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Neuroinflammation is one of the most significant factors involved in the initiation and progression of Parkinson’s disease. PD is a neurodegenerative disorder with a motor disability linked with various complex and diversified risk factors. These factors trigger myriads of cellular and molecular processes, such as misfolding defective proteins, oxidative stress, mitochondrial dysfunction, and neurotoxic substances that induce selective neurodegeneration of dopamine neurons. This neuronal damage activates the neuronal immune system, including glial cells and inflammatory cytokines, to trigger neuroinflammation. The transition of acute to chronic neuroinflammation enhances the susceptibility of inflammation-induced dopaminergic neuron damage, forming a vicious cycle and prompting an individual to PD development. Epigenetic mechanisms recently have been at the forefront of the regulation of neuroinflammatory factors in PD, proposing a new dawn for breaking this vicious cycle. This review examined the core epigenetic mechanisms involved in the activation and phenotypic transformation of glial cells mediated neuroinflammation in PD. We found that epigenetic mechanisms do not work independently, despite being coordinated with each other to activate neuroinflammatory pathways. In this regard, we attempted to find the synergic correlation and contribution of these epigenetic modifications with various neuroinflammatory pathways to broaden the canvas of underlying pathological mechanisms involved in PD development. Moreover, this study highlighted the dual characteristics (neuroprotective/neurotoxic) of these epigenetic marks, which may counteract PD pathogenesis and make them potential candidates for devising future PD diagnosis and treatment.

The aeroelastic response to time-dependent gusts or turbulence should be considered in airplane design. A robust generalized predictive control law for gust response alleviation is designed and simulated on an aircraft model by using the real wind tunnel response and approximated gust input. Based on the open-loop response of an aircraft model at different test conditions, a nominal Auto Regressive (AR) model with parameter uncertainty is identified. Singular Value Decomposition is designed to reduce the dimension of the uncertainty matrix. Afterwards, with the identified online aeroelastic model and its uncertainty, a robust generalized predictive control (GPC) is applied to alleviate the wing tip acceleration at all test conditions, including varying flow velocities and varying gust frequencies. Finally, the alleviation effect of gust response at different test conditions is estimated based on the comparison of simulated closed-loop acceleration with an experimental open-loop one. The comparison indicates that after robust gust response alleviation control, the wing tip acceleration response can be reduced by up to 70% under all test conditions. Remarkably, the control law is robust to the parameter uncertainties and input uncertainties, which is applicable to the gust alleviation wind tunnel test.

Three-axis spacecraft simulators are widely used to verify spacecraft control strategies. Designing a simulator whose moment of inertia are similar to the spacecraft's is important for control research. Moreover, to reduce the disturbance of gravity torque, the centre of mass of the simulator attitude stage must be exactly aligned with the centre of rotation. Various sensors and actuators are on the simulator. An optimisation method for the locations of these devices on the simulator is presented. In this method, these devices are described as rectangles and circles. Their spatial relationship are expressed by a series of inequalities. The total inertia parameters of these devices are optimised.

To ensure the effectiveness of the RF(Radio Frequency) guided HILS(Hardware-In-the-Loop Simulation) system, the credibility evaluation was necessary. At present, most of the methods were applied in the case of the simulation system input being consistent with the real system input, which is difficult to achieve in some subsystem of the RF guided HILS system. In this paper, the relative complete system reliability evaluation index system was established. Subsystems were classified, for different categories of subsystem, corresponding reliability assessment methods were studied. In particular, reliability evaluation methods based on performance level were put forward, when certain subsystems cannot be evaluated by consistency analysis methods. A simple example was given to show the evaluation process.

Purpose – The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs). Design/methodology/approach – Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management. Findings – Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system. Originality/value – The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.

This paper presents a hierarchical simultaneous localization and mapping (SLAM) system for a small unmanned aerial vehicle (UAV) using the output of an inertial measurement unit (IMU) and the bearing-only observations from an onboard monocular camera. A homography based approach is used to calculate the motion of the vehicle in 6 degrees of freedom by image feature match. This visual measurement is fused with the inertial outputs by an indirect extended Kalman filter (EKF) for attitude and velocity estimation. Then, another EKF is employed to estimate the position of the vehicle and the locations of the features in the map. Both simulations and experiments are carried out to test the performance of the proposed system. The result of the comparison with the referential global positioning system/inertial navigation system (GPS/INS) navigation indicates that the proposed SLAM can provide reliable and stable state estimation for small UAVs in GPS-denied environments.

A guidance law has been designed to guide the small unmanned aerial vehicle towards the predefined horizontal smooth path. The guidance law only needs the mathematical expression for the predefined path, the positions, and the velocities of the vehicle in the horizontal inertial frame. The stability of the guidance law has been demonstrated by the Lyapunov stability arguments. In order to improve the path following performance, one of the parameters of the guidance law is tuned by using the fuzzy logic which will still keep its stability. The simulation experiments in the Matlab/Simulink environment to realize the square-, circular-, and the athletics track-style paths following are given to verify the effectiveness of the proposed method. The simulation results show that the path following performance will be improved with smaller overshoot and oscillation amplitude and shorter arrival time with the parameter tuned.

In this study, a composite fight control method for the path following of a small unmanned helicopter is proposed. The control system design adopts the hierarchical control architecture that is composed of an inner-loop, an outer-loop and a planning layer. The inner-loop controller design is based on the setpoint tracking linear quadratic Gaussian control technique which can not only stabilise the dynamics of the helicopter, but also track the reference control signals provided by the outer-loop controller well. The core part of the outer-loop controller design is based on the notion of vector field, which is used for generating the desired heading rate command. Lyapunov stability arguments are applied to demonstrate the asymptotic approximation to the desired path in the presence of wind disturbance. Experimental results are presented to verify the validity of the method.

In this paper, analytical techniques and fuzzy logic method are applied to the dynamic modeling and efficient swimming control of a robotic fish. The bioinspired robotic fish, which follows an exact replica of a live mackerel (Scomber scombrus), is modeled by treating the undulating body and flapping tail independently using analytical methods. Comparing the results of simulations and experiments shows the feasibility of the dynamic model. Using this model, we found that the harmonic control of the Strouhal number and caudal fin angle of attack is a principal mechanism through which the robotic fish can obtain high thrust efficiency while swimming. The fuzzy control method, which is based on the knowledge of the robotic fish's dynamic behavior, has successfully utilized this principal mechanism. By comparing the thrust performance of the robotic fish with different control methods via simulation, we established that the fuzzy controller was able to achieve faster acceleration and smaller steady-state error than what could be achieved from an open-loop and conventional proportional-integral-derivative controller. The thrust efficiency during steady state was superior to that with conventional control methods. We also found that, when using the fuzzy control method, robotic fish can always swim near a “universal” Strouhal number that approximates to the swimming of live fish.

This paper focuses on the system identification of a small unmanned helicopter in hover or low-speed flight conditions. A novel genetic algorithm including chaotic optimization operation named chaos-genetic algorithm (CGA) is proposed to identify the linear helicopter model. Based on the input-output data collected from real flight tests, the identification performance of CGA is compared with those calculated by the traditional genetic algorithm (TGA) and the prediction error method (PEM). The accuracy of the identified model is verified by simulation in time domain. Additionally, the small unmanned helicopter is stabilized by a linear quadratic Gaussian (LQG) regulator based on the proposed identified model. In the automatic flight experiments, the achievement of automatic take-off and landing, hovering performance within a 1.2 m diameter circle and point-to-point horizontal polyline flight also demonstrates the accuracy of the identified model and the effectiveness of the proposed method.

This paper has presented an implementation of visual based non-GNSS navigation method for small VTOL (Vertically Taking-off and Landing) UAV (Unmanned Aerial Vehicle). A novel method of constructing landmarks is described in detail. It uses a pair of landmarks instead of the general landmark which usually is a single geometry or coding image. The paired-landmark method inherits general landmark methods' advantages of low-cost, high-efficiency and the ability of absolute positioning. And it also solves the problem of requiring too much different characteristic landmarks when handling large area navigation. The paired-landmark method has been verified by a real parameter simulation environment. Real flight experiment is also carried out to test the effectiveness and performance of the paired-landmark navigation method. It can be concluded that, besides achieved the expected positioning precision, the proposed system also reduced the consumption of calculation and memory space.

This paper presents a vision-aided inertial navigation system for small unmanned aerial vehicles (UAVs) in GPS-denied environments. During visual estimation, image features in consecutive frames are detected and matched to estimate the motion of the vehicle with a homography-based approach. Afterwards, the visual measurement is fused with the output of an inertial measurement unit (IMU) by an indirect extended Kalman filter (EKF). A delay-based approach for the measurement update is developed to introduce the visual measurement into the fusion without state augmentation. This method supposes that the estimated error state is stable and invariant during the second half of one visual calculation period. Simulation results indicate that delay-based navigation can reduce the computational complexity by about 20% compared with general augmented Vision/INS (inertial navigation system) navigation, with almost the same estimate accuracy. Real experiments were also carried out to test the performance of the proposed navigation system by comparison with the augmented filter method and a referential GPS/INS navigation.

This paper presents a monocular visual simultaneous localization and mapping (SLAM) system for a small unmanned aerial vehicle (UAV) in GPS-denied environments. A single camera is used to measure the motion of the vehicle and detect features (landmarks) for the map building. The SLAM estimates the positions of the UAV and the features by an extended Kalman filter (EKF), which takes the velocity estimated by the fusion of the inertial and visual measurements as the input. An inverse depth method is adopted for the feature initialization. Both simulations and experiments are carried out to verify the effectiveness of this system.

This paper has presented an implementation of a RPV control system for a small unmanned helicopter. Both the hardware and the software of the UAV have been described in detail. An EKF-based GPS/INS navigation system is developed with low-cost inertial sensors and a GPS receiver. A chaos-genetic algorithm is proposed in the system identification of the helicopter. A LQG-based RPV (Remotely Piloted Vehicle) control scheme is applied in the control system. The airline flight of the control system has been verified by a hardware-in-loop simulation environment. Real flight experiment is also carried out to test the performance of the RPV control system. It can be concluded that, besides the stable control of attitude, the proposed system is also able to track the target velocity quickly and realize the RPV control of the small unmanned helicopter.

In this paper we present a vision based guidance approach for small unmanned helicopters. The approach is integrated with Hu invariant moments based structural target recognition algorithm for visual acquisition of the target (a helipad) and a guidance model for locating the target. A color filter is designed to eliminate noises from the image by using HSL model. The improved structural target recognition algorithm can work robustly under the condition that the image is distorted by perspective projection. Besides, a visual guidance model is designed and implemented to locate the target accurately. With the help of the visual guidance, small unmanned helicopters can land on the target automatically. Finally, experiments are carried out to test the effectiveness and robustness of our algorithms.

This paper proposes a nonlinear controller that allows the unmanned air vehicle (UAV) to follow a predefined path. The controller only needs the information of the inertial speed of the UAV and the distance between the vehicle and the desired path. In order to improve the performance of the controller, a parameter of the controller is adjusted by a fuzzy logic rule. Lyapunov stability arguments are used to demonstrate that the path following error will be regulated to zero, even in the presence of wind disturbances. Simulation results in Matlab are given to show the effectiveness of the controller.

Focusing on the problem of autonomous flight for a small unmanned helicopter in GPS-denied environments, this paper proposes a navigation system which combines monocular visual and inertial measurements. The homography based visual odometry, as well as the inertial measurement from an IMU, can estimate the motion of a small unmanned helicopter. An extended Kalman filter is used to fuse the estimations of both visual and inertial sensors to provide the available navigation data. Finally, the effectiveness of this system is verified by real experiments. The Vision/IMU navigation has almost the same performance as the traditional GPS/IMU navigation.

In this paper, a fuzzy-based threshold method is proposed in image processing of SIFT which is used in visual navigation of small UAVs. By adjusting the thresholds of SIFT online fuzzily, it provides a stable number of features detected from aerial image sequence. The visual navigation estimates the motion of the small UAV by tracking and matching these features extracted from the consecutive image frames. Real experiments verify the stability and advantage of this method in comparison with the fixed and PID-based threshold methods. This proposed method is able to stabilize the number of features regardless of the changes of the aerial images.