Autopilot Research Articles

Trajectory Tracking Based on Active Disturbance Rejection Control for Compound Unmanned Aircraft

The compound unmanned aircraft is provided with three primary flight modes, which are helicopter flight mode in low forward speed flight, airplane flight mode in high forward speed flight and transition flight mode in middle forward speed flight. For the different flight modes, an appropriate flight control law is the need to ensure good flying qualities. In this paper, a trajectory tracking control system based on the active disturbance rejection controller (ADRC) for the compound unmanned aircraft is proposed to adapt the full flight modes. A flight dynamics model and a Simulink simulation model of the compound unmanned aircraft are developed. The transition flight control strategy is analyzed and synthesized to meet the requirement of control strategy in the full flight modes. The internal uncertainties and external disturbance of the UAV are estimated with an extended state observer to compensate control input. A genetic algorithm-particle swarm optimization (GA-PSO) algorithm is utilized to optimize the controller parameters. The simulation of route tracking and spiral climb with different flight modes is conducted, which demonstrates the tracking ability, interference rejection, robustness and effectiveness of the developed controller in the full flight modes.

Suboptimal State Tracking Control Applied to a Nonlinear Fractional-Order Slewing Motion Flexible Structure

Abstract The control of slewing motion flexible structures is important to a number of systems found in engineering and physical sciences applications, such as aerospace, automotive, robotics, and atomic force microscopy. In this kind of system, the controller must provide a stable and well-damped behavior for the flexible structure vibrations, with admissible control signal amplitudes. Recently, many works have used fractional-order derivatives to model complex and nonlinear dynamical behavior present in the mentioned systems. In order to perform digital computer-based control of fractional-order dynamical systems, a time discretization of the equations is necessary. In many cases, the Grünwald–Letnikov method is used, resulting in an implicit integration method. In this work, a nonlinear slewing motion flexible structure is modeled considering a fractional-order viscous damping in the flexible beam motion. To obtain an explicit integration method, based on the Grünwald–Letnikov definition, the discretization of the dynamical equations is performed considering the existence of sample and hold circuits. In addition, real-time suboptimal infinite horizon tracking control system strategies, namely, the linear quadratic tracking and the state-dependent Riccati equation tracking controller, are designed and implemented to control the fractional-order slewing motion flexible system. The general behavior and performance of the control systems are tested for parameter uncertainties related to the order of the fractional derivatives.

Data Detection Device for Turret Motion Image Using Photoelectric Tracking Control System and Its Performance Analysis

The photoelectric tracking control system is a system composed of photoelectric signal detection, signal processing, servo control system and mechanical structure, which plays a crucial role in the field of target tracking. The turret needs to be tested when it leaves the factory, including high-low direction, horizontal direction, aiming speed and other indicators. Moreover, there are large errors in the manual testing methods such as recorder and recording pen, so the photoelectric tracking device is used for the detection of the turret system. First, the hardware of the photoelectric tracking system is designed, including the main control circuit, peripheral expansion module circuit, intelligent power drive circuit and a detection circuit. The system is connected to the camera. The optical signal sent by the system will hit the fixed target. The upper computer will track the trajectory of the light in real-time and then track the trajectory of the turret to obtain the coordinates of the optical signal and the offset angular velocity of the turret. First, color conversion is conducted on the image data collected by photoelectric tracking through the weighted average method. The complete motion path of the turret is obtained through an image denoising algorithm to obtain the motion speed of the turret. In the test, the photoelectric tracking system is first connected to the motor, and enters a stable state when the motor rotor is 80 ms. The rotor speed is faster. When the rotor speed decreases, the system’s current ripple is large, and then decreases rapidly. After the control system is installed in the turret, its display can be driven by FPGA programming. The VGA display of influence time is set, the editing program is downloaded to the FPGA development board, and the gray image of the turret motion track can be seen through the VGA display.

Event-Triggered Dissipative Tracking Control of Networked Control Systems With Distributed Communication Delay

This article presents the design of an event-trigger-based networked robust dissipative tracking control systems. An event-triggered mechanism (ETM) is proposed by introducing the probability density distribution of communication delay. Thus, it can yield less conservative results. The tracking error system is transferred by a distributed communication delay system wherein the delay probability density is viewed as the kernel of the distributed delay. The threshold of the proposed ETM is designed as a dynamic parameter to adapt the transmission of the control signal. By applying the Lyapunov method, sufficient conditions are achieved to ensure the stability of the tracking error system with strictly dissipative performance, which includes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance and passive performance. Based on the dissipativity analysis condition, a co-design method with both tracking control and the ETM is derived in a united framework. Experiments on networked dc motor tracking system are provided to show the effectiveness of the presented method.

A Novel Method for Software Reliability Assessment via Neuro-Fuzzy System

Nowadays, the utilization of software engineering in various areas of technology is remarkably increased. As a matter of fact, it is used in many critical applications such as eye surgery, autopilot systems of airplanes, centralized traffic control (CTC), and so on. Therefore, the reliability of software is very important, and it plays an essential role in the lifetime of the software. Software reliability is one of the main characteristics of software quality. Moreover, the rapid assessment of the reliability of the application is essential during the software life cycle. In this paper, Iuse the neuro-fuzzy methods to assess the software's reliability in order to cope with uncertainties in measuring the actual parameters of the software. By designing neuro-fuzzy inference systems and applying four parameters of the ISO/IEC 9126quality model(i.e., the maturity of software, fault-tolerant, recoverability, and reliability compliance) and finding the parameters of a fuzzy system by exploiting approximation techniques from neural networks, Ipresent an integrated assessment model for evaluation of software reliability. The case study used in this paper to evaluate the proposed method is the software income tax calculator. By applying the input parameters, Iobserve that the software reliability is 0.65. software reliability in our proposed method is more exact than software reliability in the fuzzy multi-criteria and fuzzy method because The weights of the input parameters have been set by experts and software developers, and simulations are carried out using MATLAB tool (ANFIS). Simulations confirm that the proposed method provides acceptable results.

Research on human-like lane-changing control of intelligent vehicles based on extension goodness evaluation

In order to improve the trajectory tracking accuracy and passenger comfort for intelligent vehicle lane-changing at medium and high speed, this paper proposes a new control algorithm based on extension evaluation system for intelligent vehicle lane-changing. In this research, firstly, a vehicle-road dynamics model has been developed. In addition, the actual driving trajectory data of skilled drivers are collected by driving simulator, and the lane-changing path planning model is established based on generalized regression neural network (GRNN). Last, in order to improve the tracking ability to the planning path, the trajectory tracking controller is established. A two-layer structure of lateral trajectory tracking control system is designed, in which the upper layer includes two algorithms: (1) PID feedback control based on preview deviation; (2) PID feedforward-feedback control based on the curvature of the path planning by skilled drivers. While the extension goodness evaluation is introduced in the lower layer which is used to evaluate two controllers in upper layer. According to the real-time state of vehicle-road system, the controller output with higher goodness is selected. Finally, the simulation results verify that comparing with the conventional single PID feedback control, the extension goodness evaluation control can effectively improve the passenger comfort while ensuring tracking accuracy.

Continuous non‐singular terminal sliding‐mode control of permanent magnet spherical actuator for trajectory tracking based on a modified nonlinear disturbance observer

Multi-degree-of-freedom permanent magnet spherical actuators (PMSAs) are highly nonlinear, coupled, and multivariable systems that are subject to different types of disturbances including unknown payloads, unmodelled dynamics, frictions, and other external disturbances, which adversely affect the trajectory tracking performance of a system. In this paper, a disturbance-observer-based continuous non-singular terminal sliding-mode control (NDOB-CNTSMC) strategy is developed for a PMSA to enhance its trajectory tracking performance. Firstly, a nonlinear disturbance observer (NDOB) with a parameter design method is proposed to suppress uncertainty and reject disturbance in a PMSA trajectory tracking control system. Secondly, a continuous non-singular terminal sliding-mode controller (CNTSMC) is presented to compensate for the effect of the disturbance observation error and unobservable disturbances. The stability of the proposed control strategy is proved by the Lyapunov theorem. Both simulation and experimental results have shown that the control strategy proposed in this research can effectively address the issues of the inaccuracy of modelling and various disturbances and improve the trajectory tracking precision of the PMSA.

An LADRC Controller to Improve the Robustness of the Visual Tracking and Inertial Stabilized System in Luminance Variation Conditions

Disturbance from luminance variation in the identification of visual sensors causes instability in the control system of target tracking, which leads to field of vision (FOV) motion and even the target missing. To solve this problem, a linear active disturbance reject controller (LADRC) is adopted to the visual tracking and inertial stable platform (VTISP) for the first time to improve the system’s robustness. As a result, the random disturbance from identification can be smoothed by the tracking differentiator (TD).An improved linear extended state observer (LESO) modified by the TD is provided to obtain the high-order state variables for feedback. That makes the system avoid noise in a differential process from the MEMS gyroscope and enhances the response time and stability in tracking control. Finally, simulation and experimental studies are conducted, and the feasibility of the LADRC is verified. Moreover, compared with the other controller in the VTISP for remote sensing, the superiority of the LADRC in system response time and stability is proved by the experiments.

Engineering Design and Path Planning Implementation for Autonomous Rover

The objective of the study is to provide undergraduate students an opportunity to conduct research and engineering design in the area of autonomous robotics. The engineering design aspect of the project involved the reengineering of a powered wheelchair and the design of electrical, electronic, and software interfaces. Application of path planning algorithms took the form of a modified A* implementation for indoor autonomous travel, and a PixHawk autopilot system for outdoor autonomous navigation.&#x0D; The testing of the modified A* algorithm was conducted by using the hallways of Shepherd University as a real-world representation of the rover’s virtual map. The rover’s internal understanding of the map was an outline of the local space and required the robot to respond to the unknown objects in the environment to appropriately navigate from its starting point to the endpoint. The goal of the outdoor autonomous navigation was to implement GPS-based navigation in a large open area like a parking lot without any obstacles.&#x0D; The initial findings are that the engineering design for the rover is a success and that it initially shows the capability of receiving and responding to autonomous navigation cues. This project remains ongoing and will continue to be available to future computer science and engineering students at Shepherd University who are interested in autonomous robotics design and research.&#x0D; &#x0D; Funding and support for this project are provided by JLG Industries, Inc., Professor Jason Miller, and Prof. Weidong Liao of Shepherd University’s CME Department.

Event-triggered finite-time tracking control of underactuated MSVs based on neural network disturbance observer

In this work, a trajectory tracking control issue is discussed for the underactuated marine surface vessels (MSVs) with uncertain dynamic and unknown external disturbances. In the control design, the neural network approximation technology is applied to reconstruct the unknown dynamic, and a finite-time disturbance estimator is established to reconstruct the lumped uncertainties including the unknown external disturbance and approximation error online. Under the design framework of line-of-sight, a new finite-time trajectory tracking control scheme for underactuated MSVs is developed combining the finite-time control theory with the backstepping design method. The developed control scheme has the following remarkable characteristics: (1) it realizes the fast and accurate online reconstruction of lumped uncertainties; (2) it can release the requirement of online disturbance estimation technology on an accurate MSV motion model and expand the application range of online disturbance estimation technology; (3) the unnecessary wear and tear of actuators is suppressed by introducing the event-triggered control (ETC) approach. Based on the Lyapunov theory, it is proved that all signals in the closed-loop tracking control system are bounded. Finally, the effectiveness of the proposed control scheme is verified by the simulation and comparison.

Nonsingular recursive-structure sliding mode control for high-order nonlinear systems and an application in a wheeled mobile robot

This work investigates the problem of fast tracking control for a class of high-order nonlinear systems subject to the matched disturbances. More particularly, a novel practical fixed-time disturbance observer is first presented by using a smooth hyperbolic tangent function. Then, a new nonsingular recursive-structure sliding mode surface is proposed based on the terminal sliding mode surface. With the reconstructed information deriving from the designed disturbance observer, a nonsingular recursive-structure sliding mode based finite-time tracking control approach incorporating with a new adaptive law is proposed to ensure the tracking errors converge to a small region of the origin in finite time. The finite-time stability of the closed-loop tracking control system driven by the proposed control scheme is analyzed and proved utilizing Lyapunov theory. And also, the proposed generalized control approach is applied to a mobile robotic experimental platform to achieve accurate trajectory tracking on the uneven ground. Finally, the numerical simulation and comparative experiment results demonstrate the effectiveness and superiority of the proposed approach.

Event-triggered adaptive neural tracking control for MSVs under input saturation: An appoint-time approach

This paper addresses an event-triggered tracking control issue for marine surface vessels (MSVs), which suffers from uncertain dynamics, unknown external disturbances and input saturation. To facilitate the implementation of backstepping design procedure, the input saturation nonlinearity is replaced by a smooth Gaussian error function. Under the backstepping design framework, introducing a nonlinear transformation and integrating the indirect adaptive neural, single parameter learning and event-triggered control techniques, a novel event-triggered neuroadaptive appoint-time tracking control scheme is proposed. Compared with most existing results, the proposed control solution is of the following notable characteristics: (1) the convergence time and accuracy of the position and velocity errors are determined by designer offline; (2) only one unknown parameter needs to be updated, which reduces the computational burden of control system greatly; (3) it decreases mechanical wear of the MSV actuators by reducing the response frequency of actuators to the control command. The theoretical analyses validate that all signals of the closed-loop trajectory tracking control system are bounded. Simulation results illustrate the effectiveness of the developed scheme.

Tracking Control of Physical Systems with Application to a System with a DC Motor: A Bond Graph Approach

In this paper, the bond graph modeling for the control of tracking systems has been applied. The closed loop system is built by the bond graph model of the system to be controlled, an additional bond graph according to the tracking input signal, and feedback gains in the physical domain. Hence, a procedure to obtain the closed loop tracking system is proposed. The proposal of modeling and tracking control systems in this paper determines symmetries in the bond graph approach with respect to the traditional algebraic approach. The great advantage of this graphical approach is that the mathematical determination of the system model is not necessary. Moreover, the coefficients of the characteristic polynomial using unidirectional causal loops of the closed loop system modeled in bond graphs are obtained. A case of study of a DC motor connected to an electrical supply network and a mechanical load is considered. Tracking control for the step, ramp, and acceleration type input signals in a bond graph approach are applied. In order to show the effectiveness of the proposed procedure, the simulation results are shown.

Modernization of technological process in agriculture using the John Deere 730 Air Disk Drill

The authors have shared the experience of operating the John Deere 730 Air Disk Drill coupled with the John Deere 1910 Commodity Air Cart at the Scientific and educational production center ‘Integration’ (Orel, Russia). The study gives a qualitative assessment of the operation of the seeder unit taking into account field size and shape, and proposes the technology of seed sowing at the field edges. The article presents the technical characteristics of the seeder, shows the impact of seeder operating parts on the technological process, and demonstrates the results of comparative experiments of the quality of seeding down and variation curves of the distribution of intervals in terms of planting depth. The authors have noted that the evenness of seeding depth is ensured by the special characteristics of the opener with the hydraulic reloading system. It is proven that the John Deere 730 Air Disk Drill equipped with the autopilot system allows achieving productivity up to 190 hectares per day.

Networked Active Fault-Tolerant Predictive Control for Systems With Random Communication Constraints and Actuator/Sensor Faults

This brief presents an active fault-tolerant predictive control method for the output tracking of networked control systems (NCSs) subject to random communication constraints as well as actuator and sensor faults. The system state and actuator/sensor faults are simultaneously estimated via an observer and then used to perform a fault-tolerant predictive controller. Thus, both faults and the communication constraints in the backward and forward channels are actively compensated. A closed-loop stability condition is obtained, which also guarantees a zero steady-state tracking error. Moreover, the separation principle for designing the observer-based controller holds. Finally, the numerical simulation for a mass-spring-damper system is conducted to illustrate the effectiveness of the proposed method.

GEVE: A generative adversarial network for extremely dark image/video enhancement

A variety of information of the real-time scenes is carried by the images and videos. Processing these images and videos in an intelligent way helps in many domains such as computer vision, object detection, deep learning and 3D reconstruction leaving its large usage in applications such as auto pilots, augmented reality, smart vehicles, etc. The quality of image and videos plays a vital role in case of real-time systems. One such scenario is where the images are captured without sufficient illumination. Images captured in cameras where sufficient light is not present, leads to noisy and information-loss images. It is a fact that, dark images have mainly two aspects which make its study a difficult task. They are at its low dynamic range and high propensity for generating high noise levels. Hence, an approach based on deep learning based system is adopted. For this purpose, Generative Adversarial Network (GAN) based Extremely Dark Video Enhancement Network (GEVE) model is proposed. The main objective of GEVE is to team the model with low /normal- light image pairs. Thus, GAN network learns the translation from light feeble images and images captured under normal illumination and automatically translate original images taken under extremely low light conditions into images of quality. It is clearly observed that the proposed GEVE outperforms the known state-of-art techniques. We are the view that the proposed system is an ideal candidate to handle dark image/video frames.

Photoelectric Tracking Servo Control System and Its Data Mining Adoption

Photoelectric tracking control system is a system composed of photoelectric signal detection, signal processing, servo control system, and mechanical structure, which plays an important role in the field of target tracking. In the design process, the overall design scheme of the system is firstly proposed. In the photoelectric signal detection part, a conversion device for mercury thermometer in non-electric environment is designed. The temperature scale on traditional mercury thermometer is converted into light intensity by optical method, optical signal is transmitted by optical fiber, and data processing is realized by single chip microcomputer. Giving that the influence of optical path disturbance on the temperature measurement accuracy of photoelectric conversion device, the dual optical path compensation method is adopted to overcome the possible optical path disturbance in the device. At the same time, the other hardware structure of the control system is also designed, including the main control circuit and peripheral expansion module circuit. In view of PID control can’t solve the contradiction between tracking accuracy and speed, data mining technology is used to improve it. In the experiment, the photoelectric signal detection device is firstly tested. Model 202-O electric thermostat and CENTER300 thermocouple thermometer are selected. The electric thermostat is used to change the temperature, and the thermocouple thermometer records the temperature change. The results show that the device has good performance in the whole temperature measurement range, and the temperature fluctuation is between [±0.08 °C,±0.26 °C], which basically meets the accuracy requirements of the device. When the control system electrode is set at 2,000 rpm, the startup and braking operation shows that the system can enter steady state in a short time. The introduction of data mining technology can reduce the regulation time of the system and reduce the interference of overshoot to the normal operation of the system.

A combined backstepping and fractional-order PID controller to trajectory tracking of mobile robots

Trajectory tracking is a critical problem in the field of mobile robotics. In this paper, a control scheme combined with backstepping and fractional-order PID is developed for the trajectory tracking of the differential-drive mobile robot. The kinematic and dynamic models of the mobile robot are described in detail for the trajectory tracking controller design. Then, based on the model of the mobile robot, the design of the trajectory tracking control system is addressed by combining backstepping with fractional-order PID. Moreover, to obtain an optimal control system, an improved beetle swarm optimization algorithm is presented to tune the parameters of the kinematic and dynamic controllers simultaneously. Finally, several simulations are implemented to the trajectory tracking of mobile robots in the cases with and without skidding and sliding, and the results can confirm the effectiveness and superiority of the combined control scheme. Abbreviations: FOPID: fractional-order PID; FOPD: fractional-order PD; DDMR:differential-drive mobile robot; BAS: beetle antennae search; BA: beetle antennae; PSO:particle swarm optimization; BSO: beetle swarm optimization.

Model predictive trajectory tracking control of unmanned vehicles based on radial basis function neural network optimisation

To improve the accuracy of tracking unmanned vehicles on known trajectories, two optimised model predictive control (MPC) trajectory tracking control systems are designed based on the adaptive compensation and robust control of a radial basis function (RBF) neural network. Based on the traditional MPC trajectory tracking controller and the local approximation characteristics of the RBF neural network, the proposed RBF compensation–MPC control system is designed to compensate for the inaccuracy in the MPC prediction model arising from modelling errors. The results show that this method can achieve a root mean square error of less than 0.3703 m for the lateral position. Subsequently, to suppress the error generated by the RBF neural network and reduce the degree of vehicle sideslip, the error is considered to be external interference, and the anti-interference characteristic of the RBF robust control is incorporated into the RBF robust-MPC control system. Following the re-optimisation of the RBF robust control, the root mean square error of the lateral position is set within 0.2352 m. The results of a MATLAB/Carsim joint simulation show that using the RBF robust control can improve the tracking accuracy of the traditional MPC controller compared with RBF compensation control, while simultaneously improving the driving stability of the vehicle.

Distributed secondary voltage control of microgrids with actuators bias faults and directed communication topologies: Event‐triggered approaches

AbstractMicrogrid (MG) is an effective means to achieve flexible management and control of distributed power sources. Compared with the traditional centralized secondary control method, the distributed coordinated secondary control strategy has higher flexibility and reliability. Nevertheless, few fault‐tolerant distributed secondary voltage control strategies for isolated MGs considered event‐triggered control mechanisms. This article proposes two event‐triggered fault‐tolerant control (ETFTC) algorithms for voltage restoration in MGs. The algorithms consider the presence of bias faults in the actuators of the distributed generators (DGs). First, an ETFTC algorithm is proposed considering the case where the communication graph is known. The algorithm eliminates the continuous communication constraint with neighboring DGs based on avoiding the constant updating of the controllers. Next, an adaptive ETFTC algorithm that is independent of the global information of the network graph and fault factor boundaries is proposed for the case where the directed topology is unknown. Furthermore, the algorithms can be applied to general linear multi‐agent systems (MASs) tracking control systems. In the end, to evaluate the performance of the proposed distributed ETFTC algorithms, a simulation study was conducted in the MATLAB/Simulink environment.