Adaptive Gain Research Articles

Synthesis of adaptive gain robust model-following/tracking controllers for uncertain systems with multiple unknown dead-zone inputs via piecewise Lyapunov functions

ABSTRACT This paper considers a design problem of adaptive gain robust model-following/tracking controllers for a class of uncertain systems with multiple unknown dead-zone inputs via piecewise Lyapunov functions. The parameters for dead-zone characteristics are assumed to be unknown, and an adaptive dead-zone inverse method is applied so as to reduce the effect for dead-zone non-linearities. Moreover, for the purpose of reducing the effects of matched and mismatched uncertainties, compensation inputs are introduced. The proposed adaptive gain robust model-following/tracking controller can achieve that the tracking error asymptotically converges to zero. In this paper, by using piecewise Lyapunov functions, we show sufficient conditions for the existence of the proposed adaptive gain robust model-following/tracking controller. Finally, an example is given to demonstrate the effectiveness of the proposed controller design method.

Fully distributed attack-resilient Nash equilibrium seeking for networked games subject to DoS attacks

This paper is concerned with distributed Nash equilibrium seeking problem for networked games that may suffer from denial-of-service (DoS) attacks. A node-based fully distributed resilient strategy for Nash equilibrium seeking is presented, where the control gains of each player can be adaptively tuned based on its local adjacent information. Then, under this strategy, the Nash equilibrium seeking can be achieved without involving any global information, provided that some conditions of DoS attacks duration can be satisfied. Moreover, an edge-based fully distributed resilient Nash equilibrium seeking strategy is presented, where an adaptive control gain is imposed on each communication link between neighbors. Finally, illustrative simulation case studies are conducted to validate the obtained results.

Fully distributed containment of a class of disturbed nonlinear multi-agent systems

In this paper, the fully distributed containment problem is investigated for a class of second-order nonlinear multi-agent systems (MASs) with disturbance and uncertain control coefficient. In the dynamics of the multi-agent systems, the nonlinear term of the multi-agent systems is Lipshitz, the leaders are subject to bounded inputs, and the disturbances are also bounded. But, the Lipshitz constant, the upper bounds of the disturbances and the leaders’ control inputs are unknown; thus, they cannot be used for the design of feedback control. To overcome the difficulty from such unknown bounds and uncertain coefficient, a new fully distributed adaptive protocol is proposed by combining the Lyapunov analysis and adaptive control technique, which based only on relative states of neighboring agents without using any global information, such that the states of the followers converge to the convex hull formed by the states of the leaders. Compared with the existing works, one main contribution is that only one adaptive gain is adopted in the fully distributed adaptive protocol to deal with containment problem of second-order nonlinear MASs, where four unknown parameters are involved. Two simulation examples are given to show the performance of the proposed protocol.

Adaptive Neural Inverse Optimal Tracking Control for Uncertain Nonlinear System With Actuator Faults

Most optimal control method aim at finding a control law to minimize the corresponding cost function, and the key problem is to solve the Hamilton–Jacobi–Bellman equation (HJBE). However, the solution of HJBE is hard and even unavailable to obtain for nonlinear system and, thus, the optimal control method can not be developed. To address this problem, an adaptive neural inverse optimal control method is proposed for nonlinear systems with external disturbances and actuator failures in this article. The cost function in this article is defined based on the controller and, therefore, once the controller is obtained, the corresponding cost function is minimized. Accordingly, the directly solution of HJBE is avoided. In more detail, the auxiliary system is constructed to develop the auxiliary controller, based on which the actual optimal fault-tolerant controller can be further obtained. The uncertainties in system dynamics are compensated by neural networks, and the unknown actuator failures are handled by adaptive control gain. It is proved that nonlinear system is input-to-state stabilizable (ISS) and the proposed controller can minimize the cost function that puts penalty on tracking error and system input. Two simulation examples are provided to demonstrate the effectiveness of the proposed control method.

Adaptive Learning Gain-Based Control for Nonlinear Systems With External Disturbances: Application to PMSM

The performance of backstepping control with an external disturbance guarantees the upper bound of the tracking error, which is determined by an unknown upper bound of the disturbance and design parameters. Therefore, tuning the design parameters is necessary to obtain a satisfactory control performance. Generally, a high gain is used to reduce the tracking error under the unknown disturbance in the previous methods. However, the use of an unnecessarily high gain may amplify the high-frequency measurement noise and peaking phenomenon. In this study, we propose an adaptive learning gain (ALG)-based nonlinear control for a strict-feedback nonlinear system with an external disturbance to achieve the desired control performance without any information about the disturbance and gain tuning. An ALG-based nonlinear controller using a backstepping procedure is designed to track the desired trajectory. The ALG update law is established to suppress the tracking error within the desired bound. The amplification of the measurement noise in the control input is reduced, as the ALG is updated to avoid the use of an unnecessarily high gain when the controller achieves more than the desired performance.

Reliable Frequency Control Support Scheme Based on Wind Power Generator Combined with Rechargeable Energy Storage System Applying Adaptive Power Reference

To reduce carbon emissions in the atmosphere, the utilization of renewable energy sources has been on the rise. However, as their integration level increases, grid system operators require higher performance of the frequency response service for renewable energy sources, especially wind power generators (WPGs). Conventional frequency control schemes release kinetic energy depending on the fixed and adjustable gains in the system difference loop between the standard and current system frequency; however, these conventional schemes cannot provide frequency support outside of the rotor’s speed operating region. In this work, a frequency regulation support strategy employing a WPG and lithium-ion battery based on an adaptable power reference is implemented. This is accomplished by assigning different roles to the WPG and battery. As the primary frequency control support, the WPG uses a frequency deviation loop with adaptable gain which depends on the speed of the rotor and the difference in frequency. Additionally, to assist with the frequency control support, the battery operates based on its state-of-charge (SOC) and rotor speed of the WPG. For investigating the capability of the suggested technique, an IEEE 14-bus system is employed. Qualitative wake effect analysis is further presented in the study to determine the feasibility of the proposed approach which consists of the hybrid WPP–battery system for frequency regulation. The main limitations of this study and further research studies that can be performed in the future to improve the performance of the proposed technique are presented. The scenario study results show that the minimum frequency point during a synchronous generator trip obtains a higher value than conventional ones in the suggested strategy by releasing more stored energy from the WPG and the battery.

Full-order adaptive sliding mode control with extended state observer for high-speed PMSM speed regulation

In order to achieve speed control of high-speed permanent magnet synchronous motor with high precision, the sliding mode control (SMC) is usually adopted. However, the inherent chattering phenomenon affects the speed control performance. In order to solve this problem, a composite speed regulator is proposed in this paper, which is made up of two parts: the adaptive full-order SMC and extended state observer (ESO). Switching gain adaption law is proposed for minimizing chattering as much as possible while ensuring the robustness of sliding mode control. The total disturbance is estimated by the ESO and through feedforward, thus improving the system anti-disturbance ability. Finally, the effectiveness of the proposed speed regulator has been validated in the test bench.

Stabilization of a chaotic oscillator via a class of integral controllers under input saturation

This work presents the straightforward design of an integral controller with an anti-windup structure to prevent undesirable behavior when actuator saturation is considered, and the proposed controller improves the performance of the closed-loop dynamics of a class of nonlinear oscillators. The proposed integral controller has an adaptive control gain, which includes the absolute value of the named control error to turn off the integral action when it is saturated. Closed-loop stability analysis is performed under the Lyapunov theory framework, where it can be concluded that the system behaves in an asymptotically stable way. The proposed methodology is successfully applied to a Rikitake-type oscillator, considering a single input-single output (SISO) structure for regulation and tracking trajectory purposes. For comparison, an equivalent fixed gain integral controller is also implemented to analyze the corresponding anti-windup properties of the proposed control structure. Numerical experiments are conducted, showing the superior performance of the proposed controller.

Sensorless Adaptive Observers for Linearly Parameterized Class of Induction Machine

In this paper a new adaptive flux, speed, load torque and rotor resistance observer is proposed for induction machine drive system. The structure of the proposed observer is simple and it is able to give rise to different observers among which adaptive high gain and adaptive sliding mode. We adopt a mild change of coordinates that allows to easily design of adaptive observer. Computer simulation results verify the validity of the proposed estimation algorithm.

A robust adaptive linear parameter-varying gain-scheduling controller for aeroengines

This paper presents an on-board model-based adaptive linear parameter-varying (LPV) gain-scheduling controller for aeroengines exhibiting strong nonlinearity, uncertainty, and component performance degradation. A novel hybrid on-board adaptive model is proposed using a dual-loop (fast-slow) structure, which consists of an adaptive LPV model as the fast loop to guarantee the on-board performance, and a component-level model as the slow loop to provide accurate model information at a slow update frequency, without violating the real-time requirement of the fast loop. An innovative mixed adaptive extended Kalman filter is designed to estimate and update the model, by introducing an extended-Kalman-filter-based covariance scaling mechanism and updating the system noise covariance matrix by employing full covariance estimation. The component-level adaptive nonlinear model and mixed adaptive extended Kalman filter increase the accuracy of the on-board model, while the adaptive LPV model enhances its real-time performance. Finally, a robust polytopic adaptive LPV gain-scheduling controller is designed based on the adaptive LPV model by introducing a new scheduling variable, i.e., health parameters estimated by the on-board adaptive model. The proposed adaptive model and controller are implemented to a high fidelity nonlinear aeroengine model. Simulation results demonstrate that the estimation error of health parameters and component degradation level of the proposed method is 0.0763%, which gives an order of magnitude higher health parameter and component degradation level estimation accuracy compared with the other commonly applied models. The proposed model-based adaptive LPV gain scheduling control scheme has a steady-state error of 0.0028%, gives one order of magnitude higher control accuracy than that of three state-of-the-art baseline controllers while demonstrating significantly reduced overshoot and adjustment time. This verifies the robustness and effectiveness of the proposed controller, and verifies that the model can meet the technical requirements of the engine control system and significantly improve the engine control performance.

An Improved Deadbeat Control Scheme for Unipolar Sinusoidal Current Excited Switched Reluctance Motor Drives

This article reports on a novel deadbeat control (DBC) scheme to improve the current regulation performance of the unipolar sinusoidal excited control strategy for switched reluctance motors (SRMs). In this scheme, the deadbeat controller with delay compensation is first designed by using a second-order inductance-based SRM model in the rotating reference frame. This developed model can provide a more accurate description of the torque and electromagnet characteristics compared with the existing model. However, ignoring the magnetic saturation during the modeling inevitably leads to uncertainties in the proposed DBC system. To further estimate the disturbance caused by these uncertainties and improve robustness, a simple adaptive disturbance observer (ADO) based on the steepest descent method is embedded in the deadbeat controller. Then, a stability analysis based on the discrete-time Lyapunov function is used to guarantee convergence and tune the adaption gain of the proposed ADO. Finally, comparative experiments under different operation conditions are carried out on a three-phase 12/8 SRM prototype to verify the effectiveness of the proposed scheme.

A composite adaptive robust control for nonlinear systems with model uncertainties

AbstractThis article is concerned with a composite adaptive robust control (CARC) for non‐strict feedback nonlinear systems with model uncertainties including parametric uncertainties and uncertain nonlinearities. It is assumed that the model uncertainties of the nonlinear system are bounded but unknown. Firstly, prescribed tracking performance is achieved by the CARC control law even the unknown parameters are time‐varying. This is accomplished firstly by designing a discontinuous projection that confines the unknown parameters in an estimated region. Then the robustness of the closed‐loop system is guaranteed by leveraging the deterministic robust control philosophy. Also, a command filter has been designed to avoid computing complex derivatives of virtual control law in the backstepping procedure. Secondly, a composite adaptation law that intelligently integrates information of both tracking error and prediction error is constructed for improving the performance of controllers. The high adaptation gain of CARC leads to higher parameter convergence speed and smaller tracking errors. Moreover, if the actual parameters are within the design range, asymptotic stability can be achieved in presence of parametric uncertainties only. In addition, exponential convergence of tracking errors and parameters can be shown under certain conditions. Finally, numerical examples are made to validate the superiority and effectiveness of the proposed control scheme.

Zoom behavior during visual search modulates pupil diameter and reflects adaptive control states.

Adaptive gain theory proposes that the dynamic shifts between exploration and exploitation control states are modulated by the locus coeruleus-norepinephrine system and reflected in tonic and phasic pupil diameter. This study tested predictions of this theory in the context of a societally important visual search task: the review and interpretation of digital whole slide images of breast biopsies by physicians (pathologists). As these medical images are searched, pathologists encounter difficult visual features and intermittently zoom in to examine features of interest. We propose that tonic and phasic pupil diameter changes during image review may correspond to perceived difficulty and dynamic shifts between exploration and exploitation control states. To examine this possibility, we monitored visual search behavior and tonic and phasic pupil diameter while pathologists (N = 89) interpreted 14 digital images of breast biopsy tissue (1,246 total images reviewed). After viewing the images, pathologists provided a diagnosis and rated the level of difficulty of the image. Analyses of tonic pupil diameter examined whether pupil dilation was associated with pathologists' difficulty ratings, diagnostic accuracy, and experience level. To examine phasic pupil diameter, we parsed continuous visual search data into discrete zoom-in and zoom-out events, including shifts from low to high magnification (e.g., 1× to 10×) and the reverse. Analyses examined whether zoom-in and zoom-out events were associated with phasic pupil diameter change. Results demonstrated that tonic pupil diameter was associated with image difficulty ratings and zoom level, and phasic pupil diameter showed constriction upon zoom-in events, and dilation immediately preceding a zoom-out event. Results are interpreted in the context of adaptive gain theory, information gain theory, and the monitoring and assessment of physicians' diagnostic interpretive processes.

Resilient Distributed State Estimation for LTI Systems Under Time-Varying Deception Attacks

This article studies the resilient distributed state estimation over the sensor networks under measurement attacks, which make the measurements of variant subsets of sensors aberrant at different time instants. For this problem, while most of the existing works focus on the static target states that do not change over time, we investigate the estimation for the dynamic ones, which evolve according to general linear time-invariant (LTI) systems. To achieve the resilient distributed state estimation for the general LTI systems under the measurement attacks, we propose a dynamic-target regulative gain estimation (DTRGE) algorithm, in which an attack detector, a regulative gain matrix, and an adaptive gain are designed. The detector helps agents monitor the measurement anomalies, and once the attacks are detected, the adaptive gain can counteract the deviation of the estimates induced by them. The regulative gain matrix restrains the negative effects on the convergence of the estimates caused by the system matrix of the target LTI system, especially the unstable one. We demonstrate that all the sensors can recover the target state by running the DTRGE algorithm, if the topology and the observability of the sensor network satisfy certain conditions. Moreover, we further apply the DTRGE algorithm to the sensor networks with switching topologies, and demonstrate that the estimation task can also be completed by these sensors. Finally, simulation and experiment results are given to illustrate the performance of the DTRGE algorithm.

Isokinetic Muscle Strength Training Strategy of an Ankle Rehabilitation Robot Based on Adaptive Gain and Cascade PID Control

Isokinetic muscle strength training refers to the mode of movement based on constant speed and variable resistance, which can guarantee the maximum resistance and force–distance output of each muscle in different angles of exercise. In this article, we develop an isokinetic muscle strength training strategy based on the adaptive gain and cascade proportion integration differentiation (PID) controller for ankle rehabilitation on our newly developed ankle robotic system. First, a heuristic threshold intention recognition method based on time-series integration was proposed to precisely recognize the motion intention, thus inducing the motion in this direction. Then, an adaptive gain algorithm was developed to provide speed gain for the isokinetic training, to avoid jitter during speed switching. Finally, the isokinetic characteristic was realized by the cascade PID controller, which can control the motion velocity in a given range with fast response speed. Experiments with healthy subjects showed good performance in the smoothness of the control system, the accuracy, and the real-time performance of velocity tracking. By introducing the isokinetic characteristic in ankle rehabilitation, the ankle robot can provide resistance training at a constant speed no matter how much force the patient uses, which is a very functional supplement and improvement for ankle rehabilitation.

An Improved Setting-Free Scheme for Fast and Reliable Detection of Complete and Partial Loss-of-Excitation

In this article, an improved setting-free loss-of-excitation (LOE) protection is developed for fast and reliable classification of various events. In the proposed scheme, the internal voltage and reactive power of the synchronous generator (SG) have been exploited to fulfill the condition of not needing the threshold amount because they are directly dependent on the rotor field and continuously reduced during the LOE, leading to having a negative polarity of variation. However, due to slow dynamic variation during the LOE condition, they are multiplied to accelerate and highlight the reduction changes, giving rise to increasing the detection speed. Meanwhile, owing to the considerable difference between the value of internal voltage and reactive power and to highlight the effect of multiplication function, an adaptive gain on the basis of steady-state parameters of SG is suggested. Furthermore, given that the negative polarity of the multiplied parameters can be affected during the hybrid events, the limited integral function is implemented to eliminate the happened swings, resulting in keeping the negative polarity and thus clustering the hybrid events reliably. The performed simulations on the IEEE 9-bus and 39-bus test systems prove that the provided setting-free protection can detect all types of the events reliably and quickly than other schemes.

Maximum Power Point Tracking in Photovoltaic Systems Based on Global Sliding Mode Control with Adaptive Gain Scheduling

Maximum power point tracking (MPPT) controllers have already achieved remarkable efficiencies. For smaller photovoltaic (PV) systems, any improvement will not really be worth mentioning as an achievement. However, for large solar farms, even a fractional improvement will eventually create a significant impact. This paper presents an MPPT control scheme using global sliding mode control (GSMC) with adaptive gain scheduling. In the two-loop controller, the first loop determines the maximum power point (MPP) reference using online calculations, while the GSMC with adaptive gain scheduling in the second loop adjusts the boost converter’s pulse width modulation (PWM) to force the PV system to operate at the MPP with improved performance. The adaptive gain scheduling regulates the gain of the switching control to maintain the controller performance over a wide range of operating conditions, while GSMC guarantees the system robustness throughout the control process by eliminating the reaching phase and improving MPPT performance. The overall PV system also has Lyapunov stability. Furthermore, the robustness analysis of the proposed controller is also performed under load variations and parametric uncertainties at various temperatures and irradiances. In the simulations, the proposed MPPT control scheme has shown faster response than other controllers, reaching the set point with rise time 0.03 s as compared to 0.07 s and 0.13 s for quasi sliding mode control (QSMC) and conventional sliding mode control (CSMC), respectively. The proposed controller showed an overshoot of 1.2 V around a steady state value of 21.9 V as compared to 1.51 V and 1.45 V, respectively, for QSMC and CSMC for a certain parametric variation. Furthermore, the proposed controller and the QSMC-based scheme showed a steady-state error of 0.3 V, while the CSMC-based approach has a more significant error. In conclusion, the proposed MPPT control scheme has a faster response and low tracking error with minimal oscillations.

Learning generalized Nash equilibria in monotone games: A hybrid adaptive extremum seeking control approach

In this paper, we solve the problem of learning a generalized Nash equilibrium (GNE) in merely monotone games. First, we propose a novel continuous semi-decentralized solution algorithm without projections that uses first-order information to compute a GNE with a central coordinator. As the second main contribution, we design a gain adaptation scheme for the previous algorithm in order to alleviate the problem of improper scaling of the cost functions versus the constraints. Third, we propose a data-driven variant of the former algorithm, where each agent estimates their individual pseudogradient via zeroth-order information, namely, measurements of their individual cost function values. Finally, we apply our method to a perturbation amplitude optimization problem in oil extraction engineering.

Emerging robust and data‐driven control methods for uncertain learning systems

Emerging robust and data‐driven control methods for uncertain learning systems

Adaptive generalized super twisting sliding mode control for PMSMs with filtered high-gain observer

This paper proposes a novel adaptive-gain generalized super twisting algorithm for permanent magnet synchronous motors. The stability of this algorithm is strict proof using the Lyapunov method. Both controllers of the speed-tracking loop and the current regulation loop are designed according to the proposed adaptive-gain generalized super twisting algorithm. Dynamically adjusted gains in the controllers can improve the transient performance and system’s robustness while reducing chattering. A filtered high-gain observer is applied in the speed-tracking loop to estimate the lumped disturbances, including parameter uncertainties and external load torque disturbances. The estimates feeding forward to the controller further improve the robustness of the system. Meanwhile, the linear filtering subsystem reduces the sensitivity of the observer to the measurement noise. Finally, experiments are constructed using the adaptive gain generalized super twisting sliding mode algorithm and the fixed gain one, showing the effectiveness and advantages of the proposed control scheme.