Uncertain External Disturbances Research Articles

Robust adaptive fault‐tolerant control using RBF‐based neural network for a rigid‐flexible robotic system with unknown control direction

AbstractIn this article, a radial basis function (RBF)‐based robust adaptive fault‐tolerant control (FTC) is developed for a class of rigid‐flexible robotic systems under actuator failures, unknown control direction, and uncertain external disturbances. The dynamics of the rigid‐flexible robotic over a vertical plane is governed by the hybrid ordinary differential equations–partial differential equations. The uncertain external disturbance is estimated via an RBF neural network. Meanwhile, the robust adaptive FTC law is utilized to follow the given joint angular and attenuate the vibration of the flexible structure in the case of actuator failures and unknown distributed disturbances. To handle the unknown control direction, the Nussbaum function is employed to robust FTC laws. By virtue of the Lyapunov direct approach, the trajectory tracking and vibration elimination for the controlled rigid‐flexible robotic system are proved and uniformly bounded in face of bounded external disturbances and unknown control directions. The efficacy of the developed control strategy is also illustrated via four comparison examples.

Dynamic positioning of ship using backstepping controller with nonlinear disturbance observer

This paper studies the adaptive dynamic positioning control problem of the full-actuated ship with uncertain time-varying environmental disturbances. Considering the disturbances with unknown boundaries, the inversion control technique is combined with the disturbance observation compensation method to design the robust adaptive backstepping control law of the ship dynamic positioning system. The Lyapunov function is adopted to prove the errors of the ship’s position and heading angle are uniformly ultimately bounded using the designed control law. The nonlinear disturbance observer can adaptively estimate and compensate for uncertain external disturbances caused by winds, waves and currents. Afterward, the verification of the proposed controller through a typical CyberShip Ⅱ model subject to environmental disturbances is carried out using a hardware-in-the-loop simulation where a thrust distribution model is established. The simulation results show the effectiveness of the proposed control law.

Improved fault detection and control allocation for hypersonic re‐entry vehicle subject to intermittent fault and disturbance

AbstractThis study investigates the nonlinear fault detection and control allocation of a hypersonic re‐entry vehicle with intermittent faults and disturbances. The uncertain external disturbances in the inner loop system are processed on the basis of the studied intermittent fault mode. A set of residuals is generated by sliding time windows, and two hypothesis tests based on the residual mean are proposed to detect the appearance and disappearance of faults. An augmented observer is used to estimate the faults subject to the ‐gain disturbance rejection constraint. A nonlinear backstepping fault‐tolerant control is designed to generate the desired control torque and make the output attitude track the given signal. An optimization control allocation algorithm distributes the desired torque to aerodynamic surfaces and reaction control system jets. The stability of the schemes is analyzed by Lyapunov and linear matrix inequality theories. Numerical simulation and rapid prototyping results are presented to demonstrate the validity of the proposed methods.

Global fuzzy adaptive asymptotic tracking control for nonlinear reaction-diffusion equations with time-varying coefficients

This study focuses on the research of the globally asymptotic tracking problem of unknown nonlinear reaction-diffusion equations with time-varying coefficients and uncertain external disturbance. Firstly, fuzzy logic systems and adaptive bounding technique are used to deal with nonlinear reaction-diffusion equations with time-varying coefficients and uncertain external disturbance. Secondly, a novel global state feedback adaptive fuzzy control algorithm is proposed to make the nonlinear reaction-diffusion equations track the target systems globally and asymptotically. In addition, the globally asymptotic tracking condition can be obtained, which overcomes the semi-global results in the existing literatures. Finally, three simulation examples are given to illustrate the feasibility and effectiveness of the proposed control protocols.

Smooth second-order sliding mode controller for multivariable mechanical systems

This paper proposes a smooth second-order sliding mode controller for a class of multi-input multi-output mechanical systems with uncertain parameters and external disturbances. Since the control law is smooth, the chattering effect that can occur with non-smooth controllers is reduced. Lyapunov-based theorems are used to prove global and finite-time convergence of the sliding mode controller. Numerical simulations are presented to illustrate the performance of the proposed controller by applying it first to a variable-length pendulum and then to a two-link robotic manipulator. For the robotic manipulator, a detailed comparison is given of the finite-time convergence and chattering properties of the proposed controller, a super-twisting controller and a super-twisting like controller.Article HighlightsA new smooth second-order sliding mode controller is proposed for multivariablesystems with uncertain parameters and external disturbances.Global finite-time convergence of the controller is proved using Lyapunov theory.Numerical simulations are used to show the performance of the controller on thevariable-length pendulum and two-link robotic manipulator.

Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

This paper presents a novel flocking algorithm based on a memory-enhanced disturbance observer. To compensate for external disturbances, a filtered regressor for the double integrator model subject to external disturbances is designed to extract the disturbance information. With the filtered regressor method, the algorithm has the advantage of eliminating the need for acceleration information, thus reducing the sensor requirements in applications. Using the information obtained from the filtered regressor, a batch of stored data is used to design an adaptive disturbance observer, ensuring that the estimated values of the parameters of the disturbance system equation and the initial value converge to their actual values. The result is that the flocking algorithm can compensate for external disturbances and drive agents to achieve the desired collective behavior, including virtual leader tracking, inter-distance keeping, and collision avoidance. Numerical simulations verify the effectiveness of the algorithm proposed in the present study.

Exponentially asymptotic adaptive tracking control of nonstrict feedback nonlinear systems

This paper considers the asymptotic adaptive tracking control issues for the nonstrict feedback nonlinear systems. By adding the negative exponential function to the Lyapunov function, the exponentially asymptotic convergence is realised for the first time. In addition, the prior knowledge of the lower and upper bounds of unknown nonlinear functions have been removed in this article. The unknown nonlinear functions and uncertain external disturbance are solved by using fuzzy logical systems (FLS) and the bound-estimation method. It is shown that the tracking error can converge to zero at an exponential rate and the whole signals of the controlled system are bounded. Finally, simulation examples have been presented to prove the effectiveness of the raised scheme.

Observer‐aided resilient hybrid fractional‐order controller for frequency regulation of hybrid power system

This article maidenly presents a resilient hybrid fractional-order controller (hy-FOC) comprised of fractional-order proportional-integral-derivative (FOPID) controller and fractional-order sliding mode controller (FOSMC) for frequency regulation of an islanded hybrid power system (HPS) integrated with a doubly-fed induction generator (DFIG)-driven wind power generator. The slime mould algorithm is applied to obtain near-optimum gains of the hy-FOC via the minimization of an integral error criterion. Moreover, a Luenberger disturbance observer (Dob) is designed to estimate uncertain plant disturbances and incorporate estimated output into the control law to alleviate chattering in the output of sliding mode controller (SMC). The acceptability and superiority of the proposed hy-FOC are established by performing an extensive comparative study with the results of PI-derivative (PID), SMC, FOPID, and FOSMC under diverse operating conditions (model uncertainties and uncertain external disturbances). The simulation results are presented and discussed to authenticate proficiency of the proposed resilient control methodology over its other integral-order and fractional-order counterparts. Governor dead-band and generation rate constraint (thermal unit) as a provenance of physical limitations are also considered in the model to show the proposed controller's effectiveness for handling the practical challenges. Lastly, the sensitivity and robustness of the proposed approach have been affirmed by varying system parameters between ±25% of nominal settings.

A novel constraint tracking control with sliding mode control for industrial robots

As industrial robots are characterized by flexibility, load variation, and unknown interference, it is necessary to develop a control strategy with strong robustness and adaptability, fast convergence rate, and simple structure. Sliding mode control is a special method widely used to handle nonlinear robot control. However, the existing control law for sliding mode control has limitations in the chattering and convergence rate. The sliding mode manifold and reaching law are firstly discussed in this article. In the meanwhile, a proposed control law for sliding mode control combining linear sliding mode manifold and double-power reaching law is developed, which is based on the robot dynamic equation derived by the Udwadia–Kalaba theory. Furthermore, a compared control law for sliding mode control combining linear sliding mode manifold with exponential reaching law is presented to test the proposed control law for sliding mode control. The comparison indicates that the proposed law effectively improves the performance in convergence rate and the chattering of constraint tracking control. Finally, the two control laws for sliding mode control are applied to the Selective Compliance Articulated Robot Arm robot system with modeling error and uncertain external disturbance to demonstrate the merit and validation of the proposed scheme.

Event‐triggered prescribed performance adaptive fuzzy asymptotic tracking of nonstrict‐feedback nonlinear systems

AbstractThis paper considers the performance‐guaranteed event‐triggered adaptive asymptotic tracking control problem for nonstrict feedback nonlinear systems with uncertain external disturbances. More precisely, the upper bounds of control gain function in stability analysis are eliminated by establishing Lyapunov function with lower bounds of control coefficients. Compared with the previous guaranteed performance results where the tracking errors converge to the neighborhood of the origin, the control scheme proposed in this paper ensures that the tracking error asymptotically converges to zero by constructing the smooth functions and boundary estimation method. So that the prescribed performance of the system is always achieved. In addition, the event‐triggered strategy is developed to reduce the waste of communication resources. The proposed control law can ensures that all the signals of system are bounded. Meanwhile, the Zeno behavior can be effectively avoid. Finally, two examples are provided to prove the effectiveness of the proposed scheme.

Hybrid Adaptive Control Strategy for Continuum Surgical Robot Under External Load

Natural orifice transluminal endoscopic surgery (NOTES) has received significant attentions due to its minimal incision trauma compared with traditional multi-port robot assisted surgery. Continuum robot can be used in NOTES due to its high flexibility which can adapt to circuitous paths. However, the modeling of continuum robot is complex due to its nonlinear dynamics, especially with uncertain external disturbance. In this letter a hybrid adaptive control framework is proposed, combining offline trained robot inverse kinematics with neural network and online adaptive adjustment of PID controller parameters with another neural network, providing additional driving amount to the robot system to compensate the positioning error caused by external disturbance. Path tracking and point tracking experiments with external load are conducted and the results validate that the proposed hybrid adaptive control framework can compensate uncertain factors such as friction, driving tendon relaxation and external load during robot movement, and obtain relatively high positioning accuracy (maximum absolute error less than 2 mm) with quickresponse.

Dynamic optimal operational control for complex systems with uncertain external loop disturbances

AbstractIn this article, a two‐layer control strategy is proposed for industrial processes where loop controllers are difficult to change. The upper layer performs the determination of the target value and the dynamic adjustment of the set points, while the lower layer realizes the tracking control of loop outputs. To optimize the performance index, observers are designed to estimate those uncertain external disturbances and a new set points adjuster is designed to minimize the influence of disturbances on the overall performance. It is noted that neither the loop structure nor the loop parameters are changed in the operational control. Finally, a simulation on coke oven gas system is given to demonstrate the effectiveness of this method.

Research on predictive sliding mode control strategy for horizontal vibration of ultra-high-speed elevator car system based on adaptive fuzzy

In order to effectively suppress horizontal vibration of the ultra-high-speed elevator car system. Firstly, considering the nonlinearity of guide shoe, parameter uncertainties, and uncertain external disturbances of the elevator car system, a more practical active control model for horizontal vibration of the 4-DOF ultra-high-speed elevator car system is constructed and the rationality of the established model is verified by real elevator experiment. Secondly, a predictive sliding mode controller based on adaptive fuzzy (PSMC-AF) is proposed to reduce the horizontal vibration of the car system, the predictive sliding mode control law is achieved by optimizing the predictive sliding mode performance index. Simultaneously, in order to decrease the influence of uncertainty of the car system, a fuzzy logic system (FLS) is designed to approximate the compound uncertain disturbance term (CUDT) on-line. Furthermore, the continuous smooth hyperbolic tangent function (HTF) is introduced into the sliding mode switching term to compensate the fuzzy approximation error. The adaptive laws are designed to estimate the error gain and slope parameter, so as to increase the robustness of the system. Finally, numerical simulations are conducted on some representative guide rail excitations and the results are compared to the existing solution and passive system. The analysis has confirmed the effectiveness and robustness of the proposed control method.

Sliding Mode Control Based on High-Order Linear Extended State Observer for Near Space Vehicle

Aiming at the uncertainty and external disturbance sensitivity of the near space vehicles (NSV), a novel sliding mode controller based on the high-order linear extended state observer (LESO) is designed in this paper. In the proposed sliding mode controller, the double power reaching law is adopted to enhance the state convergence rate, and the high-order LESO is designed to improve the antidisturbance ability. Moreover, the appropriate observer bandwidth and extended order are selected to further reduce or even eliminate the disturbance by analyzing their influences on the observer performance. Finally, the simulation demonstrations are given for the NSV control system with uncertain parameters and external disturbances. The theoretical analyses and simulation results consistently indicate that the proposed high-order LESO with carefully selected extended order and observer bandwidth has better performance than the traditional ones for the nonlinear NSV system with parametric uncertainty and external disturbance.

Satellite disturbance detection and rejection with control moment gyros

Uncertain external disturbances either secular or oscillating make the satellite attitude be deviated or oscillating from the mission attitude. These disturbances should be detected in short and rejected with proper counter torques of actuators. Nonlinear second order sliding mode observer is one of the good algorithms that can be used for the detection of disturbances’ magnitude and frequencies. With a control law of the integration and dipole rejection filters based on the identified disturbance information, the actuator torques can compensate the disturbances. The spacecraft attitude is then stabilized at the mission attitude within a tolerable error. In this paper, the performances of detection and rejection algorithms are checked respectively in computer simulations. The applicability is proved on a satellite ground simulator recently developed in Korea Aerospace University. The results show that the algorithms work properly with small error, which is due to the low grade of sensors and actuators.

Finite-Time Synchronization and Identification between Two Nonlinear Coupled Multiweighted Markovian Switching Delayed Networks with Uncertain Models and External Disturbances

In this paper, two different nonlinear coupled Markovian switching delayed network models with multiple weights are established, and the effects of parameter and model uncertainties and external disturbances are fully considered. Firstly, based on the finite-time theory, several sufficient conditions for the finite-time synchronization of Markovian switching networks are obtained. Secondly, under the feedback controller with adaptive laws, when the synchronization of the networks is achieved in a finite time, the uncertain parameters of the networks can also be identified as true values. Finally, a representative numerical simulation is given to further illustrate the validity and practicability of the theoretical analysis and proof of this paper.

Adaptive Fuzzy Consensus Tracking Control for Uncertain Fractional-Order Multiagent Systems With Event-Triggered Input

This article focuses on the problem of adaptive fuzzy consensus tracking control with event-triggered input for a class of fractional-order multiagent systems, where systems are considered to be with unmeasurable states, mismatched uncertain external disturbances, and unknown nonlinearities. An event-triggered mechanism with a decreasing threshold function is proposed in this article, which mitigates communication burden and avoids large control impulse. To achieve better tracking performance in distributed control strategy, a compensating fractional-order adaptation law associated with consensus tracking error is introduced when agents cannot obtain the information of reference trajectory directly. Under the fractional-order stability theory, a novel distributed adaptive event-triggered controller with fuzzy observers is constructed, which ensures the boundedness of tracking errors. A simulation example demonstrates the correctness of the presented scheme.

Strongly perturbed sliding mode adaptive control of vehicle active suspension system considering actuator nonlinearity

Aiming at the problem of improving driving comfort under the condition of ensuring driving safety and limited suspension travel, this paper proposes a sliding mode adaptive controller suitable for strong disturbance action. Considering the nonlinear characteristics of the hydraulic actuator, a one-half vehicle suspension model with uncertain parameters and external disturbances is established. In the sliding mode design part of the controller, the sliding mode equivalent control items of the vertical and pitch subsystems are derived, and the optimised constant velocity approach law is adopted. The sliding mode switching control item is given to ensure that the controller can effectively adjust the vehicle body posture when the system is subjected to strong external disturbance. Finally, by selecting the adaptive law, the controller can adapt to the uncertainty of the vehicle body parameters. The simulation results for the specific example considered in this work show that the control strategy can greatly improve the ride comfort under the premise of ensuring the safety of the vehicle and the stability of the steering. Compared with the backstepping adaptive control, the control strategy of this paper shows superiority when the system is subjected to strong disturbance.

Novel finite-time adaptive neural control of flexible spacecraft with actuator constraints and prescribed attitude tracking performance

A novel finite-time neural control strategy is proposed for flexible spacecraft with guaranteed attitude tracking performance subject to parametric uncertainty, uncertain external disturbance, unmeasured elastic vibration and actuator saturation. First, a new finite-time performance function is developed. Then, unlike some existing prescribed performance control methodologies, a novel finite-time controller is derived to guarantee that the attitude tracking errors can converge to a preselected range of the equilibrium within predetermined time independent of the initial conditions. The derived controller is then extended by a neural network compensator to reject the disturbance and overcome the unknown actuator saturation. Specially, the computation burden is reduced by updating the norm of ideal weight vectors rather than their elements with the aid of minimal-learning-parameter technique. Finally, apart from showing the stability of the closed-loop system, numerical simulations demonstrate the effectiveness, robustness and superiority of the proposed approach.

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