Neural Disturbance Research Articles

Adaptive neural observer-based output feedback anti-actuator fault control of a nonlinear electro-hydraulic system with full state constraints

This paper proposes an adaptive output feedback full state constrain (FSC) controller based on the adaptive neural disturbance observer (ANDO) for a nonlinear electro-hydraulic system (NEHS) with unmodeled dynamics. The Barrier Lyapunov Functions (BLFs) are utilized to ensure that all states of the system are specified within the constraints, and the approximation ability of radial basis function neural networks (RBFNNs) is used to cope with the unknown nonlinear functions. An adaptive neural compensation disturbance observer is elaborated to estimate the compound disturbance and oil leakage fault, effectively addressing these negative effects. Subsequently, observer-based output feedback command filter scheme is developed to diminish the explosion of complexity in the taking derivative procedure and obtain high precise tracking performance. The convergence of tracking errors into a small region around the equilibrium is demonstrated by the Lyapunov stability theory. Ultimately, simulation, experiment, and comparative studies are provided to further validate the effectiveness of the proposed control approach.

Adaptive neural network anti-disturbance control of a cable-driven flexible-joint-based underwater vehicle manipulator system with dead-zone input nonlinearities via multiple neural observers

To achieve the requirements of lightweight, low energy consumption, and low inertia of an underwater vehicle manipulator system, a cable-driven manipulator is installed on the underwater vehicle to form a cable-driven flexible-joint-based underwater vehicle manipulator system (CDFJ–UVMS). The CDFJ–UVMS is a complex nonlinear system subject to model uncertainties, complex marine environment disturbances, and actuator dead-zone nonlinearity. To design track controllers, the CDFJ–UVMS dynamics is divided into two parts: known and unknown. Subsequently, a radial basis function neural network is adopted to approximate the unknown nonlinearity. A neural network performance observer is constructed, whose estimation error is then used to design a novel neural disturbance observer (NDO) to estimate the total disturbance. Finally, an adaptive neural network control method is proposed for the CDFJ–UVMS based on the NDO, neural network compensator, and neural performance observer. The stability of the closed-loop system is analyzed using the Lyapunov method. The proposed control algorithm is applied to a CDFJ–UVMS with two cable-driven joints and compared with other control methods to show the effectiveness of the proposed control algorithm.

Robust adaptive tracking control for dynamic positioning ships subject to dynamic safety constraints and actuator saturation

This paper proposed a novel robust adaptive trajectory tracking control scheme for dynamic positioning (DP) ships subjected to unknown time-varying disturbances, unknown model parameters, dynamic safety constraints, and actuator saturation. The dynamic safety constraints were guaranteed based on the tan-type barrier Lyapunov function (BLF) technique, which ensured the position and velocity of the DP vessel within the given constraints. Moreover, the radial basis function neural network (RBFNN) was adopted to approximate the unknown parameters of the dynamics model, and an adaptive neural disturbance observer (ANDO) was designed to compensate for lumped disturbances. An anti-windup system was developed to handle the actuator saturation effect. By combining the above techniques and back-stepping method, the final control law was presented, and the semi-global asymptotic convergence was achieved via rigorous theoretical analysis and Lyapunov proof. The results of simulations demonstrated the superiority and robustness of this approach in resolving the trajectory tracking challenges of DP ships. Moreover, the proposed control scheme is highly effective in optimizing the trajectory tracking of DP ships, as it takes into account the multiple constraints that may arise during the process, which is a valuable contribution to the field of DP ship trajectory tracking, providing a more comprehensive and effective solution to this complex problem.

Fault-tolerant learning control of air-breathing hypersonic vehicles with uncertain parameters and actuator faults

This paper investigates fault-tolerant learning control for air-breathing hypersonic vehicles (AHVs) subject to parametric uncertainties, external disturbances, and actuator faults. By treating the flexible dynamics as equivalent disturbance, the AHV model can be decomposed into velocity subsystem and altitude subsystem. An intelligent fault diagnosis approach is deployed to estimate the actuator fault information for each subsystem sequentially. Furthermore, combined with a fixed-time neural disturbance observer, composite learning control is deployed to construct control commands using the historical stack to improve the learning accuracy, while compensating for lumped disturbances, including fault diagnosis errors and external disturbances. The designed controller addresses the fault-tolerant tracking problem by combining offline and online learning strategies, which is a significant advantage over other existing AHV controllers. Simulation results validate the effectiveness of the fault-tolerant learning control.

Finite-time adaptive anti-disturbance constrained control design for dynamic positioning of marine vessels with simulation and model-scale tests

For practical and complex offshore operations, preventing violations of system constraints can ensure safety and avoid potential marine accidents in harsh environments. This paper addresses a finite-time constrained control for dynamic positioning (DP) of vessels in the presence of inherent uncertainties, ocean disturbances, full-state and input constraints. For operational safety assurance, the core logic of the proposed constrained control strategy is to constrain the state variables including position output, velocity state, input control command within the designed safe region by employing barrier function and auxiliary system, respectively. To attenuate the adverse effects of unknown disturbances on safety and stability, an adaptive neural disturbance observer (ANDO) is constructed to compensate for lumped disturbances caused by inherent uncertainties, unmodeled dynamics, and sea loads. Then, the composite finite-time anti-disturbance constrained control strategy is formally proposed, which can guarantee semi-global practical finite-time stable (SGPFS) of the closed-loop systems. In offshore engineering, control schemes are not only theoretically feasible, but also need to be accurately verified to provide practical guidance value. For this purpose, the simulation and model-scale test are carried out to realistically reproduce the time-domain motion response of the vessels under ocean disturbances. The verification results further elucidate the applicability and feasibility of the proposed control design in practical offshore engineering.

Neural network-based adaptive synchronization for second-order nonlinear multiagent systems with unknown disturbance.

This paper handles the distributed adaptive synchronization problem for a class of unknown second-order nonlinear multiagent systems subject to external disturbance. It is supposed to be an unknown one for the underlying external disorder. First, the neural network-based disturbance observer is developed to deal with the impact induced by the strange disturbance. Then, a new distributed adaptive synchronization criterion is put forward based on the approximation capability of the neural networks. Next, we propose the necessary and sufficient condition on the directed graph to ensure the synchronization error of all followers can be reduced small enough. Then, the distributed adaptive synchronization criterion is further explored because it is difficult to obtain the relative velocity measurements of the agents. The distributed adaptive synchronization criterion without the velocity measurement feedback is also designed to fulfill the current investigation. Finally, the simulation example is performed to verify the correctness and effectiveness of the proposed theoretical results.

Contour control of biaxial motion system based on RBF neural network and disturbance observer

Friction is the main factor which degrades the control precisions of the servo system. In this paper, a cross coupled control method based on RBF neural network and disturbance observer is proposed for multi-axis servo system with LuGre friction, in order to implement high precision tracking and contouring control. Firstly, a feedback linearization controller is designed to realize the position stable tracking for single-axis motion; then, the disturbance observer is used to observe and compensate the friction. However, in practical application, the observation gain is difficult to select, and it is easy to cause observation error. In order to enhance the tracking accuracy and system robustness, the RBF neural network is introduced to approximate the disturbance observation error online. Finally, the cross coupled control is used to coordinate the motion between the axes to improve the contour accuracy. The simulation results show that the proposed method can effectively compensate the influence of friction on the system, has good tracking accuracy and high contour control precision.

Neural dysfunctions following experimental permanent occlusions of bilateral common carotid arteries cause an increase of rat voluntary alcohol drinking behavior.

We have previously reported that ischemic animal models treated with a respiratory inhibitor, rotenon, show an increased voluntary alcohol intake. Although it is clear that ischemic brain, as a result of reduced-blood flow, shows pathological events and/or neuro-degenerations apparently, little is known of causal relationship between the mechanism of neural dysfunction and voluntary alcohol consumption. Authors have investigated effects of permanent two-vessel occlusion (p2VO) on rat voluntary alcohol drinking behavior. In first experiment the p2VO-treated rats showed an increase of voluntary alcohol drinking behavior, as compared with sham controls. Using brain microdialysis technique, increases of only nucleus accumbens (ACC) dopamine (DA) releases were suppressed in the p2VO-treated rats significantly, following the high K+ (40mM) perfusion through the microdialysis probe membrane. Alcohol (200mM) perfusion-induced DA and serotonin (5-HT) releases in the ACC of the p2VO-treated rats were suppressed significantly in the second experiment, as compared with the sham-treated rats. In third experiment p2VO-treated rats showed significant decreases of the contents of DA, not 5-HT, in the ACC, caudate-putamen (C/P), ventral tegmental area-substantia nigra (VT/SN) and lateral hypothalamus (LH). Dopaminergic neurons in the ACC showed more functional vulnerability against the p2VO treatments, as compared with the serotonergic neurons. An increase of alcohol intake in the p2VO-treated rats means the compensation for the neural degeneration of the dopaminergic system in the ACC consisted brain rewarding system. It was likely suggested that neural disturbance of higher functions involved with incomplete global brain ischemia leads the risk of an abnormal alcohol drinking in human.

Adaptive synchronization of marine surface ships using disturbance rejection without leader velocity

This work realizes the adaptive neural disturbance rejection for the leader–follower cooperative synchronization of surface ships with model perturbations and ocean disturbances without leader velocity measurements. The virtual ship alleviates the requirements on leader ship’s velocities such that the information requirements are only position and heading on the leader ship. The adaptive neural networks approximate model perturbations. The robustifying term attenuates neural network approximation errors. The adaptive neural network-based disturbance observer achieves the disturbance rejection which is integrated with the dynamic surface control technique. The supply ship synchronization control system is ensured to be practical stable. The synchronization control realizes the ship’s cooperative synchronization navigation. Simulations with comparisons validate the synchronization scheme.

Event-based disturbance compensation control for discrete-time SPMSM with mismatched disturbances

This paper investigates the problem of event-based disturbance compensation control for discrete-time surface-mounted permanent magnet synchronous motor (SPMSM) subject to both mismatched external disturbances and the limited communication bandwidth. The mismatched external disturbances make it impossible to transform the original model into the multiple-step-ahead predictor model by using the existing predictor technology. To tackle such a challenge, a novel backstepping-based disturbance compensation control framework is firstly proposed for the discrete-time SPMSM with known system dynamics. In the proposed framework, the disturbance observer is designed to compensate for the external disturbances, and the novel variable substitution/decoupling technology is proposed to design the event-based controller for the original controlled system. The proposed controller is composed of the event-based feedback control signal and the feedforward disturbance compensation signal, thereby improving the system disturbance rejection ability and mitigating the communication resource. Subsequently, an event-based adaptive neural control scheme is proposed by combining a modified neural disturbance observer. The proposed scheme ensures that the tracking error converges to a small neighbourhood of the origin, all the signals in the closed-loop system are bounded and meanwhile the communication resources are greatly reduced. The effectiveness of the proposed controller is illustrated through the simulation results.

Adaptive Neural Motion Control of a Quadrotor UAV

Unmanned Aerial Vehicles have generated considerable interest in different research fields. The motion control problem is among the most important issues to be solved since system dynamic stability depends on the robustness of the main controller against endogenous and exogenous disturbances. In spite of different controllers have been introduced in the literature for motion control of fixed and rotary wing vehicles, there are some challenges for improving controller features such as simplicity, robustness, efficiency, adaptability, and stability. This paper outlines a novel approach to deal with the induced effects of external disturbances affecting the flight of a quadrotor unmanned aerial vehicle. The aim of our study is to further extend the current knowledge of quadrotor motion control by using both adaptive and robust control strategies. A new adaptive neural trajectory tracking control strategy based on B-spline artificial neural networks and on-line disturbance estimation for a quadrotor is proposed. A linear extended state observer is used for estimating time-varying disturbances affecting the controlled nonlinear system dynamics. B-spline artificial neural networks are properly synthesized for on-line calculating control gains of an adaptive Proportional Integral Derivative (PID) scheme. Simulation results highlight the implementation of such a controller is able to reject disturbances meanwhile perform proper motion control by exploiting the robustness, disturbance rejection, adaptability, and self-learning capabilities.

Robust PID Control of Multicompartment Lung Mechanics Model Using Runge-Kutta Neural Disturbance Observer

This paper proposes Runge-Kutta neural disturbance observer to enhance the robustness of PID control of a system with general multicompartment lung mechanics. It is designed to observe the states of a particular type continous time, single-input single-output system where the states cannot be measured but can be observed through the single output and there exists parametric uncertainity or disturbance affecting the underlying system. It utilizes artificial neural network to estimate the disturbance online. Once an accurate disturbance estimation is obtained, it is incorporated in the system state equation and passed through the well-known Runge-Kutta integrator to predict the state values. Hence, the predicted states are obtained considering the disturbance and more robust state observation is achieved. The proposed observer is simple and easy to implement. Adaptation of the neural network is performed using gradient descent with an adaptive learning rate which guarantees convergence. The simulation results demonstrate that the proposed observer gains a significant success in enhancing the robustness of PID control at even high level of disturbance. Note that, multicompartment lung mechanics system is a stand-in model that can mimic the behavior of human lung. Thus, it is appropriate for hardware-in-the-loop simulation which opens a path to the real-patient-tests of mechanical respiratory systems in the future.

Neural observer-based path following control for underactuated unmanned surface vessels with input saturation and time-varying disturbance

In this study, a new neural observer-based dynamic surface control scheme is proposed for the path following of underactuated unmanned surface vessels in the presence of input saturation and time-varying external disturbance. The dynamic surface control technique is augmented by a robust adaptive radial basis function neural network and a nonlinear neural disturbance observer. Radial basis function neural network is employed to deal with system uncertainties, and the nonlinear neural disturbance observer is developed to compensate for the unknown compound disturbance that contains the input saturation approximation error and the external disturbance. Moreover, the stringent known boundary requirement of the unknown disturbance constraint is eliminated with the proposed nonlinear neural disturbance observer. Meanwhile, to deal with the non-smooth saturation nonlinearity, a new parametric hyperbolic tangent function approximation model with arbitrary prescribed precision is constructed, which results in the transient performance improvement for the path following control system. Stability analysis shows that all the signals in the closed-loop system are guaranteed to be ultimately bounded. Comparative simulation results further demonstrate the effectiveness of the proposed control scheme.

Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors

In this work, a new intelligent control strategy based on neural networks is proposed to cope with some external disturbances that can affect quadrotor unmanned aerial vehicles (UAV) dynamics. Specifically, the variation of the system mass during logistic tasks and the influence of the wind are considered. An adaptive neuromass estimator and an adaptive neural disturbance estimator complement the action of a set of PID controllers, stabilizing the UAV and improving the system performance. The control strategy has been extensively tested with different trajectories: linear, helical, circular, and even a lemniscate one. During the experiments, the mass of the UAV is triplicated and winds of 6 and 9 in Beaufort’s scale are introduced. Simulation results show how the online learning of the estimator increases the robustness of the controller, reducing the effects of the changes in the mass and of the wind on the quadrotor.

Mandibulotomy Approach for Resection of Maxillary Tumours: A Clinical Review.

The objective of this study was to assess the accessibility in the resection of maxillary tumours, resection margin status, and morbidity following maxillectomy through lip split with paramedian mandibulotomy approach. A retrospective review of 20 consecutive patients who underwent maxillectomy with resection of primary tumours through lip split mandibulotomy approach with supraomohyoid neck dissection for maxillary tumours between 2008 and 2016. Patients details including the tumours site, extension and neck node involvement. were recorded. Resection technique, status of surgical resected margins was also discussed. Disease status was obtained from patients follow up records. Morbidity was assessed at mandibulotomy site in terms of infection, osteotomy healing, neural disturbance and mouth opening. The institutional research committee approval was taken for this study. All patients underwent adequate en bloc resection of the tumours, except in two patients in whom superior margins was positive. Osteotomy site healed well in our all patients except in one patient in whom there was infection at the osteotomy site during post radiation therapy. Minimal neural morbidity was encountered in four patients (three patients had lingual nerve hypothesia and two patients had inferior alveolar nerve hypothesia) which recovered in all four patients, over the 6th month post-operative period. Post-operative interincisal distance was satisfactory with a mean of 30.5mm. Mandibulotomy with lip split is considered to be an ideal approach to access tumours of maxilla and its adjacent structures, SOHND with level III clearance. This approach provide excellent accessibility for en bloc resection of operable maxillary tumours with good outcome of resultant scar and minimal morbidity.

Composite Learning Sliding Mode Control of Flexible-Link Manipulator

This paper studies the control of a flexible-link manipulator with uncertainty. The fast and slow dynamics are derived based on the singular perturbation (SP) theory. The sliding mode control is proposed while the adaptive design is developed using neural networks (NNs) and disturbance observer (DOB) where the novel update laws for NN and DOB are designed. The closed-loop system stability is guaranteed via Lyapunov analysis. The effectiveness of the proposed method is verified via simulation test.

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.

Subjective and qualitative assessment of neural disturbance after inferior alveolar nerve transposition for dental implant placement.

BackgroundThe purpose of this retrospective study was to accumulate data regarding the quality of postoperative neurosensory function after inferior alveolar nerve (IAN) transposition for dental implant placement.MethodsThe study included seven consecutive patients who underwent IAN transposition surgery for the insertion of a dental implant into the atrophic posterior mandible. Of these, six patients (seven sides) were available for long-term assessment of postoperative IAN function. Neurosensory disturbance of the IAN was assessed objectively using the modified SW perception test reported by Semmes and Weinstein. In addition, the quality of nerve paralysis was assessed according to the criteria reported by Highet.ResultsThe median follow-up time was 49 months (range 12–105 months). No implant loss was observed during the follow-up. All patients experienced numbness immediately and the days after surgery. Complete recovery of neural function was observed on two sides; weak hypoesthesia was observed on two sides, moderate hypoesthesia on two sides, and severe hypoesthesia on one side. However, only one patient expressed concern about IAN function.ConclusionsIAN transposition is a useful method for placing implants in the atrophic posterior mandible. However, the procedure is complicated, with the possibility of some degree of neurosensory disturbance, although in most of our cases, it resolved within a clinically acceptable period.

Neural Network Adaptive Control of Free Floating Space Robot with Actuator Saturation

This paper proposes an adaptive neural network law for trajectory tracking of a class of free-floating space robot with actuator saturation. Using neural network with global approximation, the control strategy design an on-line real time adaptive learning law to approach the uncertain model and the actuator saturation nonlinearity. The neural network approach errors and outside disturbance can be eliminated by a robust controller.The control strategy need not depend on the model, and can be used under actuator saturation.The control strategy can guarantee the stability of system and the asymptotic convergence of tracking errors based on the Lyapunov’s theory. The simulation results indicate that the proposed strategy can effectively work with actuator saturation.

Spatiotemporal independent component analysis combine general linear model applied to fMRI for eliminating neural noise

Functional magnetic resonance imaging (fMRI) has recently become an effective means to explore the mechanism of functional rehabilitation in stroke patients. Neural noise is an inevitable structural noise, and is an important factor caused individual differences in fMRI data, therefore, eliminating the neural noise is being regarded as one of the task that cannot be ignored. In this paper, a new algorithm combines spatiotemporal independent component analysis and general linear model (GLM) is proposed to eliminate the effect caused by excess neural activity. This new algorithm simultaneously maximizes the independence over time and space in fMRI data for establishing the spatiotemporal balance. The new technique was applied to extract the active regions of ankle dorsiflexion during fMRI scanning process. Compared to results of GLM, the results of new combined algorithm is more reasonable with an 8% improvement in correlation coefficient. It confirmed that this new algorithm is effective in eliminating system noise and neural disturbance.