Presence Of Model Uncertainties Research Articles

Combined Fault Diagnosis Scheme Based on Recurrent Neural Network and H− Observer for Satellite Attitude Control System

This study proposes a combined fault diagnosis scheme based on a recurrent neural network (RNN) and an H− observer for satellite attitude control systems (ACSs) in the presence of model uncertainties, external disturbances, and measurement noise. The ACS is decoupled into multiple independent channels, such that the residuals generated by the observers respond to the corresponding faults. A novel multilayer adaptive Gaussian recurrent neural network (MAGRNN) structure is developed as an approximator to estimate the lumped disturbance; a robust term is introduced to improve accuracy. Considering the actuator fault in the finite frequency domain and the fault-sensitive index, a set of H− unknown input observers (UIOs) is designed using the output of the MAGRNN-based approximator as compensation. The existence conditions of the approximator and observer are proposed and proved. The fault diagnosis results for three cases verify that the proposed method can be used for small fault detection and isolation.

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

SummaryNeural network (NN) methods have become increasingly efficient and applicable in control. This article presents a novel nested control strategy based on adaptive radial basis function neural networks (RBFNN) and NN supervised control embedded with integrator back stepping (IBS) for a robust position and attitude trajectory tracking of quadrotor aerial robot in presence of modeling uncertainties, sensing noise, and external bounded disturbance. The control philosophy is derived from the decentralized inverse dynamics, where the adaptive RBFNN is adopted for the outer loop to approximate the unknown external disturbance, and adaptively compensate for their effect. In conjunction with a supervised control that stabilizes the attitude in the inner loop by adaptively compensating the unknown and unmodeled uncertainties, avoiding initial instability of the attitude during NN convergence. Furthermore, noise is attenuated by an adaptive extended Kalman filter. Stability analysis was elaborated using Lyapunov stability theory. Simulation of adaptive RBFNN control philosophy proved robustness and effectiveness compared to proportional integral derivative, IBS, and offline decentralized convolution neural network algorithms as it generates the control law by guarantying a fast convergence of parameters, better external disturbance compensation, and noise attenuation. The proposed scheme of robust tracking was validated.

Bayesian Inference for State-Space Models With Student-t Mixture Distributions.

This article proposes a robust Bayesian inference approach for linear state-space models with nonstationary and heavy-tailed noise for robust state estimation. The predicted distribution is modeled as the hierarchical Student- t distribution, while the likelihood function is modified to the Student- t mixture distribution. By learning the corresponding parameters online, informative components of the Student- t mixture distribution are adapted to approximate the statistics of potential uncertainties. Then, the obstacle caused by the coupling of the updated parameters is eliminated by the variational Bayesian (VB) technique and fixed-point iterations. Discussions are provided to show the reasons for the achieved advantages analytically. Using the Newtonian tracking example and a three degree-of-freedom (DOF) hover system, we show that the proposed inference approach exhibits better performance compared with the existing method in the presence of modeling uncertainties and measurement outliers.

Modeling and motion control of helical microrobots using the dual quaternion framework and adaptive sliding mode strategy

This study investigates the tracking control problem of helical microrobots (HMRs) in complicated blood environments. The integrated relative motion model of HMRs is established by resorting to the dual quaternion method, which can describe the coupling effect between the rotational and translational motions. Subsequently, an original apparent weight compensator (AWC) is designed to alleviate the adverse effects of the HMR sinking and drifting owing to its own weight and buoyancy. An adaptive sliding mode control based on the developed AWC (AWC-ASMC) is constructed to guarantee the fast convergence of the relative motion tracking errors in the presence of model uncertainties and unknown perturbations. The chattering phenomenon of the classical SMC is significantly reduced using the developed control strategy. Furthermore, the stability of the closed-loop system under the constructed control framework is demonstrated by the Lyapunov theory. Finally, numerical simulations are performed to demonstrate the validity and superiority of the developed control scheme.

High precision structured H∞ control of a piezoelectric nanopositioning platform.

The inherent weakly damped resonant modes of the piezoelectric nanopositioning platform and the presence of model uncertainty seriously affect the performance of the system. A structured H∞ design is used in this paper to solve the accuracy and robustness problems respectively using a two-loop control structure. The multiple performance requirements of the system are constituted into an H∞ optimization matrix containing multi-dimensional performance diagonal decoupling outputs, and an inner damping controller d is set according to the damping of the resonant modes; the second-order robust feedback controller is preset in the inner loop to improve the robustness of the system; the tracking controller is connected in series in the outer loop to achieve high accuracy scanning; finally, the structured H∞ controller is designed to meet the multiple performance requirements. To verify the effectiveness of the proposed structured H∞ control, simulation comparison experiments are done with the integral resonant control (IRC) and H∞ controller. The results demonstrate that the designed structured H∞ controller achieves higher tracking accuracy compared to the IRC and H∞ controllers under grating input signals of 5, 10, and 20 Hz. Moreover, it has good robustness under 600g and 1000g loads and high frequency disturbances close to the resonant frequency of the system, meeting multiple performance requirements. Compared with the traditional H∞ control, yet with lower complexity and transparency, which is more suitable for engineering practice applications.

Active observer-based T-S fuzzy control scheme for maximum power point tracking in PV systems

This paper addresses the problem of maximum power point tracking of photovoltaic (PV) systems in the presence of model uncertainty as well as varying load and atmospheric conditions using techniques based on a Takagi–Sugeno fuzzy model. The proposed approach relies on the linear matrix inequality tool, Lambert W function, and the Newton–Raphson method. First, adopting a quadratic Lyapunov function, an active observer-based fuzzy non-parallel distributed compensation (non-PDC) controller is designed for asymptotic tracking of the desired reference input. Next, to subdue the impact of uncertainty on the PV system, the closed-loop nominal system is regarded as a reference model, and then the main control law is developed using an online lumped uncertainty estimator and keeping the nominal control law within the staple controller. This control law does not require that the bounds on uncertainties be known. The reference voltage is determined by a novel maximum power point-seeking algorithm that is organized based on the one-diode model of PV panel, Lambert W function, and Newton–Raphson method. Finally, simulations are performed for three scenarios to point out the merits and effectiveness of the proposed methodology in the presence of system uncertainties, environmental changes, and load variations.

A novel asymmetric barrier Lyapunov function-based fixed-time ship berthing control under multiple state constraints

The construction of an asymmetric barrier function with internal security set, while ensuring its invariance in the presence of interference, is a challenging task to achieve stability and security. In this paper, a translation transformation is used to construct a novel asymmetric Barrier Lyapunov Function. To the best of our knowledge, this is the first designed continuously differentiable barrier Lyapunov function (BLF) of this type. A new adaptive sliding mode control (SMC) method based on asymmetrical BLF is proposed for uncertain external disturbances. The robust control method can ensure the safety performance in the presence of model uncertainty with bounded tracking error of the ship trajectory and velocity. Its fixed-time convergence is then proved by the Lyapunov theory. The results show that it has stronger anti-interference ability in bad sea conditions, better robustness, and concise form. In addition, the system can deal with unknown disturbances, ensure the safe navigation, and keep the good tracking of the route under wind and current disturbance due to random sea conditions.

Adaptive fixed-time backstepping control for three-dimensional trajectory tracking of underactuated autonomous underwater vehicles

An adaptive fixed-time backstepping control is proposed to achieve the three-dimensional trajectory tracking control of an underactuated autonomous underwater vehicle (AUV) in the presence of model uncertainty and external disturbances. In this paper, the dynamics of the AUV in terms of five degrees of freedom (DOFs) are discussed. Considering it is an underactuated AUV, a virtual velocity guidance law is derived using the backstepping method. For velocity convergence, an adaptive fixed-time control is derived without model parameters, with adaptive adjusting law tackling system unknows. Theoretical analyses demonstrate that the tracking error converges to a small bounded field within a fixed time in the proposed control scheme. The effectiveness and superiority of the proposed method are verified by simulation results.

An adaptive state estimation for tracking hypersonic glide targets with model uncertainties

To track a maneuvering hypersonic glide target, an adaptive method for state estimation is proposed with model uncertainties in this paper. Various maneuver modes lead to considerable motion model uncertainty. In the method, unknown aerodynamic accelerations are modeled as the Singer model with a small maneuver frequency. When the real motion mode deviates from the default model, the maneuver frequency is adaptively enlarged by a novel technique derived from the orthogonal principle to reduce the model error. Simultaneously, the Huber technique is adopted to address measurement model uncertainty. Two strategies are specially formulated to alleviate possible misjudgments that a type of model error activates the non-corresponding technique. The adaptive state estimation is realized under the framework of unscented Kalman filter. Through tracking different flight trajectories, simulation results demonstrate that the proposed method has stronger robustness and higher estimation accuracy than conventional methods in the presence of model uncertainties and the computation burden is significantly less than the multiple-model method.

Position synchronization for an uncertain teleoperation system with time delays using L1 theory

The problem of position tracking in teleoperation systems containing latencies and dynamical uncertainties is addressed in this work. In many applications, such as telesurgery, safe interaction with the external environment is a factor which may undermine the synchronization of the positions. For nondestructive contact with the environment, in addition to an errorless steady-state position tracking, the closed-loop system requires to have a response with the least possible overshoot. To this end, a state-feedback controller based upon L1 theory is proposed in this work. The compensator is synthesized utilizing the linear matrix inequality (LMI) technology, and the asymptotic stability of the system is verified employing Lyapunov-Krasovskii functional. Another advantage of the proposed control scheme is that it is robust to asymmetric randomly varying time delays in the communication channels. The L1-based controller is finally compared to the well-known sliding mode controller via simulation, and is proved to outperform it from maximum error point of view, while preserving low steady-state error. The proposed controller is also illustrated to be effective even in the presence of model uncertainties.

Cost-effective distributed FTFC for uncertain nonholonomic mobile robot fleet with collision avoidance and connectivity preservation

In this paper, the fault-tolerant formation control (FTFC) problem is investigated for a group of uncertain nonholonomic mobile robots with limited communication ranges and unpredicted actuator faults, where the communication between the robots is in a directed one-to-one way. In order to guarantee the connectivity preservation and collision avoidance among the robots, some properly chosen performance functions are incorporated into the controller to per-assign the asymmetrical bounds for relative distance and bearing angle between each pair of adjacent mobile robots. Particularly, the resultant control scheme remains at a cost-effective level because its design does not use any velocity information from neighbors, any prior knowledge of system nonlinearities or any nonlinear approximator to account for them despite the presence of modeling uncertainties, unknown external disturbances, and unexpected actuator faults. Meanwhile, each follower is derived to track the leader with the tracking errors regarding relative distance and bearing angle subject to prescribed transient and steady-state performance guarantees, respectively. Moreover, all the closed-loop signals are ensured to be ultimately uniformly bounded. Finally, a numerical example is simulated to verify the effectiveness of this methodology.

Platoon formation control of autonomous underwater vehicles under LOS range and orientation angles constraints

A new controller design is presented on the basis of the dynamic surface control (DSC) method for the constrained platoon formation control of the underactuated autonomous underwater vehicles (AUVs) in the presence of model uncertainties with limited field-of-view (FOV) constraints. An asymmetric barrier Lyapunov function (BLF) is applied to prevent both the line-of-sight (LOS) range and orientation angles from violating limited FOV constraints. Lyapunov method is adopted to reveal that all signals of the closed-loop control system are bounded and the constrained platoon formation tracking errors are semi-globally uniformly ultimately bounded (SGUUB). An adaptive neural network technique is employed to compensate uncertain parameters and unmodeled dynamics of the vehicles. Compared with existing works in the literature, the proposed control scheme not only forces both the range and orientation angles between AUVs to their desired values in the presence of FOV constraints in the fixed-time, but also decrease the safety hazard by avoiding any possible collision between the vehicles, increase the tracking error convergence rate and improve the transient performance of the tracking controller for all AUVs. The computer simulation results demonstrate the efficiency of this newly proposed constrained platoon formation controller for the AUVs.

Resilient Composite Learning Neuro-Adaptive Integrated Guidance and Control for reusable Launch Vehicle

An integrated guidance and control system for tracking a reference flight path trajectory of a reusable launch vehicle in the presence of model uncertainties, external disturbances, and measurement noises is proposed. To achieve this goal, a properly-trained feedforward neural network to estimate uncertainties in conjunction with a disturbance observer, which estimates external disturbances and the estimation error made by the neural network, is implemented in both the guidance and control loops. In addition, a state observer is designed whose state estimation error is included (along with the tracking error) in the learning algorithm of the neural network and the disturbance observer; such learning is called composite learning. The combined neural network and disturbance observer (employed in both the guidance and control loops) can consistently compensate for complex uncertain terms in the dynamic model of a reusable launch vehicle. More specifically, the introduced design results in an asymptotic tracking of the reference command, thereby providing a resilient flight control system. Extensive simulations are performed to show the effectiveness and robustness of the proposed integrated guidance and control system for trajectory tracking in the presence of uncertainties, aerodynamic disturbances, and measurement noises.

Trajectory Planning and Tracking for a Re-Entry Capsule with a Deployable Aero-Brake

In the last decade, the increasing use of NanoSats and CubeSats has made the re-entry capsule an emerging research field needing updates in configuration and technology. In particular, the door to advancements in terms of efficiency and re-usability has been opened by the introduction of inflatable and/or deployable aerodynamic brakes and the use of on-board electronics for active control. Such technologies allow smaller sizes at launch, controlled re-entries, and safe recovery. This paper deals with the design of a guidance and control algorithm for the re-entry of a capsule with a deployable aero-brake. A trajectory optimization model is used both in the mission planning phase to design the reference re-entry path and during the mission to update the trajectory in case of major deviations from the prescribed orbit, thanks to simplifications aimed at reducing the computational burden. Successively, a trajectory tracking controller, based on Nonlinear Model Predictive Control (NMPC), is able to modulate the opening of the aero-brake in order to follow the planned trajectory towards the target. A robustness analysis was carried out, via numerical simulations, to verify the reliability of the proposed controller in the presence of model uncertainties, orbital perturbations, and measurement noise.

A Robust Nonlinear Sliding Mode Controller for a Three-Phase Grid-Connected Inverter with an LCL Filter

In distributed power generation systems, grid-connected inverters are becoming an attractive means of delivering the energy generated from renewable sources into the grid. However, the performance of the current controller drastically decreases in the presence of model uncertainty, grid harmonics, filter parametric, and grid impedance variations, which can jeopardize the entire system’s stability. This paper presents a novel design of a super-twisting integral sliding mode control (ST-ISMC) strategy for the first time in the application of a three-phase voltage source grid-connected inverter. The designed controller has shown robustness and maintains a low total harmonic distortion (THD) in the presence of filter parameters drift, grid impedance variation, and grid harmonics distortion. The super-twisting action is added to remove the chattering problem associated with the conventional SMC strategy, and integral action is adopted to improve the grid’s current steady-state error. The modeling and simulation of a complete system are carried out using MATLAB/SIMULINK. Finally, a real-world hardware prototype system is fabricated to demonstrate the performance and effectiveness of the proposed controller.

Effect of Model Space Priors on Statistical Inference with Model Uncertainty

Bayesian model averaging (BMA) provides a coherent way to account for model uncertainty in statistical inference tasks. BMA requires specification of model space priors and parameter space priors. In this article we focus on comparing different model space priors in the presence of model uncertainty. We consider eight reference model space priors used in the literature and three adaptive parameter priors recommended by Porwal and Raftery [37]. We assess the performance of these combinations of prior specifications for variable selection in linear regression models for the statistical tasks of parameter estimation, interval estimation, inference, point and interval prediction. We carry out an extensive simulation study based on 14 real datasets representing a range of situations encountered in practice. We found that beta-binomial model space priors specified in terms of the prior probability of model size performed best on average across various statistical tasks and datasets, outperforming priors that were uniform across models. Recently proposed complexity priors performed relatively poorly.

Feasibility in real-time optimization under model uncertainty. The use of Lipschitz bounds

In real-time optimization (RTO), feedback information from the plant is used to compensate for model uncertainty. Feasibility upon convergence can be guaranteed by simply adding bias correction terms to the constraints predicted by the model. However, the RTO solutions obtained prior to convergence may violate the plant constraints in the presence of model uncertainty. The use of constraint upper-bounding functions based on Lipschitz continuity assumptions has been proposed as a means to ensure the satisfaction of constraints. This paper presents a comparative study between three different types of Lipschitz bounds for providing theoretical feasibility guarantees in different RTO schemes. Based on a novel Lipschitz bound on the constraint modeling error, robust RTO algorithms are proposed for the two model adaptation strategies that are most commonly employed in industrial RTO practice, which are the constraint–adaptation and parameter-adaptation schemes. A robust modifier-adaptation algorithm is also studied.

Prescribed Performance Rotating Formation Control of Multi-Spacecraft Systems with Uncertainties

This paper investigates the problem of rotating formation control for multi-spacecraft systems with prescribed performance in the presence of model uncertainties. Firstly, The spacecraft dynamics containing unmodelled parts is described in a polar coordinate system, which is to solve the problem of the controllable angular velocity of rotating formation. Then, the prescribed performance control method is improved by developing new prescribed performance functions. Based on the improved prescribed performance control method, the distributed controller is designed for multi-spacecraft systems to achieve rotating formations with prescribed performance, i.e., the formations error converges to a predefined arbitrarily small residual set, with convergence time no less than a prespecified value. And an RBF neural network is used to fit the unmodelled components of the spacecraft dynamics. Compared with the existing works of literature, this paper not only solves the robust prescribed performance rotating formation control of multi-spacecraft system, but also acheives rotating formation with adjustable angular velocity. Finally, the Lyapunov approach is employed for convergence analysis, and simulation results are provided to illustrate the effectiveness of the theoretical results.

Adaptive Control of Wire-Borne Underactuated Brachiating Robots Using Control Lyapunov and Barrier Functions

Executing safe brachiation maneuvers with a cable-suspended underactuated robot is a challenging problem due to the complications induced by the cable dynamics. We present and experimentally implement an online adaptive controller for a wire-borne brachiating robot swinging on a vibrating cable. An adaptive function approximation approach is proposed to estimate the unknown dynamics of the flexible cable as an external force applied to the robot. Robust control Lyapunov and barrier functions are designed and incorporated into quadratic programs to synthesize a unified adaptive control framework, which formally guarantees the stability and safety of the brachiating robot in the presence of dynamic uncertainties, actuator constraints, and obstacles in the environment. The stability analysis and derivation of the adaptation law are carried out through a Lyapunov analysis. We demonstrate and validate the proposed control framework using extensive hardware experiments with an underactuated brachiating robot operating on a flexible cable. Simulation results, hardware experiments, and comparisons with a baseline controller show that the proposed quadratic programming-based controller achieves reliable tracking performance and disturbance estimation in the presence of model uncertainties, actuator limits, and safety constraints.

Special issue on PID control in the information age: Theoretical advances and applications

Special issue on PID control in the information age: Theoretical advances and applications