Unmodeled Dynamics Research Articles

Dynamic characteristics analysis and robust transition control of tail-sitter VTOL UAVs

The strong nonlinear dynamics of tail-sitter vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) have consistently constrained their applications. Particularly, the widely-varying flight speed and attitude during the transition flights pose substantial challenges for the controller design. In order to gain a clear understanding of the nonlinear transition flights, the long- and short- period dynamic characteristics of tail-sitter UAVs are investigated for the first time. The results reveal that the short-period mode exhibits either sluggish response or excessive overshoot in different transition stages, primarily due to the widely-varying damping ratio and natural angular frequency. Even worse, the long-period mode can diverge due to positive poles. To address these issues, a systematic controller is proposed. First, the transition trajectory is optimized and the optimal control inputs are employed in a feedforward manner to trim the nominal forces and moments. On this basis, a discrete-time linear quadratic regulation (LQR) controller with a predefined decay rate is developed to position the desired poles, and a novel angular acceleration estimation method is introduced to compensate for unmodeled dynamics. Simulations under different uncertainties indicate that the proposed control method performs better in both transition trajectory tracking and uncertainty suppression, compared to the PID and incremental nonlinear dynamic inversion (INDI) controllers.

Adaptive fuzzy finite‐time prescribed performance control for uncertain nonlinear systems with actuator saturation and unmodeled dynamics

AbstractIn this article, the tracking control problem is addressed for uncertain nonlinear systems with actuator saturation and unmeasurable unmodeled dynamics. Being different from the existing performance function control results, to constraint the output tracking error within a predefined boundary in finite time, an improved performance function, that is, finite‐time performance function, is introduced. The design difficulties of asymmetric input saturation and unmodeled dynamics are solved simultaneously for the first time by applying a smooth non‐affine function and a measurable dynamic signal. Furthermore, an adaptive fuzzy control scheme is established via command filtering, which not only guarantees the semi‐global boundedness of all the controlled system signals but also makes the output tracking error that can be restrained by the described performance bound within a finite‐time interval. At last, an effective example is supplied to validate the availability of the presented theoretical finding.

A New Event-Triggered Adaptive Fixed-Time Control Design for Uncertain Nonlinear Systems.

This article investigates the problem of dynamic memory event-triggered (DMET) fixed-time tracking control within time-varying asymmetric constraints for nonaffine nonstrict-feedback uncertain nonlinear systems with unmodeled dynamics and unknown disturbances. The existing dynamic event-triggered control methods cannot handle the nonlinear systems with unmodeled dynamics and nonaffine inputs, which greatly limits the applicability of the strategy. To this end, a novel DMET adaptive fuzzy fixed-time control protocol is constructed based on the idea of command filtered backstepping, in which a new dynamic signal function is established to deal with the unmodeled dynamics and an improved DMET mechanism (DMETM) is designed to solve the problem of nonaffine inputs. It is proved that the newly DMET control strategy ensures the tracking error converges to an arbitrarily small compact set in a fixed time and all the signals of the closed-loop systems are bounded. The effectiveness of the proposed approach is demonstrated by two simulation examples.

Finite-time adaptive prescribed performance DSC for pure feedback nonlinear systems with input quantization and unmodeled dynamics

<abstract><p>This paper presents a new prescribed performance-based finite-time adaptive tracking control scheme for a class of pure-feedback nonlinear systems with input quantization and dynamical uncertainties. To process the input signal, a new quantizer combining the advantages of a hysteresis quantizer and uniform quantizer has been used. Radial basis function neural networks have been utilized to approximate unknown nonlinear smooth functions. An auxiliary system has been employed to estimate unmodeled dynamics by producing a dynamic signal. By introducing a hyperbolic tangent function and performance function, the tracking error was made to fall within the prescribed time-varying constraints. Using modified dynamic surface control (DSC) technology and a finite-time control method, a novel finite-time controller has been designed, and the singularity problem of differentiating each virtual control scheme in the existing finite-time control scheme has been removed. Theoretical analysis shows that all signals in the closed-loop system are semi-globally practically finite-time stable, and that the tracking error converges to a prescribed time-varying region. Simulation results for two numerical examples have been provided to illustrate the validity of the proposed control method.</p></abstract>

Adaptive Integral Sliding-Mode Control for a class of Nonlinear Spacecraft Proximity Systems under Multi-source Disturbances and Unmodeled Dynamics

Adaptive Integral Sliding-Mode Control for a class of Nonlinear Spacecraft Proximity Systems under Multi-source Disturbances and Unmodeled Dynamics

Prescribed-time adaptive stabilization of high-order stochastic nonlinear systems with unmodeled dynamics and time-varying powers

<p>In this paper, the control problem of prescribed-time adaptive neural stabilization for a class of non-strict feedback stochastic high-order nonlinear systems with dynamic uncertainty and unknown time-varying powers is discussed. The parameter separation technique, dynamic surface control technique, and dynamic signals were used to eradicate the influences of unknown time-varying powers together with state and input unmodeled dynamics, and to mitigate the computational intricacy of the backstepping. In a non-strict feedback framework, the radial basis function neural networks (RBFNNs) and Young's inequality were deployed to reconstruct the continuous unknown nonlinear functions. Finally, by establishing a new criterion of stochastic prescribed-time stability and introducing a proper bounded control gain function, an adaptive neural prescribed-time state-feedback controller was designed, ensuring that all signals of the closed-loop system were semi-global practical prescribed-time stable in probability. A numerical example and a practical example successfully validated the productivity and superiority of the control scheme.</p>

Finite-Time Adaptive Neural Prescribed Performance Control for High-Order Nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input Quantization

Finite-Time Adaptive Neural Prescribed Performance Control for High-Order Nonlinearly Parameterized Switched Systems With Unmodeled Dynamics and Input Quantization

Vibration Research on Centrifugal Loop Dryer Machines Used in Plastic Recycling Processes

This study investigates the vibrations of centrifugal loop dryer machines used in plastic recycling processes. These machines are characterized by large uncertainties, high vibration, and unmodeled dynamics, making the design and maintenance of real-time state estimators for their operational conditions difficult. The present study includes an analysis of the centrifugal loop dryer machines’ vibration characteristics and their influence on operation results based on vibration analysis, frequency response analysis, and expert advice. Two identical loop dryers installed and operated in parallel in a single recycling line were investigated. Measurements were performed using a two-sample measurement design and based on a one-sample statistical method for estimating uncertainty in repeated measurements of data processing. Additionally, a problem connected with incorrect machine operation during high vibration, resulting in insufficient drying of loaded material, was investigated. This was defined as a situation in which some melted plastic is still too wet after mechanical drying, caused by the incorrect installation of damper elements of the holding elements. Finaly, it is recommended that a correction of the machine installation and a control measurement are carried out to determine whether the vibration in the base of the machine still exists. A simplified theoretical vibration analysis of the rotating machine was also carried out in the present paper.

Autonomous Manipulation of an Underwater Vehicle-Manipulator System by a Composite Control Scheme With Disturbance Estimation

This article addresses an autonomous manipulation problem for an underwater vehicle-manipulator system (UVMS) operating in a free-floating way while subjecting to unknown continuous disturbance. More specifically, a composite control scheme composed of disturbance observer (DOB), predictor model network (PM-Net), and nonlinear model predictive control (NMPC), is devised to improve the control performance of UVMS (i.e., unicycle-like UVMS actuated only in the surge, heave, and yaw for vehicle body) in the case of disturbance, model mismatch, and input saturation. A RBF-DOB is formulated by combining a DOB and a Radial Basis Function (RBF) neural network to estimate disturbance at the current step. Then, the PM-Network, composed of a disturbance predictor network and state predictor network, is developed based on long short-term memory (LSTM) network that predicts UVMS state sequences considering model mismatch and disturbance. The NMPC is deployed as a feedback control law to endow the input saturation of the UVMS and produce optimal control action. Compared with conventional DOB control methods using feed-forward compensation of disturbance, the primary merit of the proposed approach is that the disturbance estimated by RBF-DOB is utilized in the PM-Net to predict future UVMS state sequences, which are exploited on the NMPC’s receding optimization. RGB0,0,0Finally, realistic simulation and relevant experiment are conducted to demonstrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation behind this article is the autonomous manipulation of an underwater vehicle-manipulator system subjected to unknown disturbance. However, it is not always feasible or straightforward to obtain the external disturbance and unmodeled dynamics for designing robust controllers. On the one hand, how to manipulate the disturbance into the designed controller to generate optimal control action rather than by using feed-forward compensation. On the other hand, the control input saturation often occurs in the UVMS control, especially under the disturbance rejection conditions, where it should be considered in the controller design. Currently, the predominant methods for UVMS control lack a control scheme that provides a complete and credible control strategy that takes the aforementioned issues into consideration. Motivated by the above analysis, this study provides a composite control scheme to deal with the dynamic uncertainties, unknown disturbance, and input saturation. RGB0,0,0The results of realistic simulation and relevant experiments demonstrate the effectiveness of the proposed method. Hopefully, our control method can provide valuable theoretical and technical guidance to practicing marine engineers for controller design.

Neural Network-Based Adaptive Height Tracking Control of Active Air Suspension System with Magnetorheological Fluid Damper Subject to Uncertain Mass and Input Delay.

In this paper, we present a novel robust adaptive neural network-based control framework to address the ride height tracking control problem of active air suspension systems with magnetorheological fluid damper (MRD-AAS) subject to uncertain mass and time-varying input delay. First, a radial basis function neural network (RBFNN) approximator is designed to compensate for unmodeled dynamics of the MRD. Then, a projector-based estimator is developed to estimate uncertain parameter variation (sprung mass). Additionally, to deal with the effect of input delay, a time-delay compensator is integrated in the adaptive control law to enhance the transient response of MRD-AAS system. By introducing a Lyapunov-Krasovskii (LK) functional, both ride height tracking and estimator errors can robustly converge towards the neighborhood of the desired values, achieving uniform ultimate boundness. Finally, comparative simulation results based on a dynamic co-simulator built in AMESim 2021.2 and Matlab/Simulink 2019(b) are given to illustrate the validity of the proposed control framework, showing its effectiveness to operate ride height regulation with MRD-AAS systems accurately and reliably under random road excitations.

Tuning-free filtering for stochastic systems with unmodeled measurement dynamics

This paper applies variational Bayesian to the stochastic system filtering problem for arbitrary measurement disturbances. Considering the system’s robustness when confronted with disturbances, we model its dynamics with a random walk model. Afterward, by assuming an inverse-Gamma distribution, the newly introduced covariance is estimated under a variational Bayesian (VB) framework. The proposed filter improves robustness against additive disturbance and degrades automatically into a Kalman filter when without disturbance. Moreover, this filter avoids the labor-intensive process of tuning parameters. A numerical simulation and an on-site vehicle navigation experiment are given to illustrate the effectiveness of the proposed filter.

Disturbance observer-based robust optimal control of rate-dependent hysteretic systems: Application to a piezoelectric nano-manipulator

This article investigates the robust and optimal control problem of hysteretic systems with both parameter variations and unmodeled dynamics. By means of a hysteresis compensator based on a modified Prandtl–Ishlinskii model and a feedback controller, a novel robust optimal control architecture is proposed to address the asymmetric and rate-dependent hysteresis nonlinearities and various disturbances, where the compensation errors, parameter variations, and unmodeled dynamics are treated as a bounded disturbance. The proposed controller is composed of two components: a high order sliding mode observer and an extended linear quadratic controller. The high order sliding mode observer is introduced to guarantee that the disturbance observation error converges to the origin in a finite time such that the extended linear quadratic controller enables the predefined optimal performance to be achieved. Moreover, the exponential stability of the closed-loop system has been proven, and some sufficient conditions are given. Finally, the proposed control algorithm is applied to a piezoelectric nano-manipulator system, both the good hysteresis modeling accuracy and excellent tracking performance are demonstrated in the real-time experiments.

Current decoupling control of permanent magnet synchronous motor based on improved nonlinear extended state observer

Precise current decoupling is required to achieve good control performance in the vector control of permanent magnet synchronous motor (PMSM), but traditional decoupling methods are unable to solve the problem of poor current decoupling effectiveness when the system inductance parameters are mismatched. Therefore, a decoupling control method of PMSM based on improved nonlinear extended state observer (INESO) is proposed in this paper. Firstly, a smooth, differentiable nonlinear function is used to replace the traditional fal function, which overcomes the problem of discontinuous gain changes. Secondly, a time-varying gain is designed to address the problem of reduced observer performance due to differential peak values in traditional NESO. Finally, the stability of INESO and the current loop closed-loop system is proven, followed by simulation and experimental verification. The simulation and experimental results show the proposed method effectively observes and compensates for system uncertainties, including parameter perturbations, external disturbances, and unmodeled dynamics. This method achieves complete decoupling of d and q-axes currents, while suppressing interference and enhancing the control performance of PMSM. The proposed INESO decoupling method reduces the d-axis current fluctuation about 96% and 90% when the load changes abruptly under motor inductance parameter mismatch, and can reduce q-axis current fluctuation about 88% and 78% when the d-axis current steps, respectively, compared with the conventional Current Feedback Decoupling Control (CFDC) and CDDC decoupling methods.

Finite-Time Tracking Control of a Flexible Link Manipulator Based on an Extended State Observer

In this paper, the finite-time trajectory tracking control problem of a flexible link manipulator (FLM) system with unknown parameters is investigated in joint space. An adaptive nonsingular terminal sliding mode (ANTSM) controller based on an extended state observer (ESO) is proposed to ensure that the tracking error converges to a small neighborhood of zero. Firstly, an adaptive ESO is introduced, which is used to estimate unknown system parameters, including the unknown rigid-flexible coupling coefficient and the unknown control gain. Then, an ESO based on the parameters estimated above is proposed to estimate the unmodeled dynamics and external disturbances of the system in real-time. Finally, an ANTSM controller based on ESO is studied to achieve finite-time trajectory tracking control and vibration suppression without any prior knowledge of system uncertainties and external disturbances. The finite-time convergence ability of the closed-loop system is proved by Lyapunov theory. Experimental results on the Quanser Flexible Link System verify the effectiveness of the proposed controller.

Integrated control of path tracking and handling stability for autonomous ground vehicles with four-wheel steering

Accurate modelling of an autonomous ground vehicle (AGV) is challenging due to strong nonlinearity, model perturbations and external disturbances. This necessitates the design of path tracking controllers with robustness. In this paper, an integrated control strategy for path tracking and handling stability of AGVs is proposed, leveraging a four-wheel steering (4WS) system. To address the path tracking problem, a fuzzy logic-based steering strategy is developed, which enables parameter self-adjustment based on displacement error. Additionally, a reference model is employed to transform the path tracking problem into a reference signal tracking problem, ensuring the handling stability of vehicles. A sliding mode controller is designed to track the reference signal, utilizing an extended state observer to estimate and compensate for unmodeled dynamics and unknown external disturbances in real-time. Simulation results demonstrate the effectiveness of the proposed strategy in accurately tracking the desired path while maintaining the lateral stability of the vehicle. Furthermore, the strategy exhibits strong robustness against uncertainties and disturbances.

Uncertainty and velocity observer-based predefined-time nonsingular terminal sliding mode control of the underwater robot manipulators

This paper introduces an observer-based predefined-time nonsingular terminal sliding mode control to the position tracking control of the n-degree-of-freedoms underwater robot manipulators subject to the uncertainty compensation and sensor reduction. The proposed sliding surface has only the sliding phase, while the reaching phase is eliminated using a continuous augmented function. Besides, the sliding phase is predefined-time, where it is independent of the initial conditions and control parameters. As a result, it has led to the development of a predefined-time control scheme, in which not only its convergence time is fast, but also (due to the elimination of the reaching phase) the effect of uncertainties in this phase is eliminated. Naturally, there is a wide range of uncertainties such as structured-unstructured uncertainties and/or un-modeled dynamics, ocean current, waves, etc., in the underwater robot manipulators and underwater conditions. Therefore, the proposed predefined-time nonsingular terminal sliding mode control is combined with a finite-time observer to estimate the underwater robot manipulator's joint velocity and eliminate the destructive effect of existing uncertainties. Under this combination, the stability analysis could guarantee predefined-time global asymptotic stability of the system in the presence of existing uncertainties. Simulation results are conducted on a 2-link underwater robot manipulator to validate the proposal.

Command filter and high gain observer based adaptive output feedback control for stochastic nonlinear systems with prescribed performance and input quantization

SummaryIn this paper, an adaptive output feedback dynamic surface control (DSC) strategy is proposed for strict‐feedback stochastic nonlinear systems with input quantization, prescribed performance and dynamic uncertainties. A new quantizer is used to process the input signal, which can avoid the chattering of the quantization signal and keep the upper bound of the quantization error constant. Radial basis functions are used to approximate unknown smooth functions, unmodeled dynamics are processed by dynamic signals, and unmeasurable states are estimated by high gain observer. Hyperbolic tangent functions are employed to handle prescribed performance. The second order command filter is used to replace the first order filter used in general DSC, and the compensation term is added in each step of DSC. By the Lyapunov stability analysis, all signals in the controlled system are semi‐globally uniformly ultimately bounded (SGUUB) in probability. Two examples further prove that the control scheme designed in this paper is reasonable and effective.

Adaptive tracking control of a unicycle subject to uncertainties and perturbations

An adaptive trajectory tracking control scheme is proposed for the command of a unicycle robot which is subject to dynamic uncertainties (inertia, mass, viscous parameter, etc), unmodeled dynamics, unknown disturbances, slipping effects, and loss of effectiveness (LOE). The latter are adaptively estimated and compensated in control laws. Moreover, projection algorithms are added to bound estimators, preventing them from drifting. The control objective is developed in two steps. First, a kinematic control law is designed to compute the necessary velocities (linear and angular velocities) that allow the robot to reach the desired trajectory. Afterward, sliding mode based adaptive control torques are developed in order to track velocities computed in the first step. The proposed control strategy guarantees the stability of the closed-loop system by ensuring the asymptotic convergence to both desired trajectory and necessary velocities. Finally, simulation results illustrate the applicability and effectiveness of the proposed control scheme.

Observer-Based Adaptive NN Security Control for Switched Nonlinear Systems Against DoS Attacks: An ADT Approach.

In this article, a novel switched observer-based neural network (NN) adaptive control algorithm is established, which addresses the security control problem of switched nonlinear systems (SNSs) under denial-of-service (DoS) attacks. The considered SNSs are described in lower triangular form with external disturbances and unmodeled dynamics. Note that when an attack is launched in the sensor-controller channel, the controller will not receive any message, which makes the standard backstepping controller not workable. To tackle the challenge, a set of NN adaptive observers are designed under two different situations, which can switch adaptively depending on the DoS attack on/off. Further, an NN adaptive controller is constructed and the dynamic surface control method is borrowed to surmount the complexity explosion phenomenon. To eliminate double damage from DoS attacks and switches, a set of switching laws with average dwell time are designed via the multiple Lyapunov function method, which in combination with the proposed controllers, guarantees that all the signals in the closed-loop system are bounded. Finally, an illustrative example is offered to verify the availability of the proposed control algorithm.

Integrated L1 adaptive cartesian PD control for robotic loco-manipulation

This paper presents an integrated framework of L1 adaptive control and Cartesian PD control that overcomes the inherent limitations of the latter controller in tracking end effector position for loco-manipulation tasks. In a simplified system, this work represented the loco-manipulation problem with the Cartesian space dynamics of the end effector object, capturing the dominating parts of the dynamics in linear terms. The unmodeled nonlinear dynamics, along with other model uncertainties and external disturbances, are accounted for by the L1 adaptive controller, taking the Cartesian PD input as a component of the control law to resemble its behavior for the desired system dynamics modeled in the state predictor of the L1 adaptive controller. Through simulation results of various combinations of model perturbations and external disturbances, we show that this integrated framework can achieve similar tracking performances of the end effector and object without the need to re-tune controller parameters. This work demonstrated the mitigation of effects from system uncertainties with model perturbation of up to 100% higher than expected mass and up to 15N of sinusoidal external disturbances, with higher disturbance rejection possible through designing for more conservative estimates of unknown model parameters. Combined with the low computational cost of this framework, the improvement in state command tracking gives rise to potential implementation on robotic systems controlled with linear optimization formulations.