High-gain Feedback Research Articles

Exact output tracking in prescribed finite time via funnel control

Output reference tracking of unknown nonlinear systems is considered. The control objective is exact tracking in predefined finite time, while in the transient phase the tracking error evolves within a prescribed boundary. To achieve this, a novel high-gain feedback controller is developed that is similar to, but extends, existing high-gain feedback controllers. Feasibility and functioning of the proposed controller is proven rigorously. Examples for the particular control objective under consideration are, for instance, linking up two train sections, or docking of spaceships.

Control stability analysis of dynamic model with time delay for quadrotor UAV

For the first-order unstable object with time delay, the PD control parameter tuning method based on the traditional definition lacks consideration of the lower boundary of amplitude margin, resulting in a certain deviation between the result and the reality. In this paper, through analyzing the control loop of quadrotor UAV (QUAV), the integral link is moved from the controlled object to the controller, and the QUAV becomes a first-order unstable object, realizing the conversion of PD to PI in the hovering state. The system stability margin remains unchanged, and the position and attitude control objectives can be realized synchronously. By means of parameter setting method, the upper boundary of stability margin is obtained. Pade approximation is carried out for the time delay link, and then the lower margin of the amplitude margin is deduced according to Routh stability criterion, so as to obtain a more accurate reachable stability margin region. To avoid the high gain feedback of attitude angular velocity of QUAV, a numerical analytical setting formula for PD attitude control is derived to meet the requirements of gain and phase margin. The graph shows that the reachable region obtained by the strict definition of stability margin analysis decreases significantly. The proposed method can not only give the reachable region intuitively, but also give the tuning formula of the control parameters concisely, which has important engineering application value.

Nonlinear Robust Control of Vehicle Stabilization System with Uncertainty Based on Neural Network

To effectively suppress the effects of uncertainties including unmodeled dynamics and external disturbances in the vehicle stabilization system, a nonlinear robust control strategy based on a multilayer neural network is proposed in this paper. First, the mechanical and electrical coupling dynamics model of the vehicle stabilization system, considering model uncertainty and actuator dynamics, is refined. Second, the lumped uncertainty of the vehicle stabilization system is estimated by a multi-layer neural network and compensated by feedforward control. The high robustness of the system is ensured by constructing the sliding mode feedback control law. The proposed control method overcomes the limitations of sliding mode technology and the neural network and is naturally applied to the vehicle stabilization system, avoiding the adverse effects of high-gain feedback. Based on Lyapunov theory, it is demonstrated that the proposed controller is able to achieve the desired stability tracking performance. Finally, the effectiveness of the proposed control strategy is verified by co-simulation and comparative experiments.

A wideband inductorless LNA exploiting three-stage feedback and thermal noise canceling

In inductorless wideband low-noise amplifier (LNA) design, input-matching is traded off with noise figure (NF). The common-source noise-canceling (CSNC) structure and the gyrator alleviate this trade-off, but the corresponding study is insufficient. This work combines the CSNC and gyrator and introduces a three-stage feedback loop to achieve high feedback gain while minimizing the added input capacitance. Hence, the trade-off between NF and input impedance matching is significantly released. Consequently, the lowest NF among inductorless LNAs was achieved, while an operation bandwidth of more than 3 GHz was obtained. Post-layout simulation results in 40 nm CMOS technology show that the circuit achieves an NF of 1.35 dB–1.7 dB while maintaining a wide operation bandwidth of 100 MHz to 3.46 GHz. As far as the author knows, this is the lowest NF inductorless LNAs have achieved. Furthermore, S21 of 10.9 dB and IIP3 (input third-order intercept point) of −5.6 dB m are obtained, and the circuit consumes 14.9 mW at a 1.2 V voltage supply. Being self-biased, the circuit core consumes only 0.0032 mm2 die area.

Adaptive Multi-Input Super Twisting Control for a Quadrotor: Singular Perturbation Approach

Singular perturbations easily result in the problem of high gain. Instead of high gain feedbacks, this work proposes an adaptive two-time-scale (TTS) super twisting control (STWC) algorithm for the attitude tracking of a singularly perturbed quadrotor system (SPQS). The bounds of uncertainties are assumed to be perturbed around their nominal values. First, a nominal STWC with slow and fast control gains is designed to achieve the finite-time attitude tracking for the nominal SPQS. The finite-time stability of the close-loop system is proven by utilizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -dependent Lyapunov method. Then, dynamically adapted slow and fast control gains initialized at their nominal values are used for the accurate finite-time convergence of tracking errors to the region around the origin, which avoids overestimating the values of control gains. Experimental results demonstrate the performance of the proposed controller in terms of attitude stabilization and tracking, and robustness to external disturbances.

Adaptive disturbance rejection neural output feedback control of hydraulic manipulator systems

This paper proposes an adaptive disturbance rejection neural output feedback control (ADRNC) scheme for multi-degree-of-freedom (n-DOF) hydraulic manipulator systems, subjected to unknown nonlinearities, external disturbances and unmeasured system states. The controller design is formulated by integrating Radial Basis Function Neural Networks (RBFNNs) with state and disturbance observers using the backstepping method. The RBFNNs are synthesized to handle unknown nonlinear functions and the residual estimate error, coupled with external disturbances, is estimated through the combination of state observer and disturbance observer. The unique features of the proposed controller lies in its capability to estimate both matched and unmatched lumped disturbances. The auxiliary disturbance estimation law is guided by the neural learning weights and estimated system states provided by state observers. By effectively utilizing neural networks to approximate and mitigate most nonlinear uncertainties, the workload of the disturbance observer is substantially reduced. High-gain feedback is therefore avoided and improved tracking performance can be expected. Moreover, to avoid the tedious analysis and the problem of “explosion of complexity” in the conventional backstepping method, we employ a first-order sliding-mode differentiator. Rigorous analysis via Lyapunov methods establishes the stability of the entire closed-loop system, ensuring guaranteed and satisfactory tracking performance under the integrated influence of unknown nonlinearities, unmeasured states, and external disturbances. Extensive simulations are conducted to verify the effectiveness of the nested control strategy.

Influence of visual feedback and cognitive challenge on the age-related changes in force steadiness.

Force steadiness can be influenced by visual feedback as well as presence of a cognitive tasks and potentially differs with age and sex. This study determined the impact of altered visual feedback on force steadiness in the presence of a difficult cognitive challenge in young and older men and women. Forty-nine young (19-30yr; 25 women, 24 men) and 25 older (60-85yr; 15 women; 10 men) performed low force (5% of maximum) static contractions with the elbow flexor muscles in the presence and absence of a cognitive challenge (counting backwards by 13) either with low or high visual feedback gain. The cognitive challenge reduced force steadiness (increased force fluctuation amplitude) particularly in women (cognitive challenge × sex: P < 0.05) and older individuals (cognitive challenge × age: P < 0.05). Force steadiness improved with high-gain visual feedback compared with low-gain visual feedback (P < 0.01) for all groups (all interactions: P > 0.05). Manipulation of visual feedback had no influence on the reduced force steadiness in presence of the cognitive challenge for all groups (all P > 0.05). These findings indicate that older individuals and women have greater risk of impaired motor performance of the upper extremity if steadiness is required during a low-force static contraction. Manipulation of visual feedback had minimal effects on the reduced force steadiness in presence of a difficult cognitive challenge.

A Model-Following Control Approach to Peaking Attenuation in High-Gain Partial State Feedback for Nonlinear Systems

We consider the model-following control (MFC) scheme for a class of nonlinear minimumphase systems in Byrnes-Isidori form using the nonlinear model in the model control loop (MCL). For the control design we apply partial state feedback in the process control loop (PCL). In particular we consider a high-gain feedback of the external state in the PCL to stabilise the system globally. For the proposed MFC design the typical peaking phenomenon of the high-gain feedback can be overcome if a sufficiently accurate estimate of the initial process state is available. We further establish that the control effort of the proposed MFC design is guaranteed less than that of a comparable stabilising high-gain design using a single-loop feedback only. We demonstrate the design capability of peaking attenuation by a numerical example.

Low-power high-gain homogeneous feedback control for chain of integrators with disturbances

We present a new high-gain feedback control design ensuring finite-time input-to-state stabilization of chains of integrators of dimension n subject to additive disturbances. The proposed high-gain homogeneous controller involves only control gains of power limited to 2 regardless of the dimension of the chains of integrators in contrast to the classical high-gain controllers involving gains with powers up to n + 1 which grows with the growth of the chain of integrators. The stability analysis of the closed-loop system is achieved by means of classical Lyapunov-based tools and homogeneity-based concepts. This result can be seen as the dual design of low-power high-gain observers recently extended to the homogeneous context.

Visual Feedback Gain Modulates the Activation of Task-Related Networks and the Suppression of Non-Task Networks During Precise Grasping.

Visual feedback gain is a crucial factor influencing the performance of precision grasping tasks, involving multiple brain regions of the visual motor system during task execution. However, the dynamic changes in brain network during this process remain unclear. The aim of this study is to investigate the impact of changes in visual feedback gain during precision grasping on brain network dynamics. Sixteen participants performed precision grip tasks at 15% of MVC under low (0.1°), medium (1°), and high (3°) visual feedback gain conditions, with simultaneous recording of EEG and right-hand precision grip data during the tasks. Utilizing electroencephalogram (EEG) microstate analysis, multiple parameters (Duration, Occurrence, Coverage, Transition probability(TP)) were extracted to assess changes in brain network dynamics. Precision grip accuracy and stability were evaluated using root mean square error(RMSE) and coefficient of variation(CV) of grip force. Compared to low visual feedback gain, under medium/high gain, the Duration, Occurrence, and Coverage of microstates B and D increase, while those of microstates A and C decrease. The Transition probability from microstates A, C, and D to B all increase. Additionally, RMSE and CV of grip force decrease. Occurrence and Coverage of microstates B and C are negatively correlated with RMSE and CV. These findings suggest that visual feedback gain affects the brain network dynamics during precision grasping; moderate increase in visual feedback gain can enhance the accuracy and stability of grip force, whereby the increased Occurrence and Coverage of microstates B and C contribute to improved performance in precision grasping. Our results play a crucial role in better understanding the impact of visual feedback gain on the motor control of precision grasping.

Disturbance observer based actor-critic learning control for uncertain nonlinear systems

This paper investigates the disturbance observer based actor-critic learning control for a class of uncertain nonlinear systems in the presence of unmodeled dynamics and time-varying disturbances. The proposed control algorithm integrates a filter-based design method with actor-critic learning architecture and disturbance observer to circumvent the unmodeled dynamic and the time-varying disturbance. To be specific, the actor network is employed to estimate the unknown system dynamic, the critic network is developed to evaluate the control performance, and the disturbance observer is leveraged to provide efficient estimation of the compounded disturbance which includes the time-varying disturbance and the actor-critic network approximation error. Consequently, high-gain feedback is avoided and the improved tracking performance can be expected. Moreover, a composite weight adaptation law for actor network is constructed by utilizing two types of signals, the cost function and the modeling error. Eventually, theoretical analysis demonstrates that the developed controller can guarantee bounded stability. Extensive simulations and experiments on a robot manipulator are implemented to validate the performance of the resulted control strategy.

Prescribed-time control of perturbed nonholonomic systems by time-varying feedback

This paper studies the prescribed-time control problem of nonholonomic systems. By using some properties of a class of parametric Lyapunov equations and constructing time-varying Lyapunov-like functions, a time-varying high-gain feedback controller is firstly proposed for the chained nonholonomic system such that the state of the closed-loop system converges to zero at any prescribed time. The advantage of time-varying feedback is revealed by comparison with time-invariant feedback. Then time-varying high-gain feedback controllers for a class of perturbed nonholonomic systems are designed to achieve prescribed time convergence of the states. Both state feedback and observer-based output feedback are considered and the controls are proved to be bounded in all the cases. The effectiveness of the proposed methods are verified by numerical examples.

Model-Based Reinforcement Learning Control of Electrohydraulic Position Servo Systems

Even though the unprecedented success of AlphaGo Zero demonstrated reinforcement learning as a feasible complex problem solver, the research on reinforcement learning control of hydraulic systems is still void. We are motivated by the challenges presented in hydraulic systems to develop a new model-based reinforcement learning controller that achieves high-accuracy tracking at performance level and with asymptotic stability guarantees at system level. In this article, the proposed design consists of two frameworks: A recursive robust integral of the sign of the error (RISE) control approach to providing closed-loop system stability framework, and a reinforcement learning approach with actor-critic structure to dealing with the unknown dynamics or more specifically, the actor neural network is used to reduce the high feedback gain of the recursive RISE control approach by compensating the unknown dynamic while the critic neural network is integrated to improve the control performance by evaluating the filtered tracking error. A theoretical guarantee for the stability of the overall dynamic system is provided by using Lyapunov stability theory. Simulation and experimental results are provided to demonstrate improved control performance of the proposed controller.

Prescribed-Time Stabilization of Singularly Perturbed Systems

Dear Editor, This letter investigates the prescribed-time stabilization of linear singularly perturbed systems. Due to the numerical issues caused by the small perturbation parameter, the off-the-shelf control design techniques for the prescribed-time stabilization of regular linear systems are typically not suitable here. To solve the problem, the decoupling transformation techniques for time-varying singularly perturbed systems are combined with linear time-varying high gain feedback design techniques. A composite linear time-varying state feedback controller is designed, and the existence of the time-varying Chang transformation matrix for decoupling the slow and fast dynamics is guaranteed. As a result, the prescribed-time stability is ensured. Finally, a numerical example is provided to illustrate the effectiveness of the results.

Adaptive Jerk Control and Modified Parameter Estimation for PMLSM Servo System With Disturbance Attenuation Ability

The direct-drive industrial technology has put forward much higher requirements for the application of high-performance permanent magnet linear synchronous motor (PMLSM) servo system. To improve the control accuracy and transient performance, a novel disturbance attenuation approach is proposed based on adaptive jerk control (AJC) and modified parameter estimation for PMLSM servo system in this article. Within the control details, AJC is proposed to realize asymptotic stability without requiring <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge of disturbances, which can release the strong restriction and eliminate the design conservation. Furthermore, a novel adaptive law is designed for feedback gain to further enhance the robustness when the system meets the challenge of high-gain feedback and measurement noise. Meanwhile, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϵ</i> -modification parameter estimation is employed to approximate and compensate for the parameter variation, which can not only achieve fast convergence rate but also guarantee small overshoot. The asymptotic stability of the proposed control method is analyzed and proved by the Lyapunov theorem. Finally, the experimental results are presented to validate the efficiency with better disturbance attenuation ability.

Advanced sensorless nonlinear control strategy for grid connected photovoltaic systems via the two-time scale singular perturbation technique

This paper presents a new control strategy for grid connected photovoltaic (PV) system. The system consists of a solar panel, a boost converter, and a full-bridge inverter with L-filter connected to the grid. The control objectives are threefold: 1) tracking the maximum power point of the PV panel; 2) balancing the power exchange by regulating the DC link voltage; 3) ensuring a unity power factor in the grid. To achieve these objectives, a multi-loop controller is synthesised using a high-gain output feedback controller and the singular perturbation technique. The addressed control problem involves several difficulties including the existence of a number of state variables that are inaccessible to measurement. To address this problem, two-time-scale sliding mode observers are added which allow estimating, simultaneously, the solar panel current and the grid voltage. It is formally shown that all control objectives are asymptotically accomplished. This theoretical result is confirmed by numerical simulation tests.

Adaptive disturbance observer-based improved super-twisting sliding mode control for electromagnetic direct-drive pump

In this study, to enhance the frequency-flow characteristics of the novel electromagnetic direct-drive pump (EDDP). An adaptive disturbance observer-based improved super-twisting sliding mode control (ISTSMC-ADOB) is proposed to address the problem in which the response quality is deteriorated by factors such as parameter mismatch and disturbance. An adaptive disturbance observer (ADOB) is designed to achieve adaptive compensation, avoid the use of high-gain feedback, and extend the applicability of the conventional disturbance observer. An improved super-twisting sliding mode control (ISTSMC) based on the fast terminal sliding mode algorithm is designed to ensure faster convergence of the error in finite time. By combining the ADOB with the ISTSMC, the conservative parameter selection of the sliding mode control (SMC) is avoided, and the accuracy and robustness are further strengthened. The stability is analyzed based on Lyapunov. The results show that the proposed method effectively improves the steady-state accuracy, response speed, and robustness in trajectory tracking of the electromagnetic linear actuator for the EDDP. This further enhances the frequency-flow characteristics of the system.

Real-Time Torque Estimation of Automotive Powertrain With Dual-Clutch Transmissions

This article proposes a nonlinear observer for estimating a powertrain transmitted torque of vehicles equipped with dual-clutch transmissions. A new dynamical model of vehicle driveline is presented. It introduces a single characteristic state based on the dynamic friction model to scale the clutch torque capacity, which is available from the actuator position-to-torque map. Based on this model, the generated torque on the clutch disk can be effectively estimated in real time, and the sensitivity of the estimated value caused by high gain feedback can be reduced in the observer. Also, the engine torque uncertainty is estimated from the integrated driveline model. The Lyapunov-based analysis is presented to prove the convergence of the proposed observer. The effectiveness of the proposed method is shown by experimental validations on a test vehicle that allows for real measurements of the input shaft torque of transmission.

High-Gain Nonlinear Active Disturbance Rejection Control Strategy for Traction Permanent Magnet Motor Drives

In order to improve the performance of the traction permanent magnet motor drives without weight transducer for direct-drive elevator applications during starting operation, this article proposes a novel control method based on the high-gain nonlinear active disturbance rejection control (HNLADRC) strategy. First, the limitation of linear and nonlinear error decay function of active disturbance rejection control (ADRC) method is analyzed, which is not suitable for the complex situation at starting operation of the traction motor. A high-gain nonlinear error decay function based ADRC method is proposed, which can achieve better performance in the starting operation of the traction motor. Both the high-gain extended state observer (HNLESO) and the high-gain nonlinear error feedback law (HNLEFL) are constructed by the high-gain error decay function to improve the compensation accuracy of the starting torque and the dynamics of the speed loop. By analyzing the error decay performance of the HNLESO and the HNLEFL, it is proved that the HNLADRC method has a faster error decay rate. Experimental results show that the proposed method can obtain a shorter rollback distance and faster response capability.

High-gain observer-based output feedback control with sensor dynamic governed by parabolic PDE

In this paper it is proposed to extend the result described in Khalil and Praly (2014) and the references therein, regarding the high-gain observer-based nonlinear control to the case of systems with diffusion sensor dynamic. Based on some usual hypotheses, we provide sufficient conditions involving the high-gain parameter and the length on the PDE sensor. In fact it is brought into light an explicit trade off between them: the larger the observer gain, the smaller the length of the PDE sensor needs to be. The stability analysis of the closed loop system is based on a Lyapunov functional.