Showing papers on "Feedback loop published in 2021"

Fractional Sliding Mode Control for Micro Gyroscope Based on Multilayer Recurrent Fuzzy Neural Network

Abstract: In this paper, an approximation-based adaptive fractional sliding mode control scheme is proposed, where a double loop recurrent fuzzy neural network (DLRFNN) is employed to approximate system uncertainties and disturbance. A fractional order term is incorporated into sliding surface that could add an extra degree of freedom, and combine the advantages of fractional calculus and sliding mode control. A new four-layer FNN is studied, which has two feedback loops (internal feedback loop and external feedback loop) to capture the weights and output signal calculated in the previous step, and use it as a feedback signal for the next step. On the one hand, the proposed DLRFNN structure combines the fuzzy system to process uncertain information with the neural network to learn from the process. On the other hand, both the internal state information and output signal are acquired and stored so that better approximation performance is obtained compared to regular FNN system. Furthermore, the adaptive law of DLRFNN parameters is derived, which can automatically update free parameters with bound. Finally, the effectiveness of the proposed adaptive fractional SMC using DLRFNN strategy is identified by simulations analysis with different fractional orders, whereby tracking errors are uniformly ultimately bounded. The proposed adaptive fractional SMC using DLRFNN strategy can achieve remarkably superior tracking performance in terms of high-precision and fast-response by comprehensive comparisons.

Learning With Context Feedback Loop for Robust Medical Image Segmentation

Abstract: Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixel-wise objective function. However, this approach can lead to less output pixel interdependence producing incomplete and unrealistic segmentation results. In this paper, we present a fully automatic deep learning method for robust medical image segmentation by formulating the segmentation problem as a recurrent framework using two systems. The first one is a forward system of an encoder-decoder CNN that predicts the segmentation result from the input image. The predicted probabilistic output of the forward system is then encoded by a fully convolutional network (FCN)-based context feedback system. The encoded feature space of the FCN is then integrated back into the forward system’s feed-forward learning process. Using the FCN-based context feedback loop allows the forward system to learn and extract more high-level image features and fix previous mistakes, thereby improving prediction accuracy over time. Experimental results, performed on four different clinical datasets, demonstrate our method’s potential application for single and multi-structure medical image segmentation by outperforming the state of the art methods. With the feedback loop, deep learning methods can now produce results that are both anatomically plausible and robust to low contrast images. Therefore, formulating image segmentation as a recurrent framework of two interconnected networks via context feedback loop can be a potential method for robust and efficient medical image analysis.

Closed-Loop Range-Based Control of Dual-Stage Nanopositioning Systems

Abstract: In this article, a closed-loop control framework for dual-stage nanopositioning systems is presented that allows the user to allocate control efforts to the individual actuators based on their range capabilities. Recent work by the authors has focused on range-based control of dual-stage actuators implemented as a prefilter, which assumes that each individual actuator has sensor feedback enabling them to be controlled separately. This article seeks to address the problem of range-based control of dual-stage systems when sensor measurements are only available from the total output of the system, a commonly encountered design. This is a significant departure from previous work since the range-based filter is included in the dual-stage system feedback loop and stability becomes a concern. In this article, the controller is presented, stability conditions are determined, and imaging experiments are performed on an atomic force microscope. Tracking results show that the root-mean-square tracking error for various triangular reference trajectories is improved with the presented range-based control structure by up to 50% compared to frequency-based methods.

Learning With Context Feedback Loop for Robust Medical Image Segmentation

Abstract: Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixel-wise objective function. However, this approach can lead to less output pixel interdependence producing incomplete and unrealistic segmentation results. In this paper, we present a fully automatic deep learning method for robust medical image segmentation by formulating the segmentation problem as a recurrent framework using two systems. The first one is a forward system of an encoder-decoder CNN that predicts the segmentation result from the input image. The predicted probabilistic output of the forward system is then encoded by a fully convolutional network (FCN)-based context feedback system. The encoded feature space of the FCN is then integrated back into the forward system's feed-forward learning process. Using the FCN-based context feedback loop allows the forward system to learn and extract more high-level image features and fix previous mistakes, thereby improving prediction accuracy over time. Experimental results, performed on four different clinical datasets, demonstrate our method's potential application for single and multi-structure medical image segmentation by outperforming the state of the art methods. With the feedback loop, deep learning methods can now produce results that are both anatomically plausible and robust to low contrast images. Therefore, formulating image segmentation as a recurrent framework of two interconnected networks via context feedback loop can be a potential method for robust and efficient medical image analysis.

Controlling self-excited vibration of a nonlinear beam by nonlinear resonant velocity feedback with time-delay

Abstract: In this paper, the efficacy of velocity feedback based nonlinear resonant controller is proposed to control the free and forced self-excited vibration of a nonlinear beam The velocity signal obtained from the sensor is fed through a second-order filter and the nonlinear function of the derivative of the filter variable is used to obtain the control force The resulting control system being a band-pass one, is believed to be superior to other resonant control schemes like Positive Position Feedback (PPF) control and Acceleration Feedback Control (AFC) To the best of the authors’ knowledge, it is the first attempt to explore the efficacy of resonant nonlinear velocity feedback controller for mitigating self-excited vibration Analytical results are obtained using multiple time-scale method, are validated with the data obtained from direct numerical simulation carried out in Matlab Simulink model Analytical results of the uncontrolled system show the periodic, quasi-periodic as well as chaotic behavior It is observed that the proposed controller can reduce the amplitude of vibration by a significant level near primary resonance Moreover, the Hopf bifurcations are eliminated by the control action It is also observed that the proposed controller can reduce the amplitude of the chaotic oscillation significantly The effect of the actuation delay in the feedback loop is also explored It is observed that the effect of time delay is detrimental to system performance

Optimal PI–PD Controller Design for Pure Integrating Processes with Time Delay

Abstract: Though Proportional-Integral-Derivative (PID) controllers are commonly being used for process control applications, it has been proven that they may give unacceptable closed loop responses for open loop unstable processes including integrating ones. Hence, this paper addresses to tuning of PI–PD controllers which is an extension of PID controllers and uses PD part in an inner feedback loop to convert the open loop unstable processes to a stable one so that PI controller in the forward path can be used to achieve a better closed loop response. PI–PD tuning parameters are determined from simple analytical rules which were obtained from minimization of the control system error based on IST3E criterion which is an integral performance index and has been proven to be resulting in very satisfactory closed loop responses. Derived tuning rules are in terms of the assumed process transfer function parameters, namely the gain and time delay. Effectiveness and superiority of obtained tuning rules have been shown by simulation examples.

Novel nondelay-based reservoir computing with a single micromechanical nonlinear resonator for high-efficiency information processing.

Abstract: Reservoir computing is a potential neuromorphic paradigm for promoting future disruptive applications in the era of the Internet of Things, owing to its well-known low training cost and compatibility with hardware. It has been successfully implemented by injecting an input signal into a spatially extended reservoir of nonlinear nodes or a temporally extended reservoir of a delayed feedback system to perform temporal information processing. Here we propose a novel nondelay-based reservoir computer using only a single micromechanical resonator with hybrid nonlinear dynamics that removes the usually required delayed feedback loop. The hybrid nonlinear dynamics of the resonator comprise a transient nonlinear response, and a Duffing nonlinear response is first used for reservoir computing. Due to the richness of this nonlinearity, the usually required delayed feedback loop can be omitted. To further simplify and improve the efficiency of reservoir computing, a self-masking process is utilized in our novel reservoir computer. Specifically, we numerically and experimentally demonstrate its excellent performance, and our system achieves a high recognition accuracy of 93% on a handwritten digit recognition benchmark and a normalized mean square error of 0.051 in a nonlinear autoregressive moving average task, which reveals its memory capacity. Furthermore, it also achieves 97.17 ± 1% accuracy on an actual human motion gesture classification task constructed from a six-axis IMU sensor. These remarkable results verify the feasibility of our system and open up a new pathway for the hardware implementation of reservoir computing.

Unconditional mechanical squeezing via backaction-evading measurements and nonoptimal feedback control

Abstract: Backaction-evading (BAE) measurements of a mechanical resonator, by continuously monitoring a single quadrature of motion, can achieve precision below the zero-point uncertainty. When this happens, the measurement leaves the resonator in a quantum squeezed state. The squeezed state so generated is however conditional on the measurement outcomes, while for most applications it is desirable to have a deterministic, i.e., unconditional, squeezed state with the desired properties. In this work we apply feedback control to achieve deterministic manipulation of mechanical squeezing in an optomechanical system subject to a continuous BAE measurement. We study in details two strategies, direct (Markovian) and state-based (Bayesian) feedback. We show that both are capable to achieve optimal performances, i.e., a vanishing noise added by the feedback loop. Moreover, even when the feedback is restricted to be a time-varying mechanical force (experimentally friendly scenario) and an imperfect BAE regime is considered, the ensuing non-optimal feedback may still obtain significant amount of squeezing. In particular, we show that Bayesian feedback control is nearly optimal for a wide range of sideband resolution. Our analysis is of direct relevance for ultra-sensitive measurements and quantum state engineering in state-of-the-art optomechanical devices.

Fabricaide: Fabrication-Aware Design for 2D Cutting Machines

Abstract: Designers of machine-cut objects must often consider whether and how their design can be fabricated with their available materials. In contrast to tools that support preparing finished designs for fabrication, we investigate shortening the feedback loop between design creation and fabrication preparation. To this end, we present Fabricaide, a fabrication-aware tool that interleaves the processes of creating and preparing designs for fabrication. By providing live feedback on how parts should be placed onto material sheets, analyzing how much material is consumed, and alerting users when designs are infeasible, Fabricaide enables users to proactively tailor their design to their available material. Fabricaide achieves this with a custom packing algorithm that arranges parts onto material sheets at interactive speeds. Our qualitative user study shows how Fabricaide can support different workflows, encourage material-conscious design practices, and provide insights on how to further improve similar interfaces in the future.

Dynamics of Time-Delayed Optoelectronic Oscillators With Nonlinear Amplifiers and Its Potential Application to Random Numbers Generation

Abstract: Time-delayed dynamical systems generally feature smooth nonlinear transfer functions in the feedback loop, such as polynomial or sinusoidal functions. As a consequence, the complexity of their dynamical behavior mainly originates from the time-delay. In this paper, we explore the opposite case where the nonlinear transfer function is complex ( $cos^{2}(sinh)$ ), and therefore, non-smooth. We perform a bifurcation analysis of the system, and evidence that this novel type of time-delayed system can display a chaotic behavior characterized by positive maximum Lyapunov exponent and quasi-maximal entropy. The high entropy behavior of the system combined with post-processing are used to generate random numbers for small values of the feedback gain with an overall bit rate up to 1.478 Gb/s. Our theoretical results are in excellent agreement with experimental measurements, performed with an optoelectronic oscillator involving a complex transfer function designed ad hoc .

Photonic generation of high-performance microwave frequency combs using an optically injected semiconductor laser with dual-loop optoelectronic feedback.

Abstract: An approach to generating microwave frequency combs (MFCs) with superior performance is proposed and experimentally demonstrated based on an optically injected semiconductor laser (OISL). The OISL operates at the period-one (P1) oscillation state under proper injection conditions. A sinusoidal voltage signal is used to modulate the P1 state for the initial MFC generation, and then two optoelectronic feedback loops are introduced to enhance the performance of the MFC: a short-delay feedback loop is firstly applied to improve comb contrast based on Fourier domain mode locking (FDML), and a long-delay feedback loop is added to reduce the comb linewidth based on the self-injection-locking technique. In the proof-of-concept experiment, a K-band MFC (18–26 GHz) with a line spacing of 8.45 MHz is obtained, where a comb linewidth of approximately 500 Hz and a comb contrast over 45 dB are simultaneously achieved. Additionally, each comb component exhibits superior performance in terms of phase noise, all below −90dBc/Hz at 10 kHz offset, demonstrating an excellent coherence among these combs.

Accelerated Deep Reinforcement Learning for Fast Feedback of Beam Dynamics at KARA

Abstract: Coherent synchrotron radiation (CSR) is generated when the electron bunch length is in the order of the magnitude of the wavelength of the emitted radiation. The self-interaction of short electron bunches with their own electromagnetic fields changes the longitudinal beam dynamics significantly. Above a certain current threshold, the micro-bunching instability develops, characterized by the appearance of distinguishable substructures in the longitudinal phase space of the bunch. To stabilize the CSR emission, a real-time feedback control loop based on reinforcement learning (RL) is proposed. Informed by the available THz diagnostics, the feedback is designed to act on the radio frequency (RF) system of the storage ring to mitigate the micro-bunching dynamics. To satisfy low-latency requirements given by the longitudinal beam dynamics, the RL controller has been implemented on hardware (FPGA). In this article, a real-time feedback loop architecture and its performance is presented and compared with a software implementation using Keras-RL on CPU/GPU. The results obtained with the CSR simulation Inovesa demonstrate that the functionality of both platforms is equivalent. The training performance of the hardware implementation is similar to software solution, while it outperforms the Keras-RL implementation by an order of magnitude. The presented RL hardware controller is considered as an essential platform for the development of intelligent CSR control systems.

Force sensing in an optomechanical system with feedback-controlled in-loop light

Abstract: Quantum control techniques applied at macroscopic scales provide us with opportunities in fundamental physics and practical applications. Among them, measurement-based feedback allows efficient control of optomechanical systems and quantum-enhanced sensing. In this paper, we propose a near-resonant narrow-band force sensor with extremely low optically added noise in an optomechanical system subject to a feedback-controlled in-loop light. The membrane's intrinsic motion consisting of zero-point motion and thermal motion is affected by the added noise of measurement due to the backaction noise and imprecision noise. We show that, in the optimal low-noise regime, the system is analogous to an optomechanical system containing a near quantum-limited optical parametric amplifier coupled to an engineered reservoir interacting with the cavity. Therefore, the feedback loop enhances the mechanical response of the system to the input while keeping the optically added noise of measurement below the standard quantum limit. Moreover, the system based on feedback offers a much larger amplification bandwidth than the same system with no feedback.

Hidden Feedback Loops in Machine Learning Systems: A Simulation Model and Preliminary Results

Abstract: In this concept paper, we explore some of the aspects of quality of continuous learning artificial intelligence systems as they interact with and influence their environment. We study an important problem of implicit feedback loops that occurs in recommendation systems, web bulletins and price estimation systems. We demonstrate how feedback loops intervene with user behavior on an exemplary housing prices prediction system. Based on a preliminary model, we highlight sufficient existence conditions when such feedback loops arise and discuss possible solution approaches.

Fuzzy Multiple Hidden Layer Neural Sliding Mode Control of Active Power Filter With Multiple Feedback Loop

Abstract: A fuzzy multiple hidden layer neural sliding mode control with multiple feedback loop (FMHLNSMCMFL) is proposed for a single-phase active power filter (APF), where a sliding mode controller is designed to make the current tracking error converge to zero and a new neural network with multiple feedback loops is introduced to approximate unknown dynamics. At the same time, the fuzzy neural network can eliminate chattering, improve the control accuracy and reduce the current distortion rate of APF. Moreover, the proposed double feedback fuzzy double hidden layer recurrent neural network is the weighted sum of fuzzy network and double hidden layer network and has strong global learning ability. The adaptive parameters obtained by Lyapunov function can ensure the asymptotic stability of the system. Simulation and hardware experiments verify the introduced FMHLNSMCMFL scheme is a viable control solution for the APF.

Closing the Feedback Loop in DevOps Through Autonomous Monitors in Operations

Abstract: DevOps represent the tight connection between development and operations. To address challenges that arise on the borderline between development and operations, we conducted a study in collaboration with a Swedish company responsible for ticket management and sales in public transportation. The aim of our study was to explore and describe the existing DevOps environment, as well as to identify how the feedback from operations can be improved, specifically with respect to the alerts sent from system operations. Our study complies with the basic principles of the design science paradigm, such as understanding and improving design solutions in the specific areas of practice. Our diagnosis, based on qualitative data collected through interviews and observations, shows that alert flooding is a challenge in the feedback loop, i.e. too much signals from operations create noise in the feedback loop. Therefore, we design a solution to improve the alert management by optimizing when to raise alerts and accordingly introducing a new element in the feedback loop, a smart filter. Moreover, we implemented a prototype of the proposed solution design and showed that a tighter relation between operations and development can be achieved, using a hybrid method which combines rule-based and unsupervised machine learning for operations data analysis.

Mitigation of Complex Non-Linear Dynamic Effects in Multiple Output Cascaded DC-DC Converters

Abstract: In the modern world of technology, the cascaded DC-DC converters with multiple output configurations are contributing a dominant part in the DC distribution systems and DC micro-grids. An individual DC-DC converter of any configuration exhibits complex non-linear dynamic behavior resulting in instability. This paper presents a cascaded system with one source boost converter and three load converters including buck, Cuk, and Single-Ended Primary Inductance Converter (SEPIC) that are analyzed for the complex non-linear bifurcation phenomena. An outer voltage feedback loop along with an inner current feedback loop control strategy is used for all the sub-converters in the cascaded system. To explain the complex non-linear dynamic behavior, a discrete mapping model is developed for the proposed cascaded system and the Jacobian matrix’s eigenvalues are evaluated. For the simplification of the analysis, every load converter is regarded as a fixed power load (FPL) under reasonable assumptions such as fixed frequency and input voltage. The eigenvalues of period-1 and period-2 reveal that the source boost converter undergoes period-2 orbit and chaos whereas all the load converters operate in a stable period-1 orbit. The proposed configuration eliminates the period-2 and chaotic behavior from all the load converters and is also validated using simulation in MATLAB/Simulink and experimental results.

Controlling the Injection of Best-Effort Tasks to Harvest Idle Computing Grid Resources

Abstract: High Performance Computing systems are facing more and more variability in their performance, related to e.g., Input/Output (I/O) behavior and power consumption: they are less predictable, which requires more run-time management to meet the requirements. This can be addressed following feedback approach, where a management feedback loop, in response to monitored information in the systems, based on analysis of this data, decides to activate system-level or application-level adaptation mechanisms. One such regulation problem is found in the context of CiGri, a lightweight computing grid system which exploits the unused resources of a set of computing clusters. The computing power left over by the execution of premium cluster users’ HPC applications, is used to execute smaller jobs, which are injected as much as the global system allows.The feedback loop which we design has to regulate this injection of jobs in such a way as to avoid overloading of the distributed file system (or file-server), which would be detrimental to the main performance, while self-adapting to variations in load in order to make the best use of available resources. We put in place a mechanism for feedback control in system software by controlling the number of jobs to be sent to the cluster in response to system information about the current number of processed jobs and the file-server load, which has a significant impact of the performance of the priority users jobs. We perform experimental validation by comparing several control solutions.

Active anti-sway crane control using partial state feedback from inertial sensor

Abstract: The paper deals with development of active anti-sway feedback control method for gantry cranes. Inertial measurement unit is chosen as a load motion sensing device allowing to close a feedback loop. The paper provides guidelines for the successive steps of mathematical modelling, data-driven identification and model-based controller design. The proposed method is experimentally validated on an industrial overhead crane system.

Adaptive Fault Tolerant Flight Control for Input Redundant Systems Using a Nonlinear Reference Model

Abstract: In this article, an adaptive fault-tolerant control scheme is proposed for nonlinear systems with actuator redundancy. To simultaneously consider multiple types of actuator faults and enable the use of an ideal system with nonlinear dynamics, the $\mathcal {L}_1$ adaptive control scheme for nonlinear time-varying reference systems is extended to systems with redundant inputs and an input gain uncertainty. The time-varying state-dependent additive uncertainty and the effectiveness of each input are selected as the adaptive parameters. A fixed, predictable control allocation is achieved through a feedback loop within the control block. The conditions for the performance bounds on the system relative to the ideal system are derived. It is shown that the performance bounds can be arbitrarily improved by increasing the adaptation rate and the feedback gain. The proposed adaptive control scheme is applied to the fault-tolerant control of an aircraft using a backstepping controller as a baseline controller, and numerical simulations are conducted to demonstrate the performance of the proposed controller for various fault scenarios. Monte Carlo simulations are conducted to verify the robustness property of the proposed controller.

A trajectory control gate driver for Wide Band Gap devices in standard packages

Abstract: The acceptance of Wide band Gap devices is partially dependent on the ease of retrofitting existing designs of power converter. Standard power modules are inherently ill-suited to the fast switching behavior of Silicon Carbide power transistors. Active gate drivers can manage the switching dynamics of power devices by means of feedback and control on the gate. This paper proposes a low cost yet effective di/dt-based active gate driver using a sensor made on a printed circuit board (PCB) and an analog feedback loop.

Development of EMG-FMG Based Prosthesis With PVDF-Film Vibrational Feedback Control

Abstract: The paper proposes an EMG-FMG-based prosthesis using a vibrational feedback method. The control loop for bio-feedback consists of a feed-forward loop for controlling the prosthetic hand and a sensory feedback loop for delivering the sense of vibration according to the distance with an object. In addition, the motion of the prosthetic hand using the EMG-FMG knit band sensor as an input device is generated via a decision tree algorithm, the distance is measured using a capacitive knit data glove, and the vibrational feedback using a PVDF piezoelectric actuator is provided. The control loop for bio-feedback was verified experimentally. First, the performance of the sensors and control methods was analyzed, and the results of EMG-FMG motion recognition and vibrational feedback were verified. Second, the experiment was conducted on ten subjects who wore prosthesis to which the bio-feedback method was applied. Thanks to the control loops for bio-feedback, the grasping time with blindfolded was reduced by 2.92 seconds and the number of failures decreased by 1.04 times. As a result, it was confirmed that the control loop for bio-feedback could improve the performance of the prosthesis.

A small climate-amplifying effect of climate-carbon cycle feedback.

Abstract: The climate-carbon cycle feedback is one of the most important climate-amplifying feedbacks of the Earth system, and is quantified as a function of carbon-concentration feedback parameter ( β ) and carbon-climate feedback parameter ( γ ). However, the global climate-amplifying effect from this feedback loop (determined by the gain factor, g ) has not been quantified from observations. Here we apply a Fourier analysis-based carbon cycle feedback framework to the reconstructed records from 1850 to 2017 and 1000 to 1850 to estimate β and γ . We show that the β -feedback varies by less than 10% with an average of 3.22 ± 0.32 GtC ppm −1 for 1880–2017, whereas the γ -feedback increases from −33 ± 14 GtC K −1 on a decadal scale to −122 ± 60 GtC K −1 on a centennial scale for 1000–1850. Feedback analysis further reveals that the current amplification effect from the carbon cycle feedback is small ( g is 0.01 ± 0.05), which is much lower than the estimates by the advanced Earth system models ( g is 0.09 ± 0.04 for the historical period and is 0.15 ± 0.08 for the RCP8.5 scenario), implying that the future allowable CO 2 emissions could be 9 ± 7% more. Therefore, our findings provide new insights about the strength of climate-carbon cycle feedback and about observational constraints on models for projecting future climate.

Real-time data assimilation and control on mechanical systems under uncertainties

Abstract: This research work deals with the implementation of so-called Dynamic Data-Driven Application Systems (DDDAS) in structural mechanics activities. It aims at designing a real-time numerical feedback loop between a physical system of interest and its numerical simulator, so that (i) the simulation model is dynamically updated from sequential and in situ observations on the system; (ii) the system is appropriately driven and controlled in service using predictions given by the simulator. In order to build such a feedback loop and take various uncertainties into account, a suitable stochastic framework is considered for both data assimilation and control, with the propagation of these uncertainties from model updating up to command synthesis by using a specific and attractive sampling technique. Furthermore, reduced order modeling based on the Proper Generalized Decomposition (PGD) technique is used all along the process in order to reach the real-time constraint. This permits fast multi-query evaluations and predictions, by means of the parametrized physics-based model, in the online phase of the feedback loop. The control of a fusion welding process under various scenarios is considered to illustrate the proposed methodology and to assess the performance of the associated numerical architecture.

DiffLoop: Tuning PID Controllers by Differentiating Through the Feedback Loop

Abstract: Since most industrial control applications use PID controllers, PID tuning and anti-windup measures are significant problems. This paper investigates tuning the feedback gains of a PID controller via back-calculation and automatic differentiation tools. In particular, we episodically use a cost function to generate gradients and perform gradient descent to improve controller performance. We provide a theoretical framework for analyzing this non-convex optimization and establish a relationship between back-calculation and disturbance feedback policies. We include numerical experiments on linear systems with actuator saturation to show the efficacy of this approach.

A Measurement Noise Rejection Method in the Feedback Control System Based on Noise Observer

Abstract: In the conventional feedback control system, the control accuracy is affected by the measurement noise In this article, a novel method is proposed for the purpose of the measurement noise rejection in the single-input single-output (SISO) feedback control system By adding an inner noise observer loop in the conventional feedback loop, we apply the “observer-based” control method into the sensor output signal processing The proposed method is capable of reducing the measurement noise in frequencies overlapping with the control bandwidth without affecting the tracking ability of the feedback control system Moreover, this method is simple in that its noise suppression ability is merely decided by the ${Q}$ -filter, which can be set as a constant In addition, a necessary and sufficient condition for the system stability is presented considering the uncertainties of the real plant It is shown that when the ${Q}$ -filter is zero, the uncertainties of the plant cannot influence the stability of the system To verify the effectiveness, both the tracking performance and noise rejection performance of the proposed method is simulated, comparing with the conventional feedback control system Finally, laboratory experiment is conducted with a tracking mirror control system

Robustness of Uncertain Switching Nonlinear Feedback Systems against Large Time-Variation

Abstract: For a MIMO nonlinear feedback system, the robustness against uncertain time-variation (slow or infrequently large) in the feedback loop is investigated in an input-output framework. A couple of sufficient conditions in terms of bounds on average rates of time-variation for the system to be stable are derived. The conditions provide an useful tool for designing adaptive controllers for systems against uncertain large time-variation.

Design and Implementation of Signal Filtering Techniques on Real-time Pressure Process Plant

Abstract: In all the closed-loop feedback industrial processes, the measured sensor signal from the field environment is directly fed to the controller for processing. These raw signals contain the unwanted stochastic noise from the surroundings, which affects the process and controller performance. These issues can be rectified by employing a suitable filtering technique, and the feedback loop performance can be improved significantly. Hence, this paper aims to propose set-point and noise filtering usage in the process control loop. The proposed filtering technique is compared with the existing conventional methods and implemented over the real-time pressure process plant for performance analysis. Also, the numerical comparison is carried out in terms of the rise time, settling time, and peak overshoot for validation of the proposed approach.