Implementation Of Feedback Control Research Articles

Design of hybrid controller for broadband vibration isolation with active negative stiffness and ground-hook

This paper presents a novel hybrid control strategy that integrates active negative stiffness (ANS) with the ground-hook principle, aiming to enhance the vibration isolation system’s performance across both low and high-frequency ranges. Firstly, to improve vibration isolation performance, this study analyzes the existing ANS model. It identifies key factors affecting ANS stability and proposes a control parameter adjustment scheme based on stability conditions. This approach effectively refines ANS control theory and provides theoretical support for the application of ANS by ensuring stability. Secondly, addressing the limitations of ANS in high-frequency vibration isolation, this study proposes targeted solutions based on the ground-hook principle. While ANS excels in low-frequency isolation, this work tackles its shortcomings to enhance high-frequency vibration isolation. These solutions include the implementation of relative velocity feedback (RVFB) control and absolute velocity feedforward (AVFF) control to enhance high-frequency vibration isolation performance, and AVFF exhibits superior advantages over RVFB. Furthermore, the stability of different control strategies is analyzed. The findings reveal that sky-hook significantly enhances the control stability margin of the ANS loop, while RVFB decreases it, with AVFF having a minimal impact. Finally, comprehensive experiments validate a significant enhancement in both low and high-frequency vibration isolation performance, providing robust evidence for the practical engineering application of the vibration isolation system.

Design and Implementation of an L1 Adaptive Proportional Output Feedback Controller

A new approach for output feedback L1 adaptive control based on a proportional adaptation law is presented. The effectiveness of this design is assessed in simulation and validated through real-time testing on an airfoil pitch control wind tunnel experimental rig. Experimental evaluation of the robustness of the controllers, assessed by introducing various disturbances into the control signals, shows that the adaptive control has a better performance compared to PID control, particularly in scenarios with reduced control effectiveness and time-varying disturbances. The experimental results demonstrate the efficacy of the proposed method in practical applications.

Optimal attitude control for landing on asteroid with a flexible lander

The recently developed flexible lander offers a potential option for future asteroid landing missions due to its advantages in suppression of rebound and overturning. The strong nonlinear dynamics and control constraints of the flexible lander hinder the online implementation of optimal attitude feedback control. To generate the optimal attitude control rapidly, a constrained inhomogeneous approximating sequence of Riccati equations (CI-ASRE) method is proposed. The saturation function is firstly constructed to incorporate control magnitude constraints into dynamics so that the constraints can be analytically handled. Subsequently, to avoid singularity, a partial factorization strategy is proposed to convert the constrained dynamics into two parts, a pseudolinear term and an inhomogeneous term. This factorization enables the nonlinear problem to be expressed as the limit of a sequence of inhomogeneous linear quadratic problems while avoiding local linearization. At last, the analytical CI-ASRE is newly derived to iteratively solve these problems to rapidly obtain optimal feedback control. The computational simplicity and effectiveness of the CI-ASRE method overcome difficulties of optimal attitude control problems of the flexible lander associated with control constraints, strong nonlinearity, and online feedback control generation. The effectiveness of the proposed method for the flexible lander is verified using a landing scenario on asteroid 433 Eros.

Real-time feedback control of β p based on deep reinforcement learning on EAST

Recently, with the advancement of the AI field, reinforcement learning (RL) has increasingly been applied to plasma control on tokamak devices. However, possibly due to the generally high training costs of reinforcement learning based on first-principle physical models and the uncertainty in ensuring simulation results align perfectly with tokamak experiments, feedback control experiments using reinforcement learning specifically for plasma kinetic parameters on tokamaks remain scarce. To address this challenge, this work proposes a novel design scheme including the development of a low computational cost environment. This environment is derived from EAST modulation experiments data through system identification. To tackle issues of noise and actuator limitations encountered in experiments, data preprocessing methods were employed. During training, the agent collected data across multiple plasma scenarios to update its strategy, and the performance of the RL controller was fine-tuned by adjusting the weight of the integral term of the error in the reward function. The effectiveness and robustness of the proposed design were then validated in a simulated environment. Finally, the scheme was successfully implemented on EAST, effectively tracking the β p target with lower hybrid wave (LHW) at 4.6 GHz as the actuator, and providing reference for implementing feedback control based on reinforcement learning in tokamaks.

Python Red Pitaya Lockbox (PyRPL): An open source software package for digital feedback control in quantum optics experiments.

We present the Python Red Pitaya Lockbox (PyRPL), an open source software package that allows the implementation of automatic digital feedback controllers for quantum optics experiments on commercially available, affordable Field-Programmable Gate Array (FPGA) boards. Our software implements the digital generation of various types of error signals, from an analog input through the application of loop filters of high complexity and real-time gain adjustment for multiple analog output signals, including different algorithms for resonance search, lock acquisition sequences, and in-loop gain optimization. Furthermore, all necessary diagnostic instruments, such as an oscilloscope, a network analyzer, and a spectrum analyzer, are integrated into our software. Apart from providing a quickly scalable, automatic feedback controller, the lock performance that can be achieved by using PyRPL with imperfect equipment, such as piezoelectric transducers and noisy amplifiers, is better than the one achievable with standard analog controllers due to the higher complexity of implementable filters and possibilities of nonlinear operations in the FPGA. This drastically reduces the cost of added complexity when introducing additional feedback loops to an experiment. The open-source character also distinguishes PyRPL from commercial solutions, as it allows users to customize functionalities at various levels, ranging from the easy integration of PyRPL-based feedback controllers into existing setups to the modification of the FPGA functionality. A community of developers provides fast and efficient implementation and testing of software modifications.

Raman-controlled pyruvate feeding to control metabolic activity and product quality in continuous biomanufacturing.

Despite technological advances ensuring stable cell culture perfusion operation over prolonged time, reaching a cellular steady-state metabolism remains a challenge for certain manufacturing cell lines. This study investigated the stabilization of a steady-state perfusion process producing a bispecific antibody with drifting product quality attributes, caused by shifting metabolic activity in the cell culture. A novel on-demand pyruvate feeding strategy was developed, leveraging lactate as an indicator for tricarboxylic acid (TCA) cycle saturation. Real-time lactate monitoring was achieved through in-line Raman spectroscopy, enabling accurate control at predefined target setpoints. The implemented feedback control strategy resulted in a three-fold reduction of ammonium accumulation and stabilized product quality profiles. Stable and flat glycosylation profiles were achieved with standard deviations below 0.2% for high mannose and fucosylation. Whereas galactosylation and sialylation were stabilized in a similar manner, varying lactate setpoints might allow for fine-tuning of these glycan forms. The Raman-controlled pyruvate feeding strategy represents a valuable tool for continuous manufacturing, stabilizing metabolic activity, and preventing product quality drifting in perfusion cell cultures. Additionally, this approach effectively reduced high mannose, helping to mitigate increases associated with process intensification, such as extended culture durations or elevated culture densities.

Optimal operation of a large-scale packed bed chemical-looping combustion process using nonlinear model predictive control

In this work, a nonlinear model predictive control (NMPC) scheme is presented for chemical-looping combustion (CLC) in a large-scale packed bed reactor. For NMPC, the plant is represented by a multiscale model comprised of mass and energy balances for both the reactor and particles, with states dependent on their location within both the spatial and temporal domains. A kinetic scheme was chosen from existing literature such that it can accurately predict the process behaviour obtained from multiple sets of literature data, attained using reactors of different sizes operating under different inlet flowrates and temperatures. The multiscale model was used to simulate the plant behaviour, while a pseudo-homogeneous model was used as the internal NMPC model to reduce computational costs for implementation of feedback control. For the pseudo-homogeneous model, it was verified that the gradients within the particles could be assumed to be negligible, and the number of states is decreased by about 87% by focusing on the reactor behaviour. The resulting NMPC scheme is implemented for packed bed CLC and was performed for both the oxidation and reduction stages of CLC. For NMPC of the oxidation stage, manipulating both the inlet air and inert gas fluxes allowed the outlet gas temperature to track a given setpoint. In this case, the oxidation stage generated energy for three times as long as the case when a constant inlet air flux is used. When NMPC was implemented for the reduction stage, improved CO2 selectivity could be achieved by manipulating the inlet fuel flowrate. This improved the CO2 and H2O purity of the outlet stream by over 20% and thus improved the carbon capture effectiveness of the process.

Adaptive recurrent neural network for uncertainties estimation in feedback control system

In this paper, a recurrent neural network (RNN) is used to estimate uncertainties and implement feedback control for nonlinear dynamic systems. The neural network approximates the uncertainties related to unmodeled dynamics, parametric variations, and external disturbances. The RNN has a single hidden layer and uses the tracking error and the output as feedback to estimate the disturbance. The RNN weights are online adapted, and the adaptation laws are developed from the stability analysis of the controlled system with the RNN estimation. The used activation function, at the hidden layer, has an expression that simplifies the adaptation laws from the stability analysis. It is found that the adaptive RNN enhances the tracking performance of the feedback controller at the transient and steady state responses. The proposed RNN based feedback control is applied to a DC–DC converter for current regulation. Simulation and experimental results are provided to show its effectiveness. Compared to the feedforward neural network and the conventional feedback control, the RNN based feedback control provides good tracking performance.

Neural Network Assisted Laser Frequency Locking System

The laser frequency locking system with high robustness, high accuracy, and good anti-noise performance is usually used in laser synchronization and optics communications, continuous-variable quantum key distribution, and ultra-stable laser wavelength stabilization. In such systems, the analog or digital method is usually used to implement feedback control. Digital systems are more widely used than analog systems based on phase-locked loops due to their simplicity, flexibility, and robustness. The proportional- integral-derivative (PID) algorithm is the most typical algorithm well-developed in the digital locking system. However, due to the nonlinearity of the error signal induced by frequency variation, the PID algorithm will have a deviation in compensation for the transient response. This paper demonstrates a neural network assisted laser frequency locking system using predicted values to decrease the nonlinearity. Compared to the PID algorithm locking performance in the same locking system, both the peak-to-peak deviation and the root- mean-square error of the neural network assisted locking system are reduced by half.

Taming CRISPRi: Dynamic range tuning through guide RNA diversion

CRISPRi is a powerful technique to repress gene expression in a targeted and highly efficient manner. However, this potency is a double-edged sword in inducible systems, as even leaky expression of guide RNA results in a repression phenotype, complicating applications such as dynamic metabolic engineering. We evaluated three methods to enhance the controllability of CRISPRi by modulating the level of free and DNA-bound guide RNA complexes. Overall repression can be attenuated through rationally designed mismatches in the reversibility determining region of the guide RNA sequence; decoy target sites can selectively modulate repression at low levels of induction; and the implementation of feedback control not only enhances the linearity of induction, but broadens the dynamic range of the output as well. Furthermore, feedback control significantly enhances the recovery rate after induction is removed. Used in combination, these techniques enable the fine-tuning of CRISPRi to meet restrictions imposed by the target and match the input signal required for induction.

INFLUENCE OF THERMOPHYSICAL PROPERTIES ON TRANSFER OF HEAT IN MULTI-LAYER DUCTS AND TEMPERATURE CONTROL USING ELECTRIC HEATING

Ensuring oil production flow in offshore systems is a critical aspect of oil exploration operations. Any interruption in the production process, whether partial or complete, can result in significant financial losses and cause solid deposition in the production line. Such deposition is due to the crystallization of paraffin and hydrates, a common problem caused by low temperatures in deep waters. Among various mitigation strategies, the Pipe-in-Pipe (PIP) system with active heating is a technological solution to address this issue. This work aimed to perform a numerical simulation of the PIP system using the Finite Volume Method with an implicit formulation, considering the effect of temperature on fluid properties and the system's dynamic response. A control loop using a PI velocity algorithm was developed to maintain the temperature above the critical point. Such simulation studies were performed using the Python programming language in the Anaconda suite. The results showed that the fluid properties greatly influence the dynamic response. The PI control maintained the temperature in the desired condition, demonstrating its operational effectiveness in preventing solid deposition and delivering stable and low-oscillatory behavior. This research emphasizes the significance of taking temperature's impact on fluid properties into account when simulating offshore oil production systems and demonstrates the effectiveness of implementing feedback control.

Design and calibration of spoke piezoelectric six-dimensional force/torque sensor for space manipulator

The large manipulator outside the space cabin is a multi-degree of freedom actuator for space operations. In order to realize the automatic control and flexible operation of the space manipulator, a novel spoke structure piezoelectric six-dimensional force/torque sensor with redundancy ability, high stiffness and good decoupling performance is innovatively proposed. Based on the deformation coordination relationship, the redundancy measurement mechanism is revealed. The mathematical models of the sensor with and without branch fault are established respectively. The finite element model is established to verify the feasibility of structure and redundancy measuring principle of the sensor. Depending on the theoretical analysis and simulation analysis, the prototype of the sensor is developed. Static and dynamic calibration experiments are carried out. The actual output voltage signal of the six-dimensional force/torque sensor is collected to establish the equation between the standard input applied load and the actual output voltage signal. Based on ant colony optimized BP algorithm, performance indexes of the sensor with and without branch fault are analyzed respectively. The experimental results show that the spoke piezoelectric six-dimensional force/torque sensor with the eight-point support structure has good accuracy and reliability. Meanwhile, it has strong decoupling characteristic that can effectively shield the coupling between dimensions. The nonlinear errors and maximum interference errors of decoupled data with and without branch faults are less than 1% and 2%, respectively. The natural frequency of the six-dimensional force sensor can reach 2856.45 Hz and has good dynamic characteristics. The research content lays a theoretical and experimental foundation for the design, development and application of the new six-dimensional force/torque sensors with redundancy. Meanwhile, it will significantly improve the research level in this field, and provide a strong guarantee for the smooth implementation of force feedback control of the space station manipulator project.

Robust Control Based on Observed States Designed by Means of Linear Matrix Inequalities for Grid-Connected Converters

This paper provides a procedure to design robust controllers based on observed states applied to three-phase inverters with LCL filters connected to a grid with uncertain and possibly time-varying impedances, which can arise in renewable energy systems and microgrid applications. Linear matrix inequalities are used to rapidly compute, off-line, based only on the choice of two scalar parameters for pole location, sets of gains for the controller and the observer, and also to provide a theoretical certificate of the closed-loop stability, including a limit for the rate of variations of the grid impedances. The proposed design procedure allows the easy implementation of robust state feedback controllers with a reduced number of sensors, ensuring good performance for different sets of grid impedances. Additionally, larger regions of guaranteed stability are provided by the proposed procedure, when compared with a similar condition from the literature. The control law using the observed states can ensure grid currents with low harmonic content, complying with the IEEE 1547 Standard requirements, with negligible loss of performance concerning the feedback of the measured state variables. Three optimal state feedback controllers from the literature are reproduced here and successfully implemented using the observed state variables based on the proposed procedure. In all cases, the viability of the proposal was confirmed by simulations and experimental results.

Individually adjusted absolute blood volume feedback control: A promising solution for intradialytic hypotension.

Intradialytic hypotension (IDH) remains one of the most frequent complications associated to hemodialysis (HD), frequently triggered by a reduction in absolute blood volume (ABV) not compensated by vascular refilling. A recently developed dilutional method allows routinary measurement of ABV and, by a simple algorithm, may turn blood volume monitor (BVM) guided UF (ultrafiltration) biofeedback into an ABV control, automatically adjusting UF rate to maintain ABV above a preset threshold. The aim of this study is to identify an individual critical ABV threshold and test the ability of an ABV feedback control to avoid IDH. We studied 24 patients throughout three consecutive midweek HD treatments. ABV and blood pressure (BP) were measured every 30 min and anytime the patient referred any symptoms to identify each patient's critical ABV (ABV at the time of hypotension). A fixed bolus dilution approach at the start of HD was used to calculate ABV. Then, patients were followed through three additional HD treatments and IDH development was analyzed. Seventy-one treatments performed in 24 patients. ABV monitoring showed a constant decrease as HD treatment progressed. Thirteen IDH events were observed in eight different patients, with a mean systolic BP drop in IDH treatments of 37.38 ± 4.31 mmHg and a mean adjusted ABV at hypotension of 71.07 ± 14.88mL/kg. Critical ABV was individually set in patients prone to IDH. As expected, ABV feedback control successfully maintained ABV over preset critical ABV. IDH events were avoided in 21 out of 22 treatments performed. ABV drop was successfully reduced, as well as SBP drop (despite similar UF than prior to ABV feedback control implementation). ABV feedback control avoided IDH in 21 out of 22 treatments performed by maintaining blood volume above critical ABV, significantly reducing ABV variations without compromising prescribed UF.

Spring and latch dynamics can act as control pathways in ultrafast systems

Ultrafast movements propelled by springs and released by latches are thought limited to energetic adjustments prior to movement, and seemingly cannot adjust once movement begins. Even so, across the tree of life, ultrafast organisms navigate dynamic environments and generate a range of movements, suggesting unrecognized capabilities for control. We develop a framework of control pathways leveraging the non-linear dynamics of spring-propelled, latch-released systems. We analytically model spring dynamics and develop reduced-parameter models of latch dynamics to quantify how they can be tuned internally or through changing external environments. Using Lagrangian mechanics, we test feedforward and feedback control implementation via spring and latch dynamics. We establish through empirically-informed modeling that ultrafast movement can be controllably varied during latch release and spring propulsion. A deeper understanding of the interconnection between multiple control pathways, and the tunability of each control pathway, in ultrafast biomechanical systems presented here has the potential to expand the capabilities of synthetic ultra-fast systems and provides a new framework to understand the behaviors of fast organisms subject to perturbations and environmental non-idealities.

Online Transfer Function Estimation and Control Design Using Ambient Synchrophasor Measurements

This paper proposes an adaptive control design framework for damping inter-area oscillations in power systems. The design is entirely based on ambient synchrophasor measurements. The proposed framework consists of three components, namely, input-output transfer function estimation, channel selection, and feedback control implementation. For the first part, we propose a simple yet effective novel estimation algorithm in the frequency domain to identify low-order transfer functions in pre-specified frequency ranges between the measured input and output data using ambient synchrophasor measurements. In the second part, based on the identified transfer functions, the joint controllability-observability (JCO) of all identified channels is estimated and most suitable control candidates for damping the target inter-area mode are selected. Finally, an appropriate lead-lag controller is designed using a classical frequency-domain method to improve the damping of a dominant oscillatory inter-area mode. Since ambient data is used in the analysis, the selected channel as well as the designed controller parameters can be updated online whenever the system operating point changes resulting in an efficient adaptive controller. The effectiveness of the proposed framework is illustrated by implementing it on the two-area Kundur test system and the IEEE 39-bus test system.

Multicellular PD Control in Microbial Consortia

We propose a multicellular implementation of a biomolecular PD feedback controller to regulate gene expression in a microbial consortium. The implementation involves distributing the proportional and derivative control actions between two different cellular populations that can communicate with each other and regulate the output of a third target cellular population. We derive analytical conditions on biological parameters and control gains to adjust the system’s static and dynamical properties. We then validate the strategy’s performance and robustness through extensive in silico experiments.

Exponential Stabilization for a Class of Strict-Feedback Nonlinear Time Delay Systems via State Feedback Control Scheme

This paper considers the exponential stabilization problem for a class of strict-feedback nonlinear systems with multiple time-varying delays, whose nonlinear terms satisfy the linear growth condition. The state feedback controller that relies on a positive parameter to be determined is constructed to deal with nonlinear terms. By tactfully introducing the Lyapunov–Krasovskii functional with an exponential function and selecting the applicable parameter to be determined, the implementable state feedback controller can be obtained to guarantee that the closed-loop system is exponentially stable. The proposed state feedback control scheme is finally applied to the control design of two-stage chemical reactor system, which illustrates the effectiveness of the control method.

Artificial Neural Network-Based Parameter Identification Method for Wireless Power Transfer Systems

In this paper, a Wireless Power Transfer (WPT) system parameter identification method that combines an artificial neural network and system modeling is presented. During wireless charging, there are two critical parameters; specifically, mutual inductance and load resistance, which change due to the movement of the transmitter/receiver and battery conditions. The identification of these two uncertain parameters is an essential prerequisite for the implementation of feedback control. The proposed method utilizes an Artificial Neural Network (ANN) to acquire a mutual inductance value. A succinct system model is formulated to calculate the load resistance of the remote receiver. The maximum error of the mutual inductance estimation is 2.93%, and the maximum error of the load resistance estimation is 7.4%. Compared to traditional methods, the proposed method provides an alternative way to obtain mutual inductance and load resistance using only primary-side information. Experimental results were provided to validate the effectiveness of the proposed method.

On the Design of a PID Bio-Controller With Set Point Weighting and Filtered Derivative Action

Effective and robust regulation of biomolecular processes is crucial for designing reliable synthetic bio-devices functioning in uncertain and constantly changing biological environments. Proportional-Integral-Derivative (PID) controllers are undeniably the most common way of implementing feedback control in modern technological applications. Here, we introduce a highly tunable PID bio-controller with set point weighting and filtered derivative action presented as a chemical reaction network with mass action kinetics. To demonstrate its effectiveness, we apply our PID scheme on a simple biological process of two mutually activated species, one of which is assumed to be the output of interest. To highlight its performance advantages we compare it to PI regulation using numerical simulations in both the deterministic and stochastic setting.