Performance Of Feedback Controller Research Articles

Virtual feedback control of interior road noise based on headrest loudspeakers

This paper analyzes the factors that affect the noise reduction performance of feedback control based on remote microphone technique (RMT) and how these factors can be exploited to improve the control performance. First, simulations were conducted to compare the noise reduction performance of employing feedback control directly at the virtual microphone (located at the ear position) and on the physical microphone (located on the headrest), as well as the RMT-based feedback control system. Then the impact of the delay of the virtual secondary path and the coherence between physical and virtual signals on the noise reduction performance of the RMT-based feedback control system was analyzed. It is found that the noise reduction performance can be improved by reducing the delay of the virtual secondary path or increasing the number of physical microphones. Finally, experiments of a dual-channel control system conducted inside an electric car cabin demonstrate that the feedback control strategy based on RMT achieves a binaural noise reduction of 3.4 dBA when employing 4 physical microphones to estimate sound pressure at the ear positions. This approach achieves similar performance but is more cost-effective than a feedforward system using multiple reference sensors.

Sufficient active control of uncertain low-frequency space micro-vibrations near measurement limit of acceleration sensors

High-precision payloads such as ultra-stable optical clocks for next-generation space missions are greatly sensitive to the low-frequency micro-vibrations induced by various flexible appendages equipped on the spacecraft. The frequencies of such micro-vibrations are uncertain and near the measurement limit of acceleration sensors, leading to insufficient vibration control performance of classical feedback control. To solve this problem, a hybrid control algorithm which combines PI controller and the filter-x least-mean-square (FxLMS) algorithm is proposed in the paper. In particular, a frequency estimation algorithm is designed for the active vibration control system to provide the reference signal. In the new system, the rough estimation of disturbance frequency is given by the adaptive notch filter. Then, the frequency is refined by Kalman filter and provided into the autoregressive model, which fine-tunes the estimated frequency and offers the reference signal for the hybrid PI-FxLMS algorithm. Finally, the experimental prototype of active vibration control system is developed and the proposed control algorithm is adopted. The experimental results demonstrate the effectiveness of the frequency estimation algorithm and further validate that the proposed hybrid PI-FxLMS algorithm can effectively suppress the low-frequency micro-vibrations near the measurement limit of acceleration sensors and with uncertain frequency.

Engineering Fastest Control: A New Process Control Method for Thermal Power Units

The intermittent and volatile nature of wind power generation necessitates thermal power units to provide deep-peak shaving and fast frequency regulation services within the same grid. However, the current proportional integral differential (PID) control performance in the process control foundation of thermal power units falls short of meeting these requirements. Despite the lack of comprehensive research on engineering fastest control (EFC), this article aims to address this gap by studying and analyzing the mechanism and physical defects of the fast tracking filter (FTF), the output tracking input characteristics of EFTF reconstructed by FTF engineering, and the control performance of the constructed EFC. Through mathematical calculation analysis, simulation experiments, and real-world engineering practice, it is concluded that EFC surpasses the limitations of PID control performance and effectively enhances feedback control performance. As a result, it is deemed suitable for process control in thermal power units.

A novel simple disposition index (SPINA-DI) from fasting insulin and glucose concentration as a robust measure of carbohydrate homeostasis.

The widely used dynamic disposition index, derived from oral glucose tolerance testing, is an integrative measure of the homeostatic performance of the insulin-glucose feedback control. Its collection is, however, time consuming and expensive. We, therefore, pursued the question if such a measure can be calculated at baseline/fasting conditions using plasma concentrations of insulin and glucose. A new fasting-based disposition index (structure parameter inference approach-disposition index [SPINA-DI]) was calculated as the product of the reconstructed insulin receptor gain (SPINA-GR) times the secretory capacity of pancreatic beta cells (SPINA-GBeta). The novel index was evaluated in computer simulations and in three independent, multiethnic cohorts. The objectives were distribution in various populations, diagnostic performance, reliability and correlation to established physiological biomarkers of carbohydrate metabolism. Mathematical and in-silico analysis demonstrated SPINA-DI to mirror the hyperbolic relationship between insulin sensitivity and beta-cell function and to represent an optimum of the homeostatic control. It significantly correlates to the oral glucose tolerance test based disposition index and other important physiological parameters. Furthermore, it revealed higher discriminatory power for the diagnosis of (pre)diabetes and superior retest reliability than other static and dynamic function tests of glucose homeostasis. SPINA-DI is a novel simple reliable and inexpensive marker of insulin-glucose homeostasis suitable for screening purposes and a wider clinical application.

Identification Modelling and Fault-Tolerant Predictive Control for Industrial Input Nonlinear Actuator System

Industrial actuator systems play an important role in mechanical manufacture, chemical production and other industrial processes. There is important theoretical research significance and engineering application value in accurately modeling and accurately controlling for an industrial actuator system with dead-zone input nonlinearity. The structure and order of the system are determined by the mechanism relationship of the system. Based on sampled data, an identification algorithm is proposed to describe the main dynamic characteristics of the system output. The convergence property of the proposed identification algorithm is also analyzed. Process faults may reduce the tracking control accuracy of the industrial actuator system. By using an intermediate observer to estimate the faults, a fault-tolerant synchronous control feedback rate is designed to compensate faults. The input dead-zone block may weaken the feedback control performance of the input signal and reduce the control precision. According to the dead-zone input nonlinearity model parameter, a compensator is introduced to transform the dead-zone function into a linear function passing through the origin of coordinates. The transformed and dynamic linear segment of the system constitute the generalized linear system. The model predictive control (MPC) strategy is designed to achieve robust and precise control by eliminating the effects of measurement noise. The results of numerical simulation and experimental test verify the superiority and merit of the modeling and fault-tolerant control strategy. The research results of this paper can provide a good reference and guidance for other complex systems in theoretical research and engineering applications.

Sensorimotor feedback loops are selectively sensitive to reward.

Although it is well established that motivational factors such as earning more money for performing well improve motor performance, how the motor system implements this improvement remains unclear. For instance, feedback-based control, which uses sensory feedback from the body to correct for errors in movement, improves with greater reward. But feedback control encompasses many feedback loops with diverse characteristics such as the brain regions involved and their response time. Which specific loops drive these performance improvements with reward is unknown, even though their diversity makes it unlikely that they are contributing uniformly. We systematically tested the effect of reward on the latency (how long for a corrective response to arise?) and gain (how large is the corrective response?) of seven distinct sensorimotor feedback loops in humans. Only the fastest feedback loops were insensitive to reward, and the earliest reward-driven changes were consistently an increase in feedback gains, not a reduction in latency. Rather, a reduction of response latencies only tended to occur in slower feedback loops. These observations were similar across sensory modalities (vision and proprioception). Our results may have implications regarding feedback control performance in athletic coaching. For instance, coaching methodologies that rely on reinforcement or 'reward shaping' may need to specifically target aspects of movement that rely on reward-sensitive feedback responses.

Data-driven set-point control for nonlinear nonaffine systems

Optimal set-point control is important in maintaining good control performance of practical industrial processes. Considering the challenges when modeling a complex process, this work presents a data-driven optimal set-point control (DDOSC) scheme for nonlinear nonaffine systems bypassing modeling steps. The outer loop of the proposed DDOSC adopts an ideal nonlinear set-point control function which is theoretically achievable to obtain a perfect tracking. Then, a dynamic linearization (DL) is used to transfer the ideal nonlinear set-point control law into a linear parametric one such that it is implementable through a parameter updating law. To address the nonlinear nonaffine system within a data-driven framework, the DL method is again used to obtain its linear data model which is further updated by a parameter-adaptive law. With a proportional feedback controller in the inner loop, the convergence of the DDOSC method is shown. In addition, the results are further extended by considering a proportional-integral-derivative feedback controller in the inner loop. The simulation results on a numerical example and a car suspension system verify the effectiveness of the proposed DDOSC in improving the performance of the local feedback controller.

A Novel Control Strategy to Improve Stability and Performance of a Synchronous Generator Using Jacobian Gain Control

This paper proposes a novel control strategy to improve the stability and performance of the linear feedback control (LC) by alleviating the nonlinearity of a synchronous generator (SG). This control strategy, Jacobian gain control (JGC), uses the Jacobian function (JF), which is the sensitivity between the power imbalance in an electrical power system (EPS) and the state variables of SGs, for control. The JFs composed of a combination of state variables of an SG were established by the Jacobian reflecting physical characteristics of SG control. An integrated controller, JGC, for the SG was constructed by applying the JFs to the models of the governor (GOV), excitation system (EX), and power system stabilizer (PSS), respectively. The effects of JGC were identified by frequency response analysis of a single machine infinite bus (SMIB) model. The comprehensive control performance and stability of JGC were verified through various case-by-case time-domain simulations on two-area and IEEE 39-bus system configured by MATLAB/Simulink.

Development and Testing of a Durable and Novel Breast Phantom for Robotic Autonomous Ultrasound Systems

For the safe and effective development of evolving autonomous medical robotic systems that traverse the surface of the body, like in breast ultrasound scans, developing phantoms that are durable and mechanically mimic human tissue is critical. In this work, a long lasting, inexpensive, and geometrically customizable phantom is described with mechanical and ultrasound acoustic properties that simulate human breast tissue. In comparison to prior work, a priority was designing a highly elastic phantom outer layer modulus 20 kPa and inner semi-liquid layer to mimic the difficulties of traversing human breast tissue with autonomous medical robotic systems. In addition, ultrasound images of the novel phantom with enclosed tumor are similar to in vivo image of human breast tissue with invasive ductal carcinoma, representing 80% of breast cancer cases. The performance of a force feedback controller on an autonomous ultrasound scanning system was compared for the novel phantom and a commercial phantom. Overall, the controller performed worse on the novel phantom — highlighting the importance of testing autonomous systems on realistic phantoms.

Shape Tracking and Feedback Control of Cardiac Catheter Using MRI-Guided Robotic Platform—Validation With Pulmonary Vein Isolation Simulator in MRI

Cardiac electrophysiology is an effective treatment for atrial fibrillation, in which a long, steerable catheter is inserted into the heart chamber to conduct radio frequency ablation. Magnetic resonance imaging (MRI) can provide enhanced intraoperative monitoring of the ablation progress as well as the localization of catheter position. However, accurate and real-time tracking of the catheter shape and its efficient manipulation under MRI remains challenging. In this article, we designed a shape tracking system that integrates a multicore fiber Bragg grating (FBG) fiber and tracking coils with a standard cardiac catheter. Both the shape and positional tracking of the bendable section could be achieved. A learning-based modeling method is developed for cardiac catheters, which uses FBG-reconstructed three-dimensional curvatures for model initialization. The proposed modeling method was implemented on an MRI-guided robotic platform to achieve feedback control of a cardiac catheter. The shape tracking performance was experimentally verified, demonstrating 2.33° average error for each sensing segment and 1.53 mm positional accuracy at the catheter tip. The feedback control performance was tested by autonomous targeting and path following (average deviation of 0.62 mm) tasks. The overall performance of the integrated robotic system was validated by a pulmonary vein isolation simulator with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex-vivo</i> tissue ablation, which employed a left atrial phantom with pulsatile liquid flow. Catheter tracking and feedback control tests were conducted in an MRI scanner, demonstrating the capability of the proposed system under MRI.

Blood–brain barrier opening in a large animal model using closed-loop microbubble cavitation-based feedback control of focused ultrasound sonication

Focused ultrasound (FUS) in combination with microbubbles has been established as a promising technique for noninvasive and localized Blood–brain barrier (BBB) opening. Real-time passive cavitation detection (PCD)-based feedback control of the FUS sonication is critical to ensure effective BBB opening without causing hemorrhage. This study evaluated the performance of a closed-loop feedback controller in a porcine model. Calibration of the baseline cavitation level was performed for each targeted brain location by a FUS sonication in the presence of intravenously injected microbubbles at a low acoustic pressure without inducing BBB opening. The target cavitation level (TCL) was defined for each target based on the baseline cavitation level. FUS treatment was then performed under real-time PCD-based feedback controller to maintain the cavitation level at the TCL. After FUS treatment, contrast-enhanced MRI and ex vivo histological staining were performed to evaluate the BBB permeability and safety. Safe and effective BBB opening was achieved with the BBB opening volume increased from 3.8 ± 0.7 to 53.6 ± 23.3 mm3 as the TCL was increased from 0.25 to 1 dB. This study validated that effective and safe FUS-induced BBB opening in a large animal model can be achieved with closed-loop feedback control of the FUS sonication.

Controller Modeling of a Quadrotor

Dynamic modeling and control are research fields that hake kept the attention of researchers over the last decades. In this paper we describe a detailed approach to model, design and simulate a feedback controller for a quadrotor with the aim of giving the reader a detailed procedure to obtain the dynamic model and link this model with a controller design strategy. For this purpose, the dynamic model of the Parrot AR. Drone 2.0 was obtained using the Newton-Euler formulations. Next, the model was converted to the state space, and it was linearized to get the equations to perform a controller gain estimation process. Finally, the performance of state feedback controller visualized for both the linear and nonlinear models. Results shown that, the challenging goal of stabilizing the quadrotor at a desired trajectory, in short time without overshoot problems, can be achieved by means of a simple control strategy.

Flatness-based feedforward and modal model-predictive state-feedback control of a double pendulum bridge crane

In some industrial crane applications the payload is fixed at a hook by lifting slings such as belts or chains. The payload mass is typically much larger than the hook mass yielding small but high-frequent double pendulum oscillations. The main control goal for such cranes is to actively damp the single pendulum motion. Such double pendulum cranes are often approximated by single pendulum models. However, this may yield control or observation spillover effects. The influence of neglecting the double pendulum motion on feedforward control is analyzed in this paper by simulations. Therefore, a flat parametrization is derived which can be applied to both single and double pendulum cranes by simply adjusting two parameters. Besides, a modal state-feedback controller (in combination with modal state estimation) is suggested to avoid observation spillover effects without the need of damping high-frequent double pendulum oscillations at the same time. The feedback control performance is validated experimentally on a laboratory bridge crane.

Improved tracking performance by H∞ control for an automatic spinach harvester

This study was aimed at enhancing the path-following control performance of the arm length control mechanism in automatic spinach harvesters. Field experiments conducted in existing studies indicated that the root cutting blade path must be controlled to compensate for motion disturbances in the pitch direction to realize successful harvesting. Therefore, methods of path variation compensation via PID control and feedforward control have been established. Notably, using these strategies, the arm length controller cannot follow the corrected target values with an acceptable response because of uncertainties and disturbances. The insufficient performance of the controller deteriorates the harvesting performance. Consequently, the feedback control performance must be enhanced in events involving uncertainties and disturbances. To enhance the path-following performance of the arm length control mechanism, an advanced controller based on the H∞ control theory was designed in this study. The tracking performance and effectiveness of the path compensation method were experimentally evaluated.

Learning and Application of Feedback Path Database for Feedback Control in Hearing Aids

State-of-the-art feedback control systems make use of adaptive filters to model and compensate for the acoustic feedback paths, which vary in different acoustic situations. In a hearing aid application, the acoustic feedback paths are user dependent and vary over time. Although there are repeatable patterns in these variations, it remains to be seen that modern feedback control systems actively learn from and then use these patterns to improve the adaptive filter performance. In this letter, we present a new idea of learning and storing acoustic feedback paths as hearing aid users wear their hearing aids, and then to apply these learned and stored acoustic feedback paths to control and support the adaptive filter updates, thereby to achieve significantly improved feedback control performance.

Optimal sensor and actuator placement for feedback control of vortex shedding

We consider linear feedback control of the two-dimensional flow past a cylinder at low Reynolds numbers, with a particular focus on the optimal placement of a single sensor and a single actuator. To accommodate the high dimensionality of the flow, we compute its leading resolvent forcing and response modes to enable the design of$\mathcal {H}_2$-optimal estimators and controllers. We then investigate three control problems: (i) optimal estimation (OE) in which we measure the flow at a single location and estimate the entire flow; (ii) full-state information control (FIC) in which we measure the entire flow but actuate at only one location; and (iii) the overall feedback control problem in which a single sensor is available for measurement and a single actuator is available for control. We characterize the performance of these control arrangements over a range of sensor and actuator placements and discuss implications for effective feedback control when using a single sensor and a single actuator. The optimal sensor and actuator placements found for the OE and FIC problems are also compared with those found for the overall feedback control problem over a range of Reynolds numbers. This comparison reveals the key factors and conflicting trade-offs that limit feedback control performance.

Performance Bounds for Optimal and Robust Feedback Control in Networks

Important complex networks, including critical infrastructure and emerging industrial automation systems, are becoming increasingly intricate webs of interacting feedback control loops. A fundamental concern is to quantify the control properties and performance limitations of the network as a function of the structure of its dynamics and control architecture. We study performance bounds for networks in terms of both optimal and robust feedback control costs as a function of the system dynamics and actuator structure. For unstable network dynamics, we demonstrate a tradeoff between feedback control performance and the number of control inputs, in particular, showing that optimal cost can increase exponentially with the number of unstable nodes in the network. Likewise, we demonstrate a tradeoff between robustness and the numbers of control and adversarial inputs, which shows that either an increase in the number of unstable nodes or the number of adversarial inputs can make optimal cost increase exponentially or become infinite. We also derive a bound on the performance of the worst-case actuator subset for open-loop stable networks, providing insight into dynamics properties that affect the potential efficacy of actuator selection in terms of measures for how close the open-loop dynamics are to instability. However, we show that such a bound is generally not guaranteed even for open-loop stable networks in terms of robustness to adversarial inputs. When the open-loop network dynamics has a finite <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\infty$</tex-math></inline-formula> norm with respect to the adversarial inputs, we find an analogous performance bound of the worst case actuator set. We illustrate our results with numerical experiments that analyze performance in regular and random networks.

Complete model-free sliding mode control (CMFSMC)

This study presents a complete model-free sliding mode control (CMFSMC) framework for the control of continuous-time non-affine nonlinear dynamic systems with unknown models. The novelty lies in the introduction of two equalities to assign the derivative of the sliding functions, which generally bridges the designs of those model-based SMC and model-free SMC. The study includes a double SMC (DSMC) design, state observer design, and desired reference state vector design (whole system performance), which all do not require plant nominal models. The preconditions required in the CMFSMC are the plant dynamic order and the boundedness of plant and disturbances. U-model based control (U-control) is incorporated to configure the whole control system, that is (1) taking model-free double SMC as a robust dynamic inverter to cancel simultaneously both nonlinearity and dynamics of the underlying plants, (2) taking a model-free state observer to estimate the state vector, (3) taking invariant controller to specify the whole control system performance in a linear output feedback control and to provide desired reference state vector. The related properties are studied to support the concept/configuration development and the analytical formulations. Simulated case studies demonstrate the developed framework and show off the transparent design procedure for applications and expansions.

Adaptive Athlete Training Plan Generation: An intelligent control systems approach

ObjectivesThe planning and control of team sport training activities is an extremely important aspect of athletic development and team performance. This research introduces a novel system which leverages techniques from the fields of control system theory and artificial intelligence to construct optimal future training plans when unexpected disturbances and deviations from a training plan goal occur. DesignSimulation-based experimental design. MethodsThe adaptation of training load prescriptions was formulated as an optimal control problem where we seek to minimize the difference between a desired training plan goal and an observed training outcome. To determine the most suitable approach to optimize future training loads the performance of an artificial intelligence-based feedback controller was compared to random and proportional controllers. Computational simulations (N = 1800) were conducted using a non-linear training plan spanning 60 days over a 12-week period, and the control strategies were assessed on their ability to adapt future training loads when disturbances and deviations from an optimal planning policy have occurred. Statistical analysis was conducted to determine if significant differences existed between the three control strategies. ResultsThe results of a repeated measures analysis of variance demonstrated that an intelligent feedback controller significantly outperforms the random (p < .001, ES = 7.41, very large) and proportional control (p < .001, ES = 7.41, very large) strategies at reducing the deviations from a training plan goal. ConclusionsThis system can be used to support the decision making of practitioners across several areas considered important for the effective planning and adaption of athletic training.

Controllability and state feedback control of a cardiac ionic cell model

A phenomenon called alternans, which is a beat-to-beat alternation in action potential (AP) duration, sometimes precedes fatal cardiac arrhythmias. Alternans-suppressing electrical stimulus protocols are often represented as perturbations to the dynamics of membrane potential or AP duration variables in nonlinear models of cardiac tissue. Controllability analysis has occasionally been applied to cardiac AP models to determine whether different control or perturbation strategies are capable of suppressing alternans or other unwanted behavior. Since almost all previous cardiac controllability studies have focused on low-dimensional models, we conducted the present study to assess controllability of a higher-dimensional model, specifically the Luo Rudy dynamic (LRd) model of a cardiac ventricular myocyte. Higher-dimensional models are of interest because they provide information on the influence of a wider range of measurable quantities, including ionic concentrations, on controllability. After computing modal controllability measures, we found that larger eigenvalues of a linearized LRd model were on average more strongly controllable through perturbations to calcium-ion concentrations compared with perturbations to other variables. When only membrane potential was adjusted, the best time to apply perturbations (in the sense of maximizing controllability of the largest alternans eigenvalue) was near the AP peak time for shorter cycle lengths. Controllability results were found to be similar for both the default model parameters and for an alternans-promoting parameter set. Additionally, we developed several alternans-suppressing state feedback controllers that were tested in simulations. For the scenarios examined, our controllability measures correctly predicted which strategies and perturbation timings would lead to better feedback controller performance.