Unknown External Perturbations Research Articles

Multivariable adaptive decoupling control based on stochastic configuration networks with serial-parallel switching distribution

A multivariable adaptive decoupling control scheme is proposed based on stochastic configuration networks with serial-parallel switching distribution (SPSCN). Firstly, a linear controller is designed by combining a PID controller, feedback decoupling, and one-step optimal control. Secondly, a nonlinear controller is presented to deal with higher-order nonlinear terms and unknown external perturbations. SPSCN is used to improve the prediction accuracy of unmodeled dynamics. It combines uniform and normal search strategies in a serial-parallel fashion, aiming at improving the node quality and reducing the model complexity. The approximation performance of the SPSCN algorithm is demonstrated by performing approximation experiments with two functions and four benchmark datasets. Compared with the generalized minimum variance control (GMVC) algorithm in controlling the process of cement raw material decomposition, our proposed control scheme outperforms.

Boundary Disturbance Rejection Control for Fractional-order Multi-agent Systems with Reaction-diffusion

This paper focuses on boundary disturbance rejection control (BDRC) for fractional-order multi-agent systems (FMASs) with reaction diffusion terms. Firstly, based on the output information, the corresponding observer is designed to obtain information about all the agents and the unknown external perturbations. Then, a novel distributed boundary control protocol is designed based on the observed values. Thus, the convergence analysis of the closed-loop system is accomplished through the Mittag-Leffler stability theory and the linear matrix inequality condition. Finally, a simulation example is used to verify the correctness of the results.

Adaptive fixed-time robust control for function projective synchronization of hyperchaotic economic systems with external perturbations

In today's sophisticated world, where financial systems play an increasingly important role, the application of controllers through management strategies is of crucial importance, given their vital role. An analysis of the synchronization of economic systems can provide a valuable insight into the financial synchronous development of different countries or regions around the world. In this paper, an adaptive fixed-time control strategy is presented for the function projective synchronization of drive-response hyperchaotic economic systems with unknown parameters and external perturbations. The developed controller is constructed by combining the fixed-time control approach and parametric adaptation technique. Specifically, the adaptive adjusting mechanisms are designed to estimate the unknown parameters and external perturbations, respectively. Stability investigation shows that the function projective synchronization errors under the developed controller can stabilize to the minor fields about zero in fixed time. A key feature of the developed controller is its high insensitive to unknown parameters and strong robustness against external perturbations. Finally, the effectiveness and merits of the presented control strategy are examined by simulation scenarios.

Novel adaptive law for super-twisting controller: USV tracking control under disturbances

A novel adaptive super-twisting control algorithm, for Unmanned Surface Vehicles (USV) tracking control is presented. The proposed adaptive law is obtained using a Lyapunov approach, to analyze the system closed-loop stability. Furthermore, several conditions are given to guarantee robustness in presence of unknown bounded disturbances/uncertainties, chattering mitigation and the finite-time convergence. This adaptive control strategy has the advantage that the controller gains, which are in terms of a single control parameter, requires adjusting a small number of parameters compared with other adaptive strategies, moreover and its dynamics is smooth, improving the controller performance. To assess the proposed control methodology effectiveness, a trajectory tracking control is designed and implemented on an unmanned surface vehicle under the action of bounded unknown uncertainties and external perturbations. Numerical simulations and experimental results conducted with a vessel prototype demonstrate the performance and advantages under payload variations and external environmental conditions. Finally, a comparative study between the proposed approach with other adaptive super-twisting works has been conducted.

Unified neural output-constrained control for space manipulator using tan-type barrier Lyapunov function

In this paper, a unified neural control scheme is presented for the output-constrained trajectory tracking of space manipulator under unknown parameters and external perturbations. By utilizing the backstepping control technique as the main framework, the proposed controller is developed with the help of neural network (NN) and tan-type barrier Lyapunov function (BLF). The NN is introduced to identify the unknown part in the dynamic model of the space manipulator. Benefiting from the neural identification, the proposed controller is model-free and insensitive to external perturbations. Moreover, the BLF is adopted to guarantee the position tracking errors never exceed the predefined output constraints. Different from log-type BLF, the tan-type BLF is employed for the control design, which makes the proposed controller universal for the cases with and without considering the output constraints. The semiglobal uniform ultimate boundedness of the resulting closed-loop system is strictly obtained through stability argument. All error variables in the closed-loop system can eventually stabilize to the small residual sets about zero under the proposed controller. Lastly, simulations and comparisons are given to demonstrate the effectiveness and excellent tracking performance of the proposed control scheme.

Robust Finite-Time Tracking Control Based on Disturbance Observer for an Uncertain Quadrotor under External Disturbances

In this paper, a robust flight control system is proposed for an autonomous quadrotor to quickly and accurately achieve the targeted trajectories. A novel supertwisting nonsingular terminal sliding mode control (STNTSMC) has been developed to ensure that the tracking errors vanish in a short finite-time. A nonlinear disturbance observer (DO) is incorporated into the control system to estimate the unknown external perturbations and to strengthen the system’s robustness. The Lyapunov theory is used to verify the closed-loop stability of the synthesized controller. Moreover, processor-in-the-loop (PIL) implementations are performed to validate the efficacy of the suggested method. The merit of the proposed DO-STNTSMC is evaluated under multiple flight scenarios. The obtained results demonstrate that the proposed controller has a highly reduced tracking error and strong robustness against random parameter changes and external disturbances, compared to conventional nonsingular terminal sliding mode control. Finally, experimental tests are conducted to validate the performance of the suggested method.

Fuzzy-Based Adaptive Higher-Order Sliding Mode Control for Uncertain Steer-by-Wire System

Abstract This paper proposes an adaptive higher-order sliding mode (AHOSM) control method based on the adaptive fuzzy logic system for steer-by-wire (SbW) system to achieve the tracking control of the front wheels steering angle. First, an adaptive fuzzy logic system is adopted to estimate the unknown dynamics of the SbW system. Then, the AHOSM control is constructed to overcome the lumped uncertainties including unknown external perturbation and fuzzy logic system approximation error and has the advantage of attenuating the chattering caused by the discontinuous control signal. Finally, the adaptation scheme is designed for the dynamic gain of the proposed AHOSM controller without a priori knowledge of the bounds of the uncertainties. In contrast to the existing controllers applied in the SbW system, this controller has a better control performance in practical application. By means of Lyapunov stability analysis, it is theoretically proved that the system trajectory converges to an adjustable neighborhood of the origin in finite time. Simulations and vehicle experiments are carried out to verify the effectiveness of the proposed approach.

Super-Twisting Sliding Mode Control Law Design for Attitude Tracking Task of a Spacecraft via Reaction Wheels

The attitude control has been recognized as one of the most important research topics for spacecraft. If the desired attitude trajectory cannot be tracked precisely, it may cause mission failures. In the real space mission environment, the unknown external perturbations, for example, atmospheric drag and solar radiation, should be taken into consideration. Such external perturbations could deviate the precision of the spacecraft orientation and thereby lead to a mission failure. Therefore, in this paper, a quaternion-based super-twisting sliding mode robust control law for the spacecraft attitude tracking is developed. The finite time stability based on the formulation of the linear matrix inequality (LMI) is also provided. To avoid losing the control degree of freedom due to the certain actuator fault, a redundant reaction wheels configuration is adopted. The actuators distribution associated force distribution matrix (FDM) is analyzed in detail. Finally, the reference tangent-normal-binormal (TNB) command generation strategy is implemented for simulating the scenario of the space mission. Finally, the simulation results reveal that the spacecraft can achieve the desired attitude trajectory tracking demands in the presence of the time-varying external disturbances.

Finite-Time Current Tracking in Boost Converters by Using a Saturated Super-Twisting Algorithm

The power converters are widely used in several industrial applications where it is necessary to obtain from a fixed voltage another one higher or lower than the original. In this paper, we focus on the DC-DC (direct current) boost converters, where to guarantee the desired voltage, an internal current tracking loop is usually used. However, this tracking cannot be assured in the presence of unknown load changes and external perturbations when traditional controller strategies are implemented. In this paper, an advanced control strategy is proposed to ensure the current tracking using a saturated super-twisting controller on the power converter. The finite-time current tracking of a DC-DC boost converter is assured in the presence of bounded Lipschitz perturbations composed by unknown load changes and exogenous signals. The proposed approach generates a continuous bounded control signal applied to the converter by using a sigma-delta modulator Σ Δ M . The controller gains are tuned to obtain finite-time stabilization of the tracking error, while the control signal remains bounded. To illustrate the effectiveness of the proposed results, the controller is applied to a physical boost converter using the hardware implemented Σ Δ M and an STM32 Discovery development card. Besides, the controller is compared with a first-order sliding mode controller showing that for small sample times, the energy of the error signal is reduced.

Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension

Cost-competitiveness of offshore wind depends heavily in its capacity to switch preventive maintenance to condition-based maintenance. That is, to monitor the actual condition of the wind turbine (WT) to decide when and which maintenance needs to be done. In particular, structural health monitoring (SHM) to monitor the foundation (support structure) condition is of utmost importance in offshore-fixed wind turbines. In this work a SHM strategy is presented to monitor online and during service a WT offshore jacket-type foundation. Standard SHM techniques, as guided waves with a known input excitation, cannot be used in a straightforward way in this particular application where unknown external perturbations as wind and waves are always present. To face this challenge, a vibration-response-only SHM strategy is proposed via machine learning methods. In this sense, the fractal dimension is proposed as a suitable feature to identify and classify different types of damage. The proposed proof-of-concept technique is validated in an experimental laboratory down-scaled jacket WT foundation undergoing different types of damage.

Stabilization of Robots With a Regulator Containing the Sigmoid Mapping

Actuators nonlinearities are unknown external perturbations in robots, which are unwanted because they can severely limit their performance. This research is focused on the stabilization of robots subject to actuators nonlinearities with a regulator containing the sigmoid mapping. Our regulator has the following three main characteristics: a) a sigmoid mapping is used to ensure boundedness of the regulator law terms, b) the chattering is reduced by the usage of the saturation mapping instead of the signum mapping, and c) the stabilization is ensured by the Lyapunov analysis. Finally, we evaluate our regulator for the stabilization of two robots.

Path Following Control of Autonomous Ground Vehicle Based on Nonsingular Terminal Sliding Mode and Active Disturbance Rejection Control

Due to the strong nonlinearity, coupling characteristics, external disturbance, and complex driving conditions, it is difficult to establish an accurate mathematical model for the autonomous ground vehicle (AGV). This requires the AGV path following controller to have strong robustness. In this paper, a robust AGV path following control strategy that is based on nonsingular terminal sliding mode (NTSM) and active disturbance rejection control (ADRC) is presented. First, the complex path following problem is simplified to a simple yaw angle tracking problem by constructing a desired yaw angle function that satisfies that the displacement deviation of AGV converges to zero when the actual yaw angle approaches the desired yaw angle. Second, an NTSM-ADRC controller is designed for the system, which uses the extended state observer to estimate and compensate the unmodeled dynamics and unknown external perturbations of the system in real time. In order to improve response characteristics of the controller, the nonlinear error feedback control law is designed by combining the NTSM and exponential approximation law. In contrast to the existing work, the improved controller can use the simple two-degree-of-freedom linear vehicle dynamic model to provide good performance in a range of driving conditions. Finally, the CarSim-Simulink simulation results of typical conditions show that the proposed control strategy can make the AGV follow the reference path quickly and accurately while ensuring the stability of the vehicle and has strong robustness.

Adaptive sliding mode control for a class of uncertain nonlinear fractional-order Hopfield neural networks

The stabilization problem for a class of uncertain nonlinear fractional-order Hopfield neural networks (FOHNNs) is solved by adaptive sliding mode control (ASMC). The uncertain internal parameters and the unknown external nonlinear perturbations are estimated by adaptive techniques. Firstly, a switched sliding mode surface (SMS) of uncertain nonlinear FOHNNs is presented. Secondly, in order to guarantee the stability of uncertain nonlinear FOHNNs, an effective sliding mode controller (SMC) is designed. According to the fractional-order Lyapunov theory, the sliding mode asymptotically converges to the origin in finite time. Finally, a numerical example of a three-dimensional uncertain nonlinear FOHNNs is given to demonstrate the effectiveness of the proposed method.

An adaptive super-twisting sliding mode algorithm for robust control of a biotechnological process

In this paper, a robust super-twisting sliding mode control with adaptive tuning law is developed for a nonlinear biotechnological process, which takes place inside a continuous stirred tank bioreactor. A super-twisting algorithm (STA) is firstly designed to obtain high robustness as well as preserve fast convergence with high accuracy. The benefit of this approach is that its design procedure is independent of the prior knowledge of the bound value of the uncertainties and perturbations. However, the STA has a drawback that provides a chattering in the control loop. In order to overcome this drawback, an adaptive tuning algorithm is developed to adjust the STA control law without frequency switching and alleviate the undesired chattering phenomenon. Then, the robustness can be achieved despite the existence of the unknown uncertainties and external perturbations for the nonlinear process. In addition, a formal proof of the global uniform asymptotic stability based on Lyapunov criterion of the closed-loop process is derived. Several simulation results show that the proposed adaptive super-twisting algorithm guarantees the performance of the STA under external disturbance and parametric uncertainty with less chattering and illustrate the overall performance improvements.

Event-triggered neuroadaptive control for postcapture spacecraft with ultralow-frequency actuator updates

Abstract This paper investigates an event-triggered neuroadaptive control approach for postcapture flexible spacecraft with guaranteed prespecified tracking performance in the presence of unknown inertial properties, actuator constraints, and external space perturbations. By employing the minimum-learning parameter technique into the neural proportional integral-like controller, only two adaptive parameters are required to update online, which completely avoids the tedious inertial parameter identifications and dramatically reduces the complexity of controller in the meanwhile. Compared with existing works, the primary advantage of the proposed attitude control approach is that the actuator updates are determined by the prescribed event-based conditions in an aperiodic way rather than a periodic one, which greatly reduces the actuator updates. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed approach in terms of attitude stabilization and tracking for the postcapture flexible spacecraft.

Control del Convertidor CD/CD Reductor–Paralelo Implementado en FPGA

El presente artículo documenta el diseño e implementación de un controlador robusto para el convertidor CD/CD Reductor–Paralelo, bajo la perspectiva de la planitud diferencial y la técnica del control por rechazo activo de perturbaciones basado en un observador GPI. Los objetivos principales del controlador propuesto son: regular la tensión de salida y equilibrar las corrientes de los convertidores en paralelo. Para el modelado de la arquitectura hardware de este controlador se utiliza una herramienta con alto nivel de abstracción de hardware y se implementa en un FPGA Artix–7 de la compañía Xilinx. Finalmente, se muestran resultados experimentales del desempeño del controlador, cuando se somete al convertidor a diversas perturbaciones externas, desconocidas y variantes con el tiempo, y a cambios en los parámetros del mismo.

Sliding mode collision-free navigation for quadrotors using monocular vision

SUMMARYSafe and accurate navigation for autonomous trajectory tracking of quadrotors using monocular vision is addressed in this paper. A second order Sliding Mode (2-SM) control algorithm is used to track desired trajectories, providing robustness against model uncertainties and external perturbations. The time-scale separation of the translational and rotational dynamics allows to design position controllers by giving a desired reference in roll and pitch angles, which is suitable for practical validation in quad-rotors equipped with an internal attitude controller. A Lyapunov based analysis proved the closed-loop stability of the system despite the presence of unknown external perturbations. Monocular vision fused with inertial measurements are used to estimate the vehicle's pose with respect to unstructured scenes. In addition, the distance to potential collisions is detected and computed using the sparse depth map coming also from the vision algorithm. The proposed strategy is successfully tested in real-time experiments, using a low-cost commercial quadrotor.

A Control Scheme to Minimize Muscle Energy for Power Assistant Robotic Systems Under Unknown External Perturbation.

This paper proposes a novel control method to minimize muscle energy for power-assistant robotic systems that support the intended motions of a user under unknown external perturbations, using surface electromyogram (sEMG) signals. Conventional control methods based on force/torque (F/T) sensors have limitations to detect human intentions and could, presumably, misunderstand or distort such intentions because of external perturbations of the interaction forces, such as those found in activities of daily living. F/T sensors measure the sum of the applied force, including unknown external forces and human intention; thus, a power-assistant robot controller cannot exactly decompose the real human force. In this paper, we describe a counterexample that cannot be supported by conventional force-sensor-based control methods. We also verify why these control methods may guide human behavior in the wrong direction, and thus, have limitations under unknown external perturbations. We then propose a new control method to minimize the muscle energy indicated by sEMG signals. The proposed control approach is fundamentally based on the concept of power-assistance, in which a robot can reduce the users expended muscle energy while performing given tasks. The proposed control approach is verified through experiments using a power-assistant robotic system for the upper limbs under external perturbations.

Almost output regulation of LFT systems via gain-scheduling control

ABSTRACTOutput regulation of general uncertain systems is a meaningful yet challenging problem. In spite of the rich literature in the field, the problem has not yet been addressed adequately due to the lack of an effective design mechanism. In this paper, we propose a new design framework for almost output regulation of uncertain systems described in the general form of linear fractional transformation (LFT) with time-varying parametric uncertainties and unknown external perturbations. A novel semi-LFT gain-scheduling output regulator structure is proposed, such that the associated control synthesis conditions guaranteeing both output regulation and disturbance attenuation performance are formulated as a set of linear matrix inequalities (LMIs) plus parameter-dependent linear matrix equations, which can be solved separately. A numerical example has been used to demonstrate the effectiveness of the proposed approach.

Optimal estimation and filtration under unknown covariances of random factors

The general schemes of linear estimation and filtration were considered on assumption of the unknown covariance matrix of random factors such as unknown parameters, measurement errors, and initial and external perturbations. A new criterion was introduced for the quality of estimate or filter. It is the level of damping random perturbations which is defined by the maximal value over all covariance matrices of the root-mean-square error normalized by the sum of variances of all random factors. The level of damping random perturbations was shown to be equal to the square of the spectral norm of the matrix relating the error of estimation and the random factors, and the optimal estimate minimizing this criterion was established. In the problem of filtration, it was shown how the filter parameters that are optimal in the level of damping random perturbations are expressed in terms of the linear matrix inequalities.