Existence Of Feedback Controller Research Articles

Performance similarities between standard and retrofit LiDAR-assisted control for wind turbines

LiDAR-assisted control has proven to be a highly effective method for mitigating rotor speed deviations and reducing loads in wind turbines. This effectiveness stems from the ability of feedforward controllers to utilize incoming wind speed information obtained from LiDARs, enabling advanced blade pitch actions before the wind disturbs the turbine. However, the standard implementation of LiDAR-assisted control often necessitates modifications to the existing feedback controller, where the feedforward pitch rate is typically integrated into the feedback controller. This process can be challenging in terms of accessibility and may be limited to specific stakeholders, such as turbine manufacturers.A retrofit design provides an ideal solution, where the retrofit LiDAR-assisted controller modifies the rotor speed measurement to induce pitch actions without requiring alterations to the existing feedback controller. This paper aims to demonstrate the performance similarities between the standard LiDAR-assisted controller and its retrofit counterpart. Specifically, we establish that the retrofit LiDAR-assisted controller, with appropriate tuning, is equivalent to its standard counterpart. This equivalence implies that architecturally dissimilar controllers can yield the same performance in terms of rotor speed deviations and tower load reductions. The presented findings are supported by results obtained from high-fidelity closed-loop turbine simulations.

Note on control for hybrid stochastic systems by intermittent feedback rooted in discrete observations of state and mode with delays

<abstract> <p>For a hybrid stochastic system, most existing feedback controllers need to observe modes at continuous times, which is feasible when the system's mode is observable and does not incur any cost. However, in most cases, the mode is not readily apparent, and identifying it always incurs a certain expense. Therefore, in order to reduce control costs, when designing a feedback controller, both the state and the mode should be observed at discrete moments. This paper introduces an intermittent feedback controller for stabilizing an unstable hybrid stochastic system through discrete delayed observations of state and mode. By utilizing M-matrix theory, intermittent control approach, and the comparison principle, we propose sufficient conditions for the stabilization theory of hybrid stochastic systems. An illustrative example is taken to validate the proposed theory.</p> </abstract>

Adaptive Sliding Mode Feedback Control Algorithm for a Nonlinear Knee Extension Model

Functional electrical stimulation (FES) has been widely used to treat spinal cord injury (SCI) patients. Many research studies employ a closed-loop FES system to monitor the stimulated muscle response and provide a precise amount of charge to the muscle. However, most closed-loop FES devices perform poorly and sometimes fail when muscle nonlinearity effects such as fatigue, time delay response, stiffness, spasticity, and subject change happen. The poor performance of the closed-loop FES device is mainly due to discrepancies in the feedback control algorithms. Most of the existing feedback control algorithms were not designed to adapt to new changes in patients with different nonlinearity effects, resulting in early muscle fatigue. Therefore, this research proposes an adaptive sliding mode (SM) feedback control algorithm that could adapt and fine-tune internal control settings in real-time according to the current subject’s nonlinear and time-varying muscle response during the rehabilitation (knee extension) exercise. The adaptive SM feedback controller consists mainly of system identification, direct torque control, and tunable feedback control settings. Employing the system identification unit in the feedback control algorithm enables real-time self-tuning and adjusting of the SM internal control settings according to the current patient’s condition. Initially, the patient’s knee trajectory response was identified by extracting meaningful information, which included time delay, rise time, overshoot, and steady-state error. The extracted information was used to fine-tune and update the internal SM control settings. Finally, the performance of the proposed adaptive SM feedback control algorithm in terms of system response time, stability, and rehabilitation time was analysed using a nonlinear knee model. The findings from the simulation results indicate that the adaptive SM feedback controller demonstrated the best control performance in accurately tracking the desired knee angle movement by having faster response times, smaller overshoots, and lower steady-state errors when compared with the conventional SM across four reference angle settings (20°, 30°, 40°, and 76°). The adaptive feedback SM controller was also observed to compensate for muscle nonlinearities, including fatigue, stiffness, spasticity, time delay, and other disturbances.

Intelligent train stopping control for railways: A deep learning approach

AbstractStation parking accuracy is an important indicator for the automatic control of high‐speed trains; however, it is subject to many influencing factors, such as the characteristics of high nonlinearity and large time delays in the train control model, time‐variant humidity, and uncertain weather conditions, leading to unsatisfying performance with existing feedback control algorithms. This study first proposes an intelligent train stopping control approach based on deep learning for high‐speed railways. By collecting a large amount of historical train operation data from the Beijing–Shenyang high‐speed railway, three data‐driven models are developed for train stopping control. The first model is based on a deep‐layered feedforward neural network (DNN), which predicts the exact train stopping position with dynamic train running states (position, velocity etc.) as input. Taking advantage of the physical train control models used in practice, the DNN to a convolutional neural network (CNN) is then improved by customizing the convolutional layers of the CNN. To overcome the issues arising from the incompleteness of data samples, a few‐shot convolutional neural network (FSCNN) is further developed to enhance the prediction performance of the CNN. Compared with that of the current method used in practice, the simulation experiments show that the train station parking error can be decreased by 38.6%, 42.8%, and 49.2% by our developed DNN, CNN, and FSCNN, respectively.

Hybrid MPC-P controller for the core power control system at TRIGA reactor

The Training, Research, Isotopes, General Atomics (TRIGA) research nuclear reactor is utilized in a wide range nuclear applications and has three versions, Mark I, Mark II, and Mark III. The TRIGA reactor that is available in Malaysia is a Mark II type, Reaktor TRIGA PUSPATI (RTP). The core power control system in the RTP is designed based on many components including the Feedback Control Algorithm (FCA), and the control rod selection algorithm (CRSA). The CRSA is used in the RTP operation to provide external control to the reactor core reactivity. Even though the CRSA provide stability to the reactor operation, it has relatively complex software structure which produce delayed response time for the control rods selection. Thus, the Single Control Absorbing Rod (SCAR) algorithm is designed to improve the reactor performance by minimizing the time response for the rods selection. However, the SCAR yields a non-constant tracking performance due to the highly non-linear dynamic of the control rods. Thus, to compensate the drawbacks of the SCAR with the existing FCA controller, a hybrid controller is proposed based on the integration of a linear model predictive control (MPC) and Proportional (P) controller named as Hybrid MPC-P-SCAR. The MPC application in the SCAR enable the controller to handle multiple constraints through built-in prediction ability to select the best sequence of future control rod velocity during transient state. Whereas at the steady-state, the MPC is optimized by a scheduled P control switches within the allowable range of power variation. The performance of the proposed Hybrid MPC-P-SCAR, the MPC-SCAR and the existing Feedback Control Algorithm with SCAR (FCA-SCAR) is compared via simulation and evaluated in terms of tracking performance, workload of the control rod drive mechanism (CRDM), and the offset elimination in the unmeasured disturbance. Overall, the Hybrid MPC-P-SCAR controller reduces the settling time by 45% and the steady-state error by 61% of the nominal value compared to the FCA-SCAR.

Time-Optimal Current Control of Synchronous Motor Drives

We are concerned with the problem of fast and accurate tracking of currents in the general synchronous drive. The problem becomes complicated with decreasing available voltage, which is common in high-speed and field weakening regimes. The existing time-optimal controllers rely on a simplified model, ignoring stator resistance and differences in inductances. We derive a solution for the general model considering all parameters and show how the parameters affect the current trajectory. One simplifying assumption had to be made, but we show in simulation that it has a negligible impact on accuracy. The simplification allowed for the design of a feed-forward controller that has a low computational cost and can be easily implemented in realtime. We provide experimental validation of the controller on the developed IPMSM drive prototype of the rated power of 4.5 kW using conventional industrial DSP. The controller is compared to conventional PI and deadbeat solutions, demonstrating that the time-optimal controller can reach the required setpoint four times faster than the competitors at the field weakening regime of the drive. The proposed feed-forward control can be seen as a universal building block that can be combined with existing feedback controllers and observers and thus incorporated into existing control solutions.

A deep learning solution to the marginal stability problems of acoustic feedback systems for hearing aids

For hearing aids, it is critical to reduce the acoustic coupling between the receiver and microphone to ensure that prescribed gains are below the maximum stable gain, thus preventing acoustic feedback. Methods for doing this include fixed and adaptive feedback cancellation, phase modulation, and gain reduction. However, the behavior of hearing aids in situations where the prescribed gain is only just below the maximum stable gain, called here "marginally stable gain," is not well understood. This paper analyzed marginally stable systems and identified three problems, including increased gain at frequencies with the smallest gain margin, short whistles caused by the limited rate of decay of the output when the input drops, and coloration effects. A deep learning framework, called deep marginal feedback cancellation (DeepMFC), was developed to suppress short whistles, and reduce coloration effects, as well as to limit excess amplification at certain frequencies. To implement DeepMFC, many receiver signals in closed-loop systems and corresponding open-loop systems were simulated, and the receiver signals of the closed-loop and open-loop systems were paired together to obtain parallel signals for training. DeepMFC achieved much better performance than existing feedback control methods using objective and subjective measures.

On LiDAR-assisted wind turbine retrofit control and fatigue load reductions

The use of upstream wind speed measurement has motivated the development of LiDAR-assisted control for enhancing rotor speed tracking and fatigue structural load reductions. However, conventional LiDAR-assisted control designs often require altering the existing controller architecture, for example, by sending an additional feed-forward pitch angle signal to the pitch actuator or by incorporating the feed-forward pitch rate into the feedback controller. This work proposes LiDAR-assisted retrofit control solutions that can be implemented without any prior knowledge or modifications of the existing feedback controller. Specifically, the feed-forward pitch action is provoked by modifying the rotor speed measurement. Three retrofit methods are proposed according to the level of retrofit requirement and complexity. Numerical simulation results showed that all three LiDAR-assisted retrofit solutions could achieve good thrust-related load reductions. Thus, the proposed LiDAR-assisted retrofit solutions present a simple control upgrade to extend the lifetime of existing turbines.

Design of an FPGA-Based Fuzzy Feedback Controller for Closed-Loop FES in Knee Joint Model.

Functional electrical stimulation (FES) device has been widely used by spinal cord injury (SCI) patients in their rehab exercises to restore motor function to their paralysed muscles. The major challenge of muscle contraction induced by FES is early muscle fatigue due to the open-loop stimulation strategy. To reduce the early muscle fatigue phenomenon, a closed-loop FES system is proposed to track the angle of the limb’s movement and provide an accurate amount of charge according to the desired reference angle. Among the existing feedback controllers, fuzzy logic controller (FLC) has been found to exhibit good control performance in handling complex non-linear systems without developing any complex mathematical model. Recently, there has been considerable interest in the implementation of FLC in hardware embedded systems. Therefore, in this paper, a digital fuzzy feedback controller (FFC) embedded in a field-programmable gate array (FPGA) board was proposed. The digital FFC mainly consists of an analog-to-digital converter (ADC) Data Acquisition and FLC sub-modules. The FFC was designed to monitor and control the progress of knee extension movement by regulating the stimulus pulse width duration to meet the target angle. The knee is expected to extend to a maximum reference angle setting (70°, 40° or 30°) from its normal position of 0° once the stimulus charge is applied to the muscle by the FES device. Initially, the FLC was modelled using MATLAB Simulink. Then, the FLC was hardcoded into digital logic using hardware description language (HDL) Verilog codes. Thereafter, the performance of the digital FLC was tested with a knee extension model using the HDL co-simulation technique in MATLAB Simulink. Finally, for real-time verification, the designed digital FFC was downloaded to the Intel FPGA (DE2-115) board. The digital FFC utilized only 4% of the total FPGA (Cyclone IV E) logic elements (LEs) and required 238 µs to regulate stimulus pulse width data, including 3 µs for the FLC computation. The high processing speed of the digital FFC enables the stimulus pulse width duration to be updated every stimulation cycle. Furthermore, the implemented digital FFC has demonstrated good control performance in accurately controlling the stimulus pulse width duration to reach the desired reference angle with very small overshoot (1.4°) and steady-state error (0.4°). These promising results are very useful for a real-world closed-loop FES application.

Feedback control modulation for controlling chaotic maps

Based on existing feedback control methods such as OGY and Pyragas, alternative new schemes are proposed for stabilization of unstable periodic orbits of chaotic and hyperchaotic dynamical systems by suitable modulation of a control parameter. Their performances are improved with respect to: (i) robustness, (ii) rate of convergences, (iii) reduction of waiting time, (iv) reduction of noise sensitivity. These features are analytically investigated, the achievements are rigorously proved and supported by numerical simulations. The proposed methods result successful for stabilizing unstable periodic orbits in some classical discrete maps like 1-D logistic and standard 2-D Hénon, but also in the hyperchaotic generalized n-D Hénon-like maps.

Active microphonics noise suppression based on DOB control in 166.6-MHz superconducting cavities for HEPS

Superconducting 166.6-MHz cavities will be used to accelerate electron beams in high-energy photon source (HEPS). The radio-frequency (RF) fields inside these cavities have to be controlled better than 0.03% (rms error) for the amplitude and 0.03 $$^\circ $$ (rms error) for the phase. Adopting a quarter-wave geometry with $$\beta =1$$ , the 166.6-MHz cavity has two intrinsic mechanical modes at $$\sim $$ 100 Hz observed in both simulations and cryogenic tests. If coupled to external vibrations, these microphonics modes shall stress the existing proportional–integral (PI) feedback controller and inevitably deteriorate the field stabilities. Therefore, additional noise suppression may be required. A digital low-level RF system previously in-house developed was connected to a 166.6-MHz dressed cavity at room temperature in the laboratory. Piezo-tuners were used to “knock” on the cavity at various frequencies to excite cavity vibrations, and microphonics spectrum was subsequently measured. A disturbance observer (DOB)-based algorithm was adopted and integrated into the existing feedback controller. The performance of PI controller, DOB controller and a combination of PI and DOB controller was compared. The limitation of the DOB controller was also examined. The PI controller was proved to be insufficient in suppressing large cavity microphonics during the tests. By adding the DOB controller, the excellent field stabilities can be restored. Optimized loop parameters were obtained. The simple first-order filter was adequate thanks to the robustness of the DOB controller. This constitutes a first laboratory demonstration of the active microphonics noise suppression in the 166.6-MHz RF cavity for HEPS.

Modular Model Predictive Control upon an Existing Controller

The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem; the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples.

Error-Based Feedforward Control for a Charge-Coupled Device Tracking System

High control bandwidth is usually restricted in a finite sampling rate of a charge-coupled device (CCD)-based tracking loop, which hinders a good closed-loop performance. Conventionally, a rate feedforward controller is usually used to improve the control performance; however, it is restricted by the line-of-sight (LOS) rate, which is required to be estimated due to only the LOS error available in the CCD-based tracking control system. Besides that, it is also affected by the inverse of the control model. A new error-based feedforward (EBF) control mode is proposed to overcome these difficulties. Due to being plugged into an existing feedback control loop, the closed-loop performance of the CCD-based control system can be minimized by optimizing a Q-filter. The closed-loop stability and robustness with this EBF control system are investigated on the condition of the gain margin and the phase margin of the open-loop transfer function. Furthermore, this paper proposes a new criterion to enhance the robustness of the EBF control system and reveals the sensitivity function property. Because of not being restricted by an accurate model, this new feedforward controller cannot lead to a compromise between error attenuation and noise propagation in the control loop. Simulations and experiments are both used to testify the effectiveness of the improved control mode.

Adaptive fuzzy fault compensation tracking control for uncertain nonlinear systems with multiple sensor faults

This paper investigates the problem of fault-compensation tracking control for a class of uncertain nonlinear strict-feedback systems. The presence of multiple sensor faults leads to that the existing feedback control based on perfect measurement will be completely invalid and cannot achieve the desired tracking control. To solve this problem, a new-type control strategy is proposed. A cubic absolute-value Lyapunov stability theory is used to online estimating the fault information, and an adaptive signal compensation mechanism is proposed to eliminate the adverse effects caused by the sensor faults. It is proved that by applying the presented method, both the tracking performance and the boundedness of closed-loop signals can be guaranteed. Finally, simulation results are given to illustrate the established theoretical findings intuitively.

Reduced Capacitance Battery Storage DC-Link Voltage Regulation and Dynamic Improvement Using a Feedforward Control Strategy

This paper proposes a feedback/feedforward control strategy to attenuate the dc-link voltage variations in a reduced-capacitor battery energy storage system. It also identifies the impact of dc-link voltage variations on a battery energy storage system's response dynamics and power capabilities. It shows that addition of a feedforward controller to the existing feedback controller improves the dc-link voltage regulation and consequently the response dynamics of the reduced-capacitor battery energy storage system. It formulates a power setpoint change as a disturbance to the battery energy storage control system. The battery energy storage system is analyzed using an averaged-value modeling technique. Small-signal analysis is then used to tune feedforward controller parameters. The performance of the proposed controller is evaluated via an electromagnetic transient software (PSCAD/EMTDC), and the findings are experimentally validated using a laboratory-sized test bench.

Exponential Stability and Robust H∞ Control for Discrete-Time Time-Delay Infinite Markov Jump Systems

In this paper, exponential stability and robust H∞ control problem are investigated for a class of discrete-time time-delay stochastic systems with infinite Markov jump and multiplicative noises. The jumping parameters are modeled as an infinite-state Markov chain. By using a novel Lyapunov-Krasovskii functional, a new sufficient condition in terms of matrix inequalities is derived to guarantee the mean square exponential stability of the equilibrium point. Then some sufficient conditions for the existence of feedback controller are presented to guarantee that the resulting closed-loop system has mean square exponential stability for the zero exogenous disturbance and satisfies a prescribed H∞ performance level. Numerical simulations are exploited to validate the applicability of developed theoretical results.

Vibration suppression of rotating long flexible mechanical arms based on harmonic input signals

The industrial long flexible arms usually have nonuniform cross sections and complex structures, leading to the difficulty of establishing accurate models. Moreover, the delay of dynamic response leads to the complexity of the state test and control. Correspondingly, the rotating residual vibration is difficult to control using existing feedback control methods. To this end, a feedforward strategy is presented in this paper to suppress the residual vibration of long flexible mechanical arms. Based on the Lagrange equation, an equivalent rotational flexible dynamic model is established. The model approximately describes the dynamic characteristics of long flexible arms, which exploits the existing mathematical foundation and extends it from the laboratory validation to the industrial application. It can be found that the starting and stopping signals are closely related with the residual vibration. According to harmonic response analysis, the input signals during the starting and stopping process are refined by using weighted Fourier series so as to suppress the residual vibration without considering input shaping filters. The performance is tested in the real industrial equipment that has long flexible arms, which validate the effectiveness of the proposed method.

Youla–Kucera Parameterization-Based Optimally Closed-Loop Control for Tip–Tilt Compensation

In this paper, we develop a new method based on Youla–Kucera parameterization in frequency domain to reduce tip and tilt vibrations of astronomical telescopes. This proposed method can be implemented in real time and can also achieve optimal correction when frequencies of vibrations are provided. Based on this parameterization, the closed-loop stability is easily assured, which can relax the stability condition to design control parameters. Due to being plugged into an existing feedback control loop, the sensitivity function of the tip–tilt mirror control system can be minimized by optimizing Q-filter for adapting the changes of vibrations. Two types of Q-filter are introduced to deal with low-frequency and large-magnitude narrowband vibrations. Because of not being restricted by an accurate model of tip–tilt mirror, this improved control mode cannot lead to a compromise between vibrations rejection and noise propagation in the control loop. Although on-line identification of disturbances models is not required, finding the vibration frequencies is crucial. Simulations and experiments demonstrate that the Youla–Kucera controller has a clear improvement of the closed-loop performance in comparison with the original method.

Friction parameter identification and compensation using the ElastoPlastic friction model

This paper presents the application of a model-based feedforward friction compensation system based on the ElastoPlastic friction model. This nonlinear feedforward compensator, in combination with a linear feedback controller, is applied to a linear drive axis with recirculating ball bearings and a fine-positioning axis with crossed roller bearings. The developed friction compensator estimates the state of the ElastoPlastic friction model and thus the friction force. The compensator also adapts to local variations and variations over time due to wear. This feedforward system serves as an add-on friction compensation system and can be used with existing feedback control systems. Both linear axes are used in an optical measurement setup based on a modified Mahr MFU 100 measuring machine. The designed and implemented control system permits high-accuracy trajectory tracking control even for small velocities such as typical measurement velocities in surface metrology. The parameter identification procedure used for the identification of the ElastoPlastic friction model parameters is explained in detail. The trajectory tracking for the regarded hardware setups with and without friction compensation is compared in experiments, illustrating the performance of this feedforward system. Although the presented work was motivated by the mentioned measurement application, the presented modelling, identification and compensation techniques are not limited to this application and can be transferred to many mechanical systems with friction.

Preview predictive control layer design based upon known wind turbine blade‐pitch controllers

AbstractThe use of upstream wind measurements has motivated the development of blade‐pitch preview controllers for improving rotor speed tracking and structural load reduction beyond that achievable via conventional feedback control. Such preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, alter the closed‐loop dynamics of the existing blade‐pitch feedback control system. This can result in a deterioration of the robustness properties and performance of the existing feedback control system. Furthermore, performance gains from utilising the upcoming real‐time measurements cannot be easily distinguished from the feedback control, making it difficult to formulate a clear business case for the use of preview control. Therefore, the aim of this work is to formulate a modular MPC layer on top of a given output‐feedback blade‐pitch controller, with a view to retaining the closed‐loop robustness and frequency‐domain performance of the latter. The separate nature of the proposed controller structure enables clear and transparent quantification of the benefits gained by using preview control, beyond that of the underlying feedback controller. This is illustrated by results obtained from high‐fidelity closed‐loop turbine simulations, showing the proposed control scheme incorporating knowledge of the oncoming wind and constraints achieved significant 43% and 30% reductions in the rotor speed and flap‐wise blade moment standard deviations, respectively. Additionally, the chance of constraint violations on the rotor speed decreased remarkably from 2.15% to 0.01%, compared to the nominal controller. Copyright © 2017 John Wiley & Sons, Ltd.