections impact texture classification, and to compare the orientation-preference map and salt-and-pepper organization in texture recognition tasks.

An intelligent factory-wide optimal operation system for continuous production process

ABSTRACTIn this study, a novel intelligent factory-wide operation system for a continuous production process is designed to optimise the entire production process, which consists of multiple units; furthermore, this system is developed using process operational data to avoid the complexity of mathematical modelling of the continuous production process. The data-driven approach aims to specify the structure of the optimal operation system; in particular, the operational data of the process are used to formulate each part of the system. In this context, the domain knowledge of process engineers is utilised, and a closed-loop dynamic optimisation strategy, which combines feedback, performance prediction, feed-forward, and dynamic tuning schemes into a framework, is employed. The effectiveness of the proposed system has been verified using industrial experimental results.

Processing of Feature Selectivity in Cortical Networks with Specific Connectivity.

Although non-specific at the onset of eye opening, networks in rodent visual cortex attain a non-random structure after eye opening, with a specific bias for connections between neurons of similar preferred orientations. As orientation selectivity is already present at eye opening, it remains unclear how this specificity in network wiring contributes to feature selectivity. Using large-scale inhibition-dominated spiking networks as a model, we show that feature-specific connectivity leads to a linear amplification of feedforward tuning, consistent with recent electrophysiological single-neuron recordings in rodent neocortex. Our results show that optimal amplification is achieved at an intermediate regime of specific connectivity. In this configuration a moderate increase of pairwise correlations is observed, consistent with recent experimental findings. Furthermore, we observed that feature-specific connectivity leads to the emergence of orientation-selective reverberating activity, and entails pattern completion in network responses. Our theoretical analysis provides a mechanistic understanding of subnetworks’ responses to visual stimuli, and casts light on the regime of operation of sensory cortices in the presence of specific connectivity.

Iterative motion feedforward tuning: A data-driven approach based on instrumental variable identification

Feedforward control can significantly enhance the performance of motion systems through compensation of known disturbances. This paper aims to develop a new procedure to tune a feedforward controller based on measured data obtained in finite time tasks. Hereto, a suitable feedforward parametrization is introduced that provides good extrapolation properties for a class of reference signals. Next, connections with closed-loop system identification are established. In particular, instrumental variables, which have been proven very useful in closed-loop system identification, are selected to tune the feedforward controller. These instrumental variables closely resemble traditional engineering tuning practice. In contrast to pre-existing approaches, the feedforward controller can be updated after each task, irrespective of noise acting on the system. Experimental results confirm the practical relevance of the proposed method.

Generalized feedforward tuning rules for non-realizable delay inversion

This paper describes the problem of load disturbance rejection using feedforward for the case when perfect compensation is not realizable due to processes delays inversion. First, a new generalized open-loop design tuning rule for feedforward compensators is proposed. Simple and intuitive guidelines are given for aggressive, moderate, and conservative performance results. Moreover, a switching feedforward compensator is proposed to be used when oscillations are not allowed in the process output. Simulation results show important improvements in disturbance rejection, reference tracking error and control effort.

Leg muscles that mediate stability: mechanics and control of two distal extensor muscles during obstacle negotiation in the guinea fowl

Here, we used an obstacle treadmill experiment to investigate the neuromuscular control of locomotion in uneven terrain. We measured in vivo function of two distal muscles of the guinea fowl, lateral gastrocnemius (LG) and digital flexor-IV (DF), during level running, and two uneven terrains, with 5 and 7 cm obstacles. Uneven terrain required one step onto an obstacle every four to five strides. We compared both perturbed and unperturbed strides in uneven terrain to level terrain. When the bird stepped onto an obstacle, the leg became crouched, both muscles acted at longer lengths and produced greater work, and body height increased. Muscle activation increased on obstacle strides in the LG, but not the DF, suggesting a greater reflex contribution to LG. In unperturbed strides in uneven terrain, swing pre-activation of DF increased by 5 per cent compared with level terrain, suggesting feed-forward tuning of leg impedance. Across conditions, the neuromechanical factors in work output differed between the two muscles, probably due to differences in muscle-tendon architecture. LG work depended primarily on fascicle length, whereas DF work depended on both length and velocity during loading. These distal muscles appear to play a critical role in stability by rapidly sensing and responding to altered leg-ground interaction.

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.

Online neural identification of multi-input multi-output systems

A feedforward neural network tuning algorithm is developed, which is suitable for identification of multi-input multi-output nonlinear functions, by utilising the learning method of a conventional neuro-adaptive control technique. Using Lyapunov functions, it is shown that not only the approximation error converges to values that have arbitrarily reducible upper bounds, but also the weights of the neural network remain bounded. The effectiveness of the identification method and its application in force-control of an uncertain robot interacting with an unknown flexible environment are investigated as an application example.

On a New Six Degree of Freedom Modelling Method for Homing Missiles and its Application for Design/Analysis

Development of an accurate six-degree-of-freedom (6-DOF) simulation model for homing missiles incorporating seeker servosystem detailed modelling is reported. A new modelling concept for seeker servosystem simulation, the Newtonian equivalent model (NEM) has been evolved, where body motion coupling is modelled through forces and moments transformed to the seeker. The NEM-based modelling of seeker head and its integration in 6-DOF has been discussed. In the presence of body coupling, the simulation model for seeker tracking or pointing error, gimbal angles and inertial line of sight (LOS) rates as measured by the seeker mounted rate gyros have been obtained through the new modelling method. A novel method of obtaining optimum pitch and yaw LOS rates for proportional navigation (PN) guidance mechanisation is formulated based on synthesised sight line rates measured in the seeker inner gimbal axis. This new method has been validated through simulation studies using the 6-DOF model developed and by comparing the results with those obtained by the conventional method of generating LOS rates for PN guidance. Other important applications of the 6-DOF model discussed are guidance and control design validation/tuning seeker feedforward compensation design tuning of switchover point from open-loop to closed-loop PN guidance. Importance of the detailed 6-DOF simulation model as the ultimate performance evaluation tool for the weapon system in terms of both terminal performance and adequacy of seeker field of view, gimbal angle freedom, etc. has been brought out.