Adaptive Feedback Control Method Research Articles

Advanced Controllers for Heat Transfer Fluid Mass Flow Rate Control in Solar Tower Receivers

Efficient control strategies for managing the mass flow rate (MFR) of heat transfer fluids (HTF) during cloud transients in Solar Tower receivers play a pivotal role in optimizing plant profitability and receiver durability. This study focuses on the performance and durability of Solar Tower receivers during cloud transients. It evaluates adaptive feedback and feedforward control methods, which adjust the flow rate of heat transfer fluids based on real-time measurements of direct normal irradiance (DNI), receiver outlet and receiver panel outlet temperatures. The effectiveness of an aggressive all-sky and conservative clear-sky control strategy is explored against a conventional PI controller, emphasizing energy efficiency and receiver longevity. Simulations using a thermal Modelica model resembling a 100 MWel Crescent Dunes-like solar tower plant reveal that both advanced controllers provide precise setpoint tracking, while the PI controller struggles. The conservative controller which has a cloud standby mode prevents overheating during cloud transients by using a clear sky mass flow rate, while the aggressive controller uses the receiver panel outlet temperatures to correct for upstream tube temperature variations allowing for fast tracking correction and disturbance rejection, albeit with slight overshoots. Furthermore, the controllers significantly decrease the creep-fatigue damage accumulated in the receiver panels during cloudy days, due to limiting the increase in wall temperature spikes when cloud events end. Overall, this study underscores the pivotal role of HTF mass flow rate control systems in influencing receiver system failure modes and longevity and offers a new tool in controller design and operation assessment.

Passivity Analysis and Complete Synchronization of Fractional Order for Both Delayed and Non-Delayed Complex Dynamical Networks with Couplings in the Derivative

This manuscript explores the analysis of passivity and synchronization criteria for a complex fractional-order dynamical network model with derivative couplings and time-varying delays. The passivity problem of the proposed network model is deduced using various inequality methods and presented as a linear matrix inequality. To ensure complete synchronization for a fractional-order complex dynamical network with derivative couplings (CDNMDC), we derive suitable criteria using an adaptive feedback control method. Additionally, we investigate the synchronization criterion of these complex networks while accounting for parameter uncertainties. Finally, we provide an example to demonstrate the effectiveness of the proposed solutions.

Adaptive feedback control of stability in an axial compressor with circumferential inlet distortion

The adaptive feedback control of stability with circumferential inlet distortion has been experimentally investigated in a low-speed, axial compressor. The flat-baffles with different span heights are used to simulate different distorted inflow cases. Compared with auto-correlation and root-mean-square analysis, cross-correlation analysis used to predict early stall warning does not depend on the distortion position. Hence, the cross-correlation coefficient was used to monitor the stable status of the compressor and provide the feedback signal in the active control strategy when suffering from different distortions. Based on the stall margin improvement of tip air injection obtained under different distorted inflow cases and the sensitivity analysis of cross-correlation coefficients to injected momentum ratios, tip air injection was adopted as the actuator for adaptive feedback control. The digital signal processing controller was designed and applied to achieve adaptive feedback control in distorted inflow conditions. The results show that the adaptive feedback control of air injection nearly achieves the same stall margin improvement as steady air injection under different distortion intensities with a reduced injection mass flow. Thus, the proposed adaptive feedback control method is ideal for the engine operation with circumferential distorted inflow, which frequently occurs in flight.

Adaptive Control for Joint Module of Bionic Arms

Minimizing the influence of unknown external disturbances while satisfying complex control requirements in daily life is one of the difficulties of bionic arm control. However, the control of modular joints often ignores the dynamic characteristics and coupling relationship of the system, which can not meet the control requirements of a bionic arm prosthesis. In addition, the large vibration caused by unknown interference is not conducive to human-computer interaction. In order to solve this problem, we proposed an adaptive feedback control (AFC) method based on magnetic field-oriented control for the joint module to realize intelligent control of the bionic arm. The disturbance of the joint module is obtained by real-time parameter calculation. At the same time, the controller regulated the output voltage to eliminate the influence of uncertain disturbances. Theoretical analysis and simulation results show that the controller has a good effect on improving the dynamic performance of the joint module system, and maintaining the stability of the system in the interference, satisfying the requirements of bionic arm control.

An Observer-Based Fuzzy Adaptive Consensus Control Method for Nonlinear Multiagent Systems

This article investigates the problem of fuzzy adaptive consensus tracking control for nonlinear multiagent systems with unknown nonlinear control gain functions. In the control design, fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, and a distributed state observer is constructed to estimate the unmeasured states. Under the case of directed graph, by constructing the logarithm Lyapunov functions, an adaptive fuzzy distributed control method is presented, which removes the restrictive assumptions about the unknown control gain functions must be constants in traditional adaptive intelligent output feedback control methods. The developed control scheme cannot only ensure that all signals of the controlled system are semiglobal uniformly ultimately bounded, but also make the outputs of all the followers keep consensus with the output trajectory of the leader. Finally, simulation results are given to illustrate the effectiveness of the developed consensus control scheme and theorem.

Prescribed-time adaptive neural feedback control for a class of nonlinear systems

This paper studies prescribed-time stabilization for a class of unknown strict-feedback nonlinear systems. Adaptive time-varying feedback control method and non-scaling transformation strategies are employed to reduce the computation burden caused by the scaling functions. Besides, the proposed method uses a constrained function to avoid the possibility of an oversized signal when time approaches to the prescribed settling time. Based on the backstepping method, a neural prescribed-time controller is constructed to achieve finite-time regulation. Under the action of the proposed strategy, all the closed-loop signals are bounded and the system is stable in prescribed finite time. Finally, two simulation examples illustrate the effectiveness of the results.

Adaptive State Feedback Control Method Based on Recursive Least Squares

This study revealed an adaptive state feedback control method based on recursive least squares (RLS) that is introduced for a time-varying system to work with high efficiency. Firstly, a system identification block was created that gives the mathematical model of the time-varying system using the input/output data packets of the controller system. Thanks to this block, the system is constantly monitored to update the parameters of the system, which change over time. Linear quadratic regulator (LQR) is renewed according to these updated parameters, and self-adjustment of the system is provided according to the changed system parameters. The Matlab/Simulink state-space model of the variable loaded servo (VLS) system module was obtained for the simulation experiments in this study; then the system was controlled. Moreover, experiments were carried out on the servo control experimental equipment of the virtual simulation laboratories (VSIMLABS). The effectiveness of the proposed new method was observed taking the performance indexes as a reference to obtain the results of the practical application of the proposed method. Regarding the analysis of simulation and experimental results, the proposed approach minimizes the load effect and noise and the system works at high efficiency.

Adaptive friction compensation for a class of mechanical systems based on LuGre model

AbstractThis paper deals with the problem of friction compensation for a class of uncertain mechanical systems based on LuGre model. The objective is to develop control laws such that the position variable of the system tracks a periodic desired signal or a constant desired signal. Since the friction behavior of the system with periodic desired signals is largely different from that of the system with constant desired signals, two different adaptive control methods are presented for the two cases, respectively. For the case of periodic desired signals, an adaptive repetitive learning control method is presented, and for the case of constant desired signals, an adaptive dynamic state feedback control method is presented. Both the control laws can guarantee that all the signals in the closed loop system are bounded and the tracking errors converge to zero. Several numerical simulations are carried out to show effectiveness of the proposed control algorithms.

Learning‐based adaptive feedback control for tracking optimisation in wireless sensor actuator networking systems

In wireless sensor and actuator networks (WSAN), sensors can have much longer lifetime as mobile sink nodes move through the network and collect data from static sensor nodes. Applying mobile sinks in WSAN systems has faced challenges in sink path planning and data gathering scheduling. Many studies have focused on priori-trail planning for mobile sinks; however, they have not considered tracking performance degradation during operation due to system nonlinearities and uncertain WSAN environments. Thus, two learning-based adaptive feedback control methods, adaptive neural-network-based feedback control (ANNFC) and adaptive neural-network-based feedback control with integrator model (ANNFC-IM), are proposed to improve and optimise tracking performance in unknown and uncertain WSAN environments. The proposed algorithms enable a mobile sink to adjust its flying trajectory with online learning by applying a neural-network-based control method. The performance of the proposed algorithm is validated by theoretical analysis and simulation results. The results demonstrate that the proposed methods significantly outperform existing methods in terms of tracking error, system stability, and convergence.

Adaptive Fuzzy Backstepping Tracking Control for Flexible Robotic Manipulator

In this paper, an adaptive fuzzy state feedback control method is proposed for the single-link robotic manipulator system. The considered system contains unknown nonlinear function and actuator saturation. Fuzzy logic systems (FLSs) and a smooth function are used to approximate the unknown nonlinearities and the actuator saturation, respectively. By combining the command-filter technique with the backstepping design algorithm, a novel adaptive fuzzy tracking backstepping control method is developed. It is proved that the adaptive fuzzy control scheme can guarantee that all the variables in the closed-loop system are bounded, and the system output can track the given reference signal as close as possible. Simulation results are provided to illustrate the effectiveness of the proposed approach.

Command Filter Adaptive Power Capture Control Based on Fuzzy Rules Emulated Networks for Variable Speed Wind Turbines With Flexible Shaft

This paper considers the power capture control problem of variable-speed wind turbine (VSWT) systems with flexible shaft. The control objective is to optimize the capture of wind energy by tracking the desired power output. A novel command filter based adaptive power signal feedback (PSF) control method is proposed to design the PSF controller for the non-affine nonlinear VSWT systems with a two-mass model for which the parameters are totally unknown. A compensation dynamic with adaptable parameters is designed to compensate the errors caused by command filtering and the unknown control gains. The unavailable aerodynamic torque is approximated by introducing fuzzy rules emulated networks (FRENs) and estimations of the unknown parameters and their learning laws are established to derive the virtual and actual control laws. Meanwhile, it is proved that all the signals in the closed-loop system are uniformly ultimately bounded (UUB) via Lyapunov synthesis. Finally, the feasibility of the proposed controllers is demonstrated on a 5-MW variable-speed wind turbine.

Design of adaptive feedback control for new 3D chaotic system and its digital implementation on FPGA

Background/Objectives : In this research work, digital circuit implementation on FPGA of an adaptive feedback control methodology for a new 3 – D chaotic system is proposed Methods/Statistical analysis: The chaos synchronization is achieved using adaptive feedback control method. The new adaptive controllers are designed to achieve the chaos synchronization for the identical new chaotic system. The FPGA implementation of chaos synchronization using numerical methods induces artificial suppression in the chaotic system or chaotic behavior can be dead in very short-time. In this research work, the FPGA implementation of chaos synchronization is achieved with the help of automatic code generator like System generator in Matlab simulink. The adaptive feedback control for identical new chaotic system is coded with VHDL with 32 bit fixed point number, 12 for the entire and 20 for the fraction. Findings: In this paper, we designed a new 3D chaotic system and its chaotic behavior is verified using Lyapunov exponents, stability analysis and Poincare map. The complete synchronization for proposed chaotic system is achieved using adaptive feedback control methodology. The digital circuit realization of adaptive feedback control for the synchronization of identical chaotic system based on FPGA is achieved for the various applications of digital information systems. Simulation results and FPGA outputs illustrate the effectiveness of our proposed method. Novelty/Applications: The digital implementation of adaptive feedback control has many engineering applications such as digital data transmission, digital modulation, video encryption, digital cryptosystem etc. Keywords: Chaotic system; complete synchronization; adaptive feedback control; FPGA implementation; digital implementation

Bifurcation, chaos, and their control in a wheelset model

In this paper, we present an improved wheelset motion model with two degrees of freedom and study the dynamic behaviors of the system including the symmetry, the existence and uniqueness of the solution, continuous dependence on initial conditions, and Hopf bifurcation. The dynamic characteristics of the wheelset motion system under a nonholonomic constraint are investigated. These results generalize and improve some known results about the wheelset motion system. Meanwhile, based on multiple equilibrium analysis, calculation of Lyapunov exponents and Poincaré section, the chaotic behaviors of the wheelset system are discussed, which indicates that there are more complex dynamic behaviors in the railway wheelset system with higher order terms of Taylor series of trigonometric functions. This paper has also realized the chaos control and bifurcation control for the wheelset motion system by adaptive feedback control method and linear feedback control. The results show that the chaotic wheelset system and bifurcation wheelset system are all well controlled, whether by controlling the yaw angle and the lateral displacement or only by controlling the yaw angle. Numerical simulations are carried out to further verify theoretical analyses.

Adaptive geodesic feedback controller design for the quadrotor

In this paper, a non-linear adaptive control method based on SO(3) for the quadrotor attitude tracking is proposed. Distinct from other control methods on Euclidean space, the controller proposed is developed on SO(3), which can avoid singularities and ambiguities. Besides, the geodesic on SO(3) is used to provide the shortest curve between the current attitude configuration and the desire attitude configuration. Then, a feedback controller based on geodesic is derived. To solve the problem that the inertial tensor of the quadrotor is unknown, an adaptive term is designed with the Lyapunov theory to estimate the inertial tensor. Thus, the almost global asymptotic stability of the quadrotor attitude tracking is achieved without the prior knowledge of the quadrotor inertia tensor. The numerical simulations are utilised to show better control performance of the adaptive geodesic feedback control method.

Optimizing femtosecond laser-induced upconversion luminescence enhancement and suppression of Dy3+-doped glass

Enhancing or suppressing the upconversion luminescence (UCL) of rare-earth ions has very important application prospects in various areas such as color display and optoelectronic devices. However, the control efficiency is not always optimal because the pre-designed femtosecond laser field is usually not the most suitable. The femtosecond laser pulse-shaping technique has proved to be a very well-established tool to obtain the UCL enhancement and suppression. In this letter, we experimentally realize the maximum UCL enhancement and suppression of Dy3+-doped glass by optimizing the femtosecond laser spectral phase based on a genetic algorithm. Our experimental results show that the UCL intensity can be further enhanced and also completely suppressed. The physical control mechanism of the UCL enhancement and suppression is interpreted based on a resonant-mediated two-photon absorption model and the obtained optimal femtosecond laser spectral phase. This study provides a new opportunity to optimally control the UCL of rare-earth ions, where the adaptive feedback control method can automatically approach the optimization direction in the searching space and thus obtain a global solution.

Combination synchronization and stability analysis of time-varying complex-valued neural networks

Abstract In this paper, the combination synchronization of time-varying complex-valued neural networks and parameter identification for unknown neural network topology are studied. Combination synchronization is a special synchronization for complex-valued system because real-part and imaginary-part make the neural state more complex than real system and the combination behaviors are more flexible. In addition, in the synchronization process, time-varying delays and unknown parameters are taken into consideration to make the network synchronization more practical and universal. In order to get the synchronization and identification, adaptive feedback control method is utilized and the adaptive combination synchronization controllers are proved with the Lyapunov stability theory. In the simulation, examples are given to demonstrate the robustness and effectiveness of the provided combination synchronizations.

Adaptive fuzzy control for uncertain nonlinear systems

ABSTRACTIn this paper, a survey of adaptive fuzzy for uncertain nonlinear systems is presented. The first part introduces adaptive fuzzy control emergence and some typical control methods for uncertain nonlinear systems with matching conditions (single-input single-output systems, multi-input multi-output systems). The last part presents the adaptive fuzzy state feedback and output-feedback control methods for uncertain nonlinear systems with non-matching conditions based on the backstepping technique, including strict-feedback systems, pure-feedback systems and non-strict-feedback systems.

PK/PD-based adaptive tailoring of oseltamivir doses to treat within-host influenza viral infections

Influenza A virus (IAV) is a latent global threat to human health. In view of the risk of pandemics, prophylactic and curative treatments are essential. Oseltamivir is a neuraminidase inhibitor efficiently supporting recovery from influenza infections. Current common clinical practice is a constant drug dose (75 or 150 mg) administered at regular time intervals twice a day. We aim to use quantitative systems pharmacology to propose an efficient adaptive drug scheduling. We combined the mathematical model for IAV infections validated by murine data, which captures the viral dynamics and the dynamics of the immune host response, with a pharmacokinetic (PK)/pharmacodynamic (PD) model of oseltamivir. Next, we applied an adaptive impulsive feedback control method to systematically calculate the adaptive dose of oseltamivir in dependence on the viral load and the number of immune effectors at the time of drug administration. Our in silico results revealed that the treatment with adaptive control-based drug scheduling is able to either increase the drug virological efficacy or reduce the drug dose while keeping the same virological efficacy. Thus, adaptive adjustment of the drug dose would reduce not only the potential side effects but also the amount of stored oseltamivir required for the prevention of outbreaks.

Two-Microphone Hearing Aids Using Prediction Error Method for Adaptive Feedback Control

A challenge in hearing aids is adaptive feedback control which often uses an adaptive filter to estimate the feedback path. This estimate of the feedback path usually results in a bias due to the correlation between the loudspeaker signal and the incoming signal. The prediction error method (PEM) is a popular method for reducing this bias for adaptive feedback control (AFC) in hearing aids, providing a significant performance improvement compared to conventional adaptive feedback control techniques. However, the PEM-based AFC (PEM-AFC) applications are still limited to single-microphone single-loudspeaker (SMSL) systems. This paper investigates the application of the PEM-AFC to a two-microphone single-loudspeaker hearing aid with detailed theoretical analysis as well as practical experiments. In the proposed method, PEM-AFC2, we use the two-microphone adaptive feedback control (AFC2) method with two microphones and one loudspeaker. The incoming signals at the two microphones are related by a relative transfer function (RTF) which is used to predict the incoming signal at the main microphone. In addition, a prefilter is employed to prewhiten the loudspeaker and the microphone signals before the adaptive filter estimates. As a result, the proposed method obtains a lower bias and a faster tracking rate compared to the PEM-AFC and the AFC2 method, while still maintaining a good quality of the incoming signal. A new derivation for optimal filters in the AFC2 method will also be provided. The performance of the proposed method is evaluated for speech shaped noise as incoming signal and with undermodeling the RTF as well as with perfect modeling the RTF. Moreover, different types of incoming signals and a sudden change of feedback paths are also considered. The experimental results show that the proposed approach yields a significant performance improvement compared to existing state-of-the-art AFC methods such as the PEM-AFC and the AFC2.

Fuzzy Adaptive Prescribed Performance Control for a Class of Uncertain Nonlinear Systems with Unknown Dead‐Zone Inputs

This paper proposes a fuzzy adaptive prescribed performance control scheme for a class of uncertain chaotic systems with unknown control gains and unknown dead‐zone inputs. Firstly, an error transformation is introduced to transform the original constrained system into an equivalent unconstrained one. Then, based on the error transformation technique and the predefined performance technique, a fuzzy adaptive feedback control method is developed. It is shown that all the signals of the resulting closed‐loop system are bounded. Finally, an illustrative example is given to demonstrate the effectiveness and usefulness of the proposed technique.