Presence Of Unknown Disturbances Research Articles

Fixed-time anti-disturbance control for nonlinear turbofan engine with impulsive prescribed performance

To analyze the robust control problem of turbofan engine within a large flight envelope, a fixed-time prescribed performance control scheme is proposed for the nonlinear turbofan engine in presence of unknown disturbances and possibly discontinuous reference signals. Firstly, the equilibrium manifold expansion (EME) modeling method is adopted for the turbofan engine to construct an affine nonlinear model. Then, considering the possibly discontinuous reference signals, a novel fixed-time prescribed performance function (FxTPPF) with impulsive characteristic is presented to successfully deal with the singularity problem caused by its discontinuity situation. In what follows, a fixed-time nonlinear disturbance observer (FxTNDO) is developed to realize fast estimation of disturbances within fixed time. According to the transformed error system and disturbance estimations, a fixed-time anti-disturbance composite controller is further developed. By borrowing Lyapunov stability theory, the boundedness of all error signals in the impulsive closed-loop system is proved. Finally, simulation results indicate that this control scheme can ensure rotor speeds effectively to track the desired commands within a predefined time after the appearance of a discontinuity and the tracking errors are limited to the performance bound all along.

Distributed Neurodynamics-Based Backstepping Optimal Control for Robust Constrained Consensus of Underactuated Underwater Vehicles Fleet.

Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in 3-D space is a challenging but practical problem. To address this problem, this article develops a novel consensus-based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical coordinate transform is introduced to tackle the nonholonomic constraint, and then a distributed optimal motion coordination strategy is developed. As a result, the optimal formation tracking of UUVs fleet can be achieved, and the constraints are fulfilled. To realize the generated optimal commands better and, meanwhile, deal with the underactuation, at the lower-level control loop a neurodynamics-based robust backstepping controller is designed, and in particular, the issue of "explosion of terms" appearing in conventional backstepping-based controllers is avoided and control activities are improved. The stability of the overall UUVs formation system is established to ensure that all the states of the UUVs are uniformly ultimately bounded in the presence of unknown disturbances. Finally, extensive simulation comparisons are made to illustrate the superiority and effectiveness of the derived optimal formation tracking protocol.

Dynamic Modeling and Robust Trajectory Tracking Control of a Hybrid Propulsion-Based Small Underwater Robot

This paper proposes a hybrid propulsion-based small underwater robot for robust trajectory tracking control in a harsh and complex underwater environment. The robot is equipped with a Coanda-effect jet thruster and a pair of propeller-based reconfigurable magnetic-coupling thrusters, allowing it to traverse safely in confined or cluttered spaces as well as cruise efficiently in the open water. To investigate the robot dynamic modeling, we first formulated its simplified mathematical model and estimated the hydrodynamic coefficients by performing the planar motion mechanism using CFD (computational fluid dynamics) simulation. Then, a double-loop trajectory tracking control architecture was designed considering the model uncertainties and environmental disturbances. Based on Lyapunov theory, the outer-loop kinematic control produces the virtual velocity command, while the inner-loop dynamic control adopts the full-state feedback L1-adaptive control to match the command. The asymptotic convergence of the tracking errors and the stability of the whole closed-loop system are guaranteed. Finally, comparative simulations in the presence of unknown disturbances and the variation of model parameters were carried out to verify the robustness of our proposed trajectory tracking control, which is also suitable for the separated son robots.

Safety-critical control for autonomous underwater vehicles with unknown disturbance using function approximator

Safety and stability are two primary factors that affect the task execution performance of autonomous underwater vehicles (AUVs), especially when they operate in a disturbed environment. The unknown disturbances and unmodeled dynamics will severely degrade the performance, resulting in unsafe and unstable behaviors. This paper presents a safety-critical control framework for AUVs in the presence of unknown disturbances using a neural network function approximator. The high-order control barrier functions (CBFs) are utilized to address the safe constraints of relative degree two. A nominal controller is designed in which the unknown lumped disturbances are compensated by a radial basis function neural network, thus ensuring that the tracking errors are uniformly ultimately bounded. Besides, the function approximation and the associated bounded approximation errors are adopted to estimate the unknown lumped disturbance terms in the second-order derivative of CBFs, which alleviates the mismatch of the forward invariance condition for the safety set. The minimally invasive quadratic program (QP) is formulated to make minimal sacrifice on the performance of nominal controller. The proposed method encompasses a straightforward structure for disturbance estimation, alongside a highly efficient explicit estimating rule that promotes rapid convergence. Integrated with computationally efficient QP, the proposed method demonstrates substantial promise for real-time implementation. Comparative numerical results with disturbance observer and input-to-state safety-based robust methods reveal an improved trade-off between safety and control performance.

Disturbance Observer-Based Robust Take-Off Control for a Semi-Submersible Permeable Slender Hybrid Unmanned Aerial Underwater Quadrotor

The development of hybrid unmanned aerial underwater vehicles (HAUVs) compatible with the advantages of the aerial vehicles and the underwater vehicles is of great significance. This paper presents the first study on a new HAUV layout using four rotors to realize the medium crossing motion of a transverse slender body similar to the fuselage of a missile or a submarine, that is, the hybrid aerial underwater quadrotor (HAUQ). Then, a robust control strategy is proposed for the take-off HAUQ on the water in the presence of unknown disturbances and complex model dynamic uncertainties. As a semi-submersible HAUQ rises straight from the water, the inside of the slender fuselage placed horizontally is filled with water. The center of the mass, the moment of inertia, and the arm of the force of the HAUQ will change rapidly in the take-off phase from the water because of the rapid nonuniform change in mass caused by the passive fast drainage. It is difficult to establish an accurate mathematical model of the complex dynamic changes caused by the multi-media dynamics, the fast changing buoyancy, and the added mass crossing the air–water surface. Therefore, an uncertain kinematic and dynamic model is established through the passive, fast, nonuniform change and the complex dynamics are considered as the unknown terms, and the external disturbances of gust and other factors are assumed as the bounded disturbance input. A robust design approach is introduced to deal with the fast time-varying mass disturbance based on the input-to-state stability (ISS) theorem. The complex dynamics are estimated using the basis function and the unknown weight parameters, and the adaptive laws are adopted for the on-line estimation of the unknown weight parameters. Considering the residual disturbance of the uncertain nonlinear system as a total disturbance term, a disturbance observer is introduced for disturbance observation. The numerical simulation shows the feasibility and robustness of the proposed algorithm.

Synchronization of time-varying fractional-order chaotic systems in the presence of unknown disturbance using a fast disturbance identification

This paper studies the synchronization of fractional-order chaotic systems in the presence of unknown external disturbances. A new disturbance identification method using fractional-order algebraic identification is proposed to handle unknown disturbances. In order to guarantee system stability, the nonlinear state-feedback controller is designed based on the Lyapunov theory. All system parameters and orders, as well as the external disturbances, are considered time-varying in this paper. The simulation results show that the estimation of unknown disturbance is executed more quickly and accurately using the proposed method, which also leads to fast and precise synchronization of the chaotic systems. A circuit implementation is also performed to identify the unknown disturbances in a fractional-order electronic chaotic oscillator system to evaluate the practicability of the proposed approach.

Adaptive Asymmetric Time-Varying Integral Barrier Lyapunov Control-Based Trajectory Tracking of Autonomous Vehicles

This paper investigates the lane following and changing maneuvers of autonomous vehicles in the presence of unknown disturbances, taking into account the dynamic system states and input constraints. The integrated longitudinal-lateral and yaw rate dynamics of the vehicle are simultaneously considered to improve the tracking accuracy and system stability when navigating under critical conditions. Then, an adaptive asymmetric time-varying integral barrier Lyapunov control and dynamic surface control scheme are developed to design the active front steering controller, longitudinal controller, and direct yaw moment control controller, which is capable of constraining the system states and control signals within the predefined boundary. In addition, the radius basis function neural network (RBFNN) is employed to estimate the lumped disturbances caused by the parametric uncertainties, external disturbances, and unmodeled dynamics, and the command filter system is used to avoid the explosion of terms phenomenon. Due to the fast and accurate torque response characteristics of the in-wheel motors, the optimization-based method is then implemented to effectively allocate the driving/braking torque to each in-wheel motor so as to improve vehicle performance. The stability of the closed-loop system is comprehensively demonstrated by means of the Lyapunov theory. Finally, the quantitative and qualitative comparisons in different diving scenarios using the Carim-Simulink joint environment are carried out to illustrate the effectiveness and validation of the proposed method.

Attitude Tracking Control of Uncertain Flexible Spacecraft Systems Subject to External Disturbances*

In this paper, we consider the attitude tracking and disturbance rejection problem of uncertain flexible spacecrafts. Compared with existing works, we consider two types of uncertainties of flexible spacecrafts, that is, the uncertain parameters of the inertial matrix, and the unknown external disturbance with unbounded energy. Inspired by the internal model principle, we design a class of dynamic compensators and some auxiliary dynamic systems to compensate the external disturbance. Then, by introducing a series of coordinate transformations, we convert the attitude tracking and disturbance rejection problem into an adaptive regulation problem of an augmented system. Finally, we design the adaptive control law and show that attitude tracking of uncertain flexible spacecrafts can be achieved in the presence of unknown disturbances. The effectiveness and robustness of the controller are illustrated by some numerical simulations.

Symplectic discrete-time Krasovskii passivity-based control for output consensus*

In this paper, we design a sampled-data distributed output feedback controller to achieve output consensus for linear continuous-time port-Hamiltonian systems in presence of unknown disturbances. The key idea is borrowed from Krasovskii passivity-based output consensus control for continuous-time dynamics. To conceptualise this rationale to sampled control systems, we deal with a discrete-time system arising from a symplectic discretization of a continuous-time linear port-Hamiltonian system, such as the implicit midpoint method. As a preliminary step, we introduce the concept of Krasovskii passivity for discrete-time systems and further show that a discretized linear port-Hamiltonian system is Krasovskii passive in the discrete-time sense. Then, based on the discrete-time version of Krasovskii passivity, we develop a sampled-data output feedback controller to achieve output consensus. The proposed sampled-data controller can be understood as a symplectic discretization of the continuous-time output consensus controller. Finally, we illustrate the effectiveness of the main result by achieving current sharing in a DC power network.

Adaptive fault‐tolerant tracking control for nonlinear systems with unknown control coefficient and input saturation

SummaryIn this article, the tracking control problem is investigated for a class of nonlinear systems in the presence of unknown disturbance, input saturation, actuator fault, and unknown control coefficient. A novel disturbance observer‐based adaptive fault‐tolerant tracking control strategy is proposed with regard to nonlinear systems. Based on the Gaussian error function, the auxiliary dynamic system is designed to offset effects caused by the input saturation. Moreover, the Nussbaum‐type function is employed to avert control singularity and deal with the unknown control coefficient. A theoretical analysis indicates that the boundedness of all signals in the closed‐loop system can be guaranteed. Finally, two examples with one concerning the dynamic point‐the‐bit rotary steerable drilling tool system are given to confirm the validity of the method.

Fault-tolerant complete synchronization of integer and fractional derivative 6D hyper-chaotic systems with unknown time delay

In this study, synchronization of two 6D hyper-chaotic systems in the presence of bounded disturbance and uncertainty with unknown bounds and time delay for integer-order and fractional-order systems is investigated. In the first step of the selected control approach, a proper sliding surface is defined for synchronization of two hyper-chaotic systems, a proper active control is considered, and proper adaptive laws are used to estimate the uncertainty bounds, the unknown disturbance, and unknown time delay of the master and slave systems. The stability of the closed-loop control system is proved using the Lyapunov theorem. Simulation results using MATLAB verify desired performance of the proposed controllers in the presence of unknown disturbance, uncertainty, and time delay.

Newton-series-based observer–predictor control for disturbed input-delayed discrete-time systems

This paper deals with the problem of predicting the future state of discrete-time input-delayed systems in the presence of unknown disturbances that can affect both the state and the output equations of the plant. Since the disturbance is unknown, computing an exact prediction of the future plant states is not possible. To circumvent this problem, we propose using a high-order extended Luenberger-type observer for the plant states, disturbances, and their finite difference variables, combined with a new equation for computing the prediction based on Newton’s series from the calculus of finite differences. Detailed performance analysis is carried out to show that, under certain assumptions, both enhanced prediction and improved attenuation of the unknown disturbances are achieved. Linear matrix inequalities (LMIs) are employed for the observer design to minimize the prediction errors. A stabilization procedure based on an iterative design algorithm is also presented for the case where the plant is affected by time-varying uncertainties. Examples from the literature illustrate the advantages of the scheme.

Predictor-Based Neural Dynamic Surface Control of a Nontriangular System With Unknown Disturbances

For a class of nontriangular nonlinear systems in presence of unknown disturbances, we propose a predictor-based neural dynamic surface control (PNDSC) strategy in this paper. This nontriangular system is transformed via the mean value theorem, and a predictor is then constructed. To avoid an algebraic loop problem, partial state vectors are employed as input signals of neural networks (NNs) for approximating unknown dynamics, and compensation items are designed to compensate for approximation errors from NNs. Different from the traditional NDSC, the PNDSC in this paper utilizes prediction errors to update learning parameters for improving NNs’ learning behaviors with overlarge adaptive gains. On the basis of improved NNs’ approximation behaviors, a predictor-based NNs disturbance observer (PNNDO) is constructed for compensation for external disturbances and approximation errors from NNs. Furthermore, with predictors, a normalization method of weights is developed to reduce the number of online learning parameters. On the basis of the aforementioned result, measurement noises are taken into account in our predictor-based neural control strategy. We employ predictor states, rather than measurement information paralyzed by noises, in design of our control strategy. This reduces high-frequency oscillations in control input. A Lyapunov-based stability analysis shows that all signals are ultimately bounded in the closed-loop system. Finally, the effectiveness of the proposed control strategy is verified by a numerical example and a permanent magnet brushless DC motor system.

Adaptive neural sensor and actuator fault-tolerant control for discrete-time unknown nonlinear systems

This paper proposes a novel methodology for trajectory tracking of unknown discrete-time nonlinear systems under sensor and actuator faults. The proposed approach combines a recurrent neural identifier trained online with well-known characteristics of sliding mode technique. This paper includes a stability analysis based on Lyapunov theory. Finally applicability of the proposed scheme is shown through simulation results for a three-phase induction motor whose mathematical model is considered unknown. Furthermore, the system is operated in the presence of unknown disturbances as well as sensor and actuator faults.

VDC-based admittance control of multi-DOF manipulators considering joint flexibility via hierarchical control framework

Multi-degree-of-freedom (Multi-DOF) manipulators have shown a high potential for enhancing the flexibility and performance of robotic manipulations. However, the presence of unknown disturbance, including uncertain dynamics and external forces/torques, makes the control of a multi-DOF manipulator rather complicated, and the stability of the robotic system is hard to be guaranteed. In this paper, a virtual decomposition control (VDC)-based admittance control approach for multi-DOF manipulators has been proposed considering joint flexibility via hierarchical control framework. The joint flexibility is solved by a separate adaptive controller different from the manipulator’s links. The high-level admittance controller is built upon a low-level VDC-based inner control loop, which can deal with the complicated system dynamics (including the joint friction and joint flexibility) and modeling uncertainty. The external force/torque (F/T) is estimated with a generalized momentum-based interaction force estimation technique; thereby avoiding the cost of wrist F/T sensors. The robotic system’s stability has been guaranteed in both free-space motions and constrained motions using the specific features of VDC (proof of each subsystem’s virtual stability). The advantages and effectiveness of the proposed method in tuning the robot–environment dynamic behavior are demonstrated through experiments.

Fault-tolerant cooperative navigation of networked UAV swarms for forest fire monitoring

Coordination of unmanned aerial vehicle (UAV) swarms has received significant attention due to its wide practical applications including search and rescue, cooperative exploration and target surveillance. Motivated by the flexibility of the UAVs and the recent advancement of graph-based cooperative control strategies, this paper aims to develop a fault-tolerant cooperation framework for networked UAVs with applications to forest fire monitoring. Firstly, a cooperative navigation strategy based on network graph theory is proposed to coordinate all the connected UAVs in a swarm in the presence of unknown disturbances. The stability of the aerial swarm system is guaranteed using the Lyapunov approach. In case of damage to the actuators of some of the UAVs during the mission, a decentralized task reassignment algorithm is then applied, which makes the UAV swarm more robust to uncertainties. Finally, a novel geometry-based collision avoidance approach using onboard sensory information is proposed to avoid potential collisions during the mission. The effectiveness and feasibility of the proposed framework are verified initially by simulations and then using real-world flight tests in outdoor environments.

Adaptive Integral Sliding Mode Based Course Keeping Control of Unmanned Surface Vehicle

This paper investigates the course keeping control problem for an unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration.

Actuator fault tolerant control via active fault diagnosis for a remotely operated vehicle

Unmanned underwater vehicles operate in unstructured and harsh environments, and fault diagnosis together with fault tolerant control play a crucial role to prevent the mission from failing. When a fault occurs in presence of unknown disturbances, such as unknown marine current or hitting an obstacle, actuator fault diagnosis becomes more challenging, because the unexpected forces that arise from disturbances may be mistaken for actuator faults. In this paper, we propose an active fault diagnosis method to discern between an actuator fault and any other disturbance: the former is modelled as a multiplicative input fault, and the latter is an unknown, additive, time varying force. Once the actuator faults have been isolated and identified, their estimations are fed to the control allocation algorithm to perform fault tolerant control allocation. Simulation results are performed with a realistic non-linear model to show the effectiveness of the proposed strategy.

Controlling the PVTOL Aircraft System with an Inverted Pendular Load by means of Nested Saturation Functions and GPI Controller

This paper presents a control scheme that allows height position regulation and stabilization for an unmanned planar vertical takeoff and landing aircraft system with an inverted pendular load. The proposed controller consists of nested saturations and a generalized proportional integral (GPI). The GPI controls the aircraft height and the roll attitude; the latter is used as the fictitious input control. Next, the system is reduced through linear transformations, expressing it as an integrator chain with a nonlinear perturbation. Finally, the nested saturation function-based controller stabilizes the aircraft’s horizontal position and the pendulum’s angle. Obtaining the control approach was a challenging task due to the underactuated nature of the aircraft, particularly ensuring the pendulum’s upright position. The stability analysis was based on the second method of Lyapunov using a simple candidate function. The numerical simulation confirmed the control strategy’s effectiveness and performance. Additionally, the numerical simulation included a comparison against a PD controller, where its corresponding performance indexes were estimated, revealing that our controller had a better response in the presence of unknown disturbances.

Reinforcement learning based model-free optimized trajectory tracking strategy design for an AUV

Considering the fact that it is very difficult to fully model an autonomous underwater vehicle (AUV) in the complex water environment, this paper presents a model-free tracking control strategy for an AUV in the presence of unknown disturbances. We first formulate an optimized control problem by defining a tracking Hamilton–Jacobi–Isaac (HJI) equation. Then, we present a reinforcement learning (RL) algorithm to compute an optimized solution by learning from the HJI equation online. It is noted that during the learning period, no information about the AUV’s dynamics is needed. In order to demonstrate the efficiency of the proposed strategy, numerical simulation is considered, results are validated and discussed.