Autopilot Research Articles

Model-free intelligent critic design with error analysis for neural tracking control

The core of the optimal tracking control problem for nonlinear systems is how to ensure that the controlled system tracks the desired trajectory. The utility functions in previous studies have different properties which affect the final tracking effect of the intelligent critic algorithm. In this paper, we introduce a novel utility function and propose a Q-function based policy iteration algorithm to eliminate the final tracking error. In addition, neural networks are used as function approximator to approximate the performance index and control policy. Considering the impact of the approximation error on the tracking performance, an approximation error bound for each iteration of the novel Q-function is established. Under the given conditions, the approximation Q-function converges to the finite neighborhood of the optimal value. Moreover, it is proved that weight estimation errors of neural networks are uniformly ultimately bounded. Finally, the effectiveness of the algorithm is verified by the simulation example.

Adaptive multi-mode control system for stability and tracking control of distributed drive automated electric vehicle

Conventional tracking control algorithms of Distributed Drive Automated Electric Vehicles (DDAEVs) are typically designed for a unique control mode, which may lead to poor lateral stability under various driving conditions. Aimed this problem, an Adaptive Multi-mode Control System (AMCS) is proposed. Firstly, according to the adhesion utilization between tires and road surface, the driving condition is classified into three categories, and three control modes corresponding to them are proposed. Secondly, an Adaptive Neuro-fuzzy Inference-based Mode Matching Control System (ANFI-MMCS) is constructed to calculate the matching degree between vehicle states and control modes. Then, in order to achieve the ideal distribution of road wheel angle and yaw moment while avoiding approaching the limit speed corresponding to the reference trajectory, a model predictive control trajectory tracking controller and a speed controller with limitations were built. Finally, to achieve smooth switching between control modes, the outputs of AMCS are weighted according to the matching degree. In the experimental section, AMCS is evaluated on Carsim-Simulink co-simulation platform, and the results under different tests reveal that the proposed controller is more adaptable.

Motion planning and control of an autonomous mobile robot

The application of autonomous mobile robots (AMRs) has gradually become crucial in smart factories due to the advantages of improving production efficiency and reducing labour costs. Motion planning has been a key part of AMR control development. This paper presents motion planning and position tracking control systems of an omnidirectional wheel AMR powered by a hybrid fuel cell and battery power source. First, the kinematical and dynamic models of the AMR are introduced. The navigation system comprises three loops, with the first loop being motor control, the second loop being position tracking control, and a motion planning layer. The position data of the AMR for feedback control is obtained through sensor fusion of data from the inertial measurement unit (IMU) sensor, encoder sensor, and ranging sensor with simultaneous localisation and mapping (SLAM) algorithm. The motion planning is then applied to obtain an optimal path with the shortest distance and collision-free movement. In addition, the tracking algorithm is designed to drive the AMR to follow the optimal path and achieve high accuracy. The experimental results show a 30% improvement in tracking accuracy compared to traditional approaches and 8 hours of continuous working, which is promising for industrial applications, and the results are satisfactory in terms of both accuracy and efficiency requirements.

Switching off automatic pilot to promote wellbeing and performance in the workplace: the role of mindfulness and basic psychological needs satisfaction.

Building on self-determination theory, this study aims to advance the happy-productive worker thesis by examining a sequential mediation linking trait mindfulness to task performance through basic psychological need satisfaction and psychological wellbeing at work. Whereas most of the papers published on the topic stem from USA and Europe, we tested our model in a Canadian sample of 283 French-speaking workers. Based on structural equation modeling, results show that the three need satisfactions at work mediate the relationship between trait mindfulness and psychological wellbeing at work. Rather than observing a sequential mediation, we find an indirect effect of trait mindfulness on task performance through the satisfaction for one of the basic psychological need (i.e., competence). The present research goes beyond previous studies by exploring a new pair of happy construct-productive criteria alongside an emergent intrapersonal factor contributing to this relationship.

Enhanced stability of aerial refueling process of UAV based on fuzzy PID

This essay aims to study UAV(Unmanned Aerial Vehicle) aerial refueling technology, and by understanding the control analysis of aerial UAV refueling by comparing multiple R&D(Research And Development) teams, a more practical and feasible method is obtained, that is, through the discussion and design of UAV motion modeling, UAV trajectory tracking control system, fuzzy PID(proportion integral derivative) controller design, etc., the purpose of UAV aerial refueling is finally achieved, and the time of UAV is increased to enhance the stability and durability of UAV in air operation.

Event-Based Wireless Tracking Control for a Wheeled Mobile Robot Against Reactive Jamming Attacks

In this paper, an event-based defense strategy is studied for a wireless remote tracking control system of a wheeled mobile robot suffering from reactive jamming attacks. First, utility functions of both the reactive jammer and control center are established based on the signal to interference plus noise ratio and residual energies. Then, interactions between the reactive jammer and control center are described through a Stackelberg game scheme, for which the existence and uniqueness of the game equilibrium solution are guaranteed. To defend this type of attacks, an event-driven communication scheme is proposed to save transmission energies at the control center. By making full use of the saved energies, a retransmission mechanism is employed to improve the reliability of the communication. Meanwhile, the unavailable data are estimated for both the robot and control center by proposing a model-based estimation method. With the combination of the event-driven communication scheme, the retransmission mechanism and the estimation method, an event-based defense strategy is established. It can be shown that the states of tracking error system under the reactive jamming attacks is guaranteed to be stochastically ultimately bounded (SUB). Finally, the efficiency of the proposed scheme is verified by simulation results.

Practical fixed-time output feedback trajectory tracking control for marine surface vessels with unknown disturbances

This paper proposes a fixed-time output feedback trajectory tracking control scheme based on an extended state observer for marine surface vehicles (MSVs) with uncertain dynamics and unknown disturbances. The scheme utilizes a high-gain fixed-time extended state observer (HGFxESO) to estimate the reconstructed unmeasured velocities and lumped disturbances. The upper bound of the settling time of the HGFxESO is only determined by the design parameters, and the estimation errors can converge to a bounded set within a fixed time. Moreover, a novel fixed-time control law is proposed based on the backstepping technique and “adding a power integrator” (API) technique. The designed continuous control law ensures the trajectory tracking errors of MSVs converge to a neighborhood around the origin within a fixed time. The fixed-time stability of the MSVs trajectory tracking control system is proved using Lyapunov theory. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control scheme.

Adaptive-Dynamic-Programming-Based Robust Control for a Quadrotor UAV with External Disturbances and Parameter Uncertainties

Thiswork addresses the trajectory-tracking-control problem for a quadrotor unmanned aerial vehicle with external disturbances and parameter uncertainties. A novel adaptive-dynamic-programming-based robust control method is proposed to eliminate the effects of lumped uncertainties (including external disturbances and parameter uncertainties) and to ensure the approximate optimal control performance. Its novelty lies in that two radial basis function neural network observers with fixed-time convergence properties were first established to reconstruct the lumped uncertainties. Notably, they tune only the scalar parameters online and have low computational complexities. Subsequently, two actor–critic neural networks were designed to approximate the optimal cost functions and control policies for the nominal system. In this design, two new actor–critic neural network weight update laws are proposed to eliminate the persistent excitation condition. Then, two adaptive-dynamic-programming-based robust control laws were obtained by integrating the observer reconstruction information and the nominal control policies. The uniformly ultimately bounded stability of the closed-loop tracking control systems was ensured using the Lyapunov methodology. Finally, numerical results are shown to verify the effectiveness and superiority of the proposed control scheme.

Multimodal Global Trajectory Planner for Autonomous Underwater Vehicles

The underwater environment introduces many limitations that must be faced when designing an autonomous underwater vehicle (AUV). One of the most important issues is developing an effective vehicle movement control and mission planning system. This article presents a global trajectory planning system based on a multimodal approach. The trajectory of the vehicle’s movement has been divided into segments between introduced waypoints and calculated in parallel by advanced path planning methods: modified A* method, artificial potential field (APF), genetic algorithm (GA), particle swarm optimisation (PSO), and rapidly-exploring random tree (RRT). The shortest paths in each planned segment are selected and combined to give the resulting trajectory. A comparison of the results obtained by the proposed approach with the path calculated by each method individually confirms the increase in the system’s effectiveness by ensuring a shorter trajectory and improving the system’s reliability. Expressing the final trajectory in the form of geographical coordinates with a specific arrival time allows the implementation of calculation results in mission planning for autonomous underwater vehicles used commercially and in the military, as well as for autonomous surface vehicles (ASVs) equipped with trajectory tracking control systems.

A light-weighted convolutional neural network for image classification in autopilot system

Autopilot has been a heated technology in recent days, and it is used in plenty of car brands. People are becoming dependent on autopilot technology. However, before this technology becomes mature, there are still a lot of insufficiencies that need to improve. The focus of this essay is on the object detection shortage that exists in the autopilot system. This essay is first going to introduce a lite CNN-based method to improve the object detection and identification module. In the result part of the essay, the essay is going to show the result of the training and some new prediction function will be implemented. Also, some accuracy improving methods including data augmentation and Dropout will be introduced, and the effect of the improving methods will be shown in the essay. After training, the autopilot system is going to distinguish pedestrians from vehicles. Although the road condition is far more complex than just vehicles and pedestrians, for convenience this essay is only going to implement the two datasets. Also, some further improvement intension will be introduced.

Maximum power tracking strategy of a wind energy conversion system based on resonant with active disturbance rejection control

PurposeThis study aims to construct a novel maximum power tracking control system for the direct drive permanent magnet synchronous generator (PMSG) of the wind energy conversion system (WECS) to solve the following problems: how to effectively eliminate the system’s model parameter disturbances and speed up the dynamic performance of the system; and how to eliminate harmonics in WECS under different wind speeds.Design/methodology/approachTo obtain the maximum output power of PMSG at WECS under different wind speeds, the following issues should be considered: (1) how to effectively eliminate the system’s model parameter disturbances and speed up the dynamic performance of the system; and (2) how to suppress system harmonics. For Problem 1, adding d–q compensation factors to active disturbance rejection control (ADRC) for the current loop realizes the d–q axis decoupling control, which speeds up the dynamic performance of the system. For Problem 2, the resonant controller is introduced into the ADRC for the current loop to suppress harmonic current in WECS under different wind speeds.FindingsThe simulation results demonstrate that the proposed control method is simpler and more reliable than conventional controllers for maximum power tracking.Originality/valueCompared with traditional controllers, the proposed controller can speed up the dynamic performance of the system and suppress the current harmonic effectively, thus better achieving maximum power tracking.

Decentralizing the lightning network: a score-based recommendation strategy for the autopilot system

The fundamental objective of the Lightning Network is to establish a decentralized platform for scaling the Bitcoin network and facilitating high-throughput micropayments. However, this network has gradually deviated from its decentralized topology since its operational inception, and its resources have quickly shifted towards centralization. The evolution of the network and the changes in its topology have been critically reviewed and criticized due to its increasing centralization. This study delves into the network’s topology and the reasons behind its centralized evolution. We explain the incentives of various participating nodes in the network and propose a score-based strategy for the Lightning Autopilot system, which is responsible for automatically establishing new payment channels for the nodes joining the network. Our study demonstrates that utilizing the proposed strategy could significantly aid in reducing the network’s centralization. This strategy is grounded in qualitative labeling of network nodes based on topological and protocol features, followed by the creation of a scoring and recommendation model. Results of the experiments indicate that in the evolved network using the proposed strategy, concentration indicators such as the Gini coefficient can decrease by up to 17%, and channels ownership of the top 1% of hubs decrease by 27% compared to other autopilot strategies. Moreover, through simulated targeted attacks on hubs and channels, it is shown that by adopting the proposed strategy, the network’s resilience is increased compared to the existing autopilot strategies for evolved networks. The proposed method from this research can also be integrated into operational Lightning clients and potentially replace the current recommendation methods used in Lightning Autopilot.

Research on Bandwidth Improvement of Fine Tracking Control System in Space Laser Communication

Piezoelectric fast steering mirror (PZT FSM) is the core component of the fine tracking system for space laser communication, and its actuator is a piezoelectric ceramic. Consequently, there is a hysteretic nonlinear disturbance throughout the entire range of the FSM’s steering. To enhance the fine tracking system’s performance, this paper innovatively analyzes and verifies the effect of the PZT FSM hysteresis characteristics on the error suppression bandwidth of the fine tracking system. Firstly, the rate-dependent hysteresis model is established by serially connecting the Prandtl–Ishlinskii (P-I) model with the dynamic linear mode. The inverse model is designed as a feedforward controller, followed by the conduction of open-loop feedforward compensation experiments. Subsequently, we propose a compound control method based on the rate-dependent hysteresis mode and conduct a simulation analysis. Finally, the experimental platform for the fine tracking system is set up, and the optimization effect of compensating for hysteresis nonlinearity on the fine tracking system is verified. The experimental results show that the nonlinearity of the PZT FSM is improved by 30% in the middle- and high-frequency ranges, and the error suppression bandwidth of the fine tracking system is improved by 41.7%. This effectively enhances the fine tracking system’s error suppression capability.

Tuning path tracking controllers for autonomous cars using reinforcement learning.

This article proposes an adaptable path tracking control system, based on reinforcement learning (RL), for autonomous cars. A four-parameter controller shapes the behaviour of the vehicle to navigate lane changes and roundabouts. The tuning of the tracker uses an 'educated' Q-Learning algorithm to minimize the lateral and steering trajectory errors, this being a key contribution of this article. The CARLA (CAR Learning to Act) simulator was used both for training and testing. The results show the vehicle is able to adapt its behaviour to the different types of reference trajectories, navigating safely with low tracking errors. The use of a robot operating system (ROS) bridge between CARLA and the tracker (i) results in a realistic system, and (ii) simplifies the replacement of CARLA by a real vehicle, as in a hardware-in-the-loop system. Another contribution of this article is the framework for the dependability of the overall architecture based on stability results of non-smooth systems, presented at the end of this article.

Enhancing photovoltaic system modeling and control under partial and complex shading conditions using a robust hybrid DE-FFNN MPPT strategy

Improving efficiency, reliability, and extending the life of Photovoltaic (PV) systems are some essential benefits of developing an effective Maximum Power Point Tracking (MPPT) control system. In this study, an intelligent MPPT approach based on the Differential Evolution (DE) algorithm, and an adaptive Feed-Forward Neural Network (FFNN) is developed to address various issues of the most commonly used MPPT algorithms that affect the performance of PV systems including, power generation efficiency, tracking time, and instabilities around the Global Optima (GO). Consequently, in order to determine the suitable hyper-parameters’ values for the optimization and training procedure, the suggested hybrid technique is evaluated in the first part of this study for a variety of the FFNN configuration’s possibilities. Afterwards, the best obtained neural model is implemented as an MPPT controller, and a performance appraisal has been made between the DE algorithm based Feed-Forward Neural Network (DE-FFNN) and the most efficient utilized MPPT techniques. The observed results in the optimization and training phases show that our proposed strategy with 11 neurons in the hidden layer presents the most optimal neural model with a Mean Square Error (MSE) of 1.4924 in 139.8613 s in the optimization phase, and 2.57e−14 in the training phase. Subsequently, the created DE-FFNN model based-MPPT with the proposed configuration illustrates excellent performance compared to the applied MPPT techniques with a tracking speed between 0.0237 and 0.0106 s, a remarkable reduction in instability, and a power efficiency that can go up to 99.9971% in four different irradiance scenarios.

A pedestrian trajectory prediction method based on improved LSTM network

AbstractPedestrian trajectory prediction based on vision is a popular task in autopilot system. As pedestrian trajectories always cross each other, pedestrian targets will frequently obscure each other, which makes the collected pedestrian trajectories produce errors. Moreover, the interaction between pedestrians will have an impact on their trajectories in crowded areas, which leads to great challenge in trajectory prediction. In order to solve the above two problems, a pedestrian trajectory prediction method is proposed. Based on the YOLOv7 algorithm, it is connected with the StrongSORT algorithm. Then adding a feature recognition (ReID) module to the model, the problem of target switching and jumping in the process of pedestrian tracking can be solved. The dot product attention mechanism is also integrated into the long short term memory (LSTM) algorithm, the improved LSTM correlate target position and distance information for the pedestrian trajectory prediction. Extensive experiments on three most challenging datasets showed that this method improves the performance and largely reduces the model size.

Fast Trajectory Tracking Control Algorithm for Autonomous Vehicles Based on the Alternating Direction Multiplier Method (ADMM) to the Receding Optimization of Model Predictive Control (MPC).

In order to improve the real-time performance of the trajectory tracking of autonomous vehicles, this paper applies the alternating direction multiplier method (ADMM) to the receding optimization of model predictive control (MPC), which improves the computational speed of the algorithm. Based on the vehicle dynamics model, the output equation of the autonomous vehicle trajectory tracking control system is constructed, and the auxiliary variable and the dual variable are introduced. The quadratic programming problem transformed from the MPC and the vehicle dynamics constraints are rewritten into the solution of the ADMM form, and a decreasing penalty factor is used during the solution process. The simulation verification is carried out through the joint simulation platform of Simulink and Carsim. The results show that, compared with the active set method (ASM) and the interior point method (IPM), the algorithm proposed in this paper can not only improve the accuracy of trajectory tracking, but also exhibits good real-time performance in different prediction time domains and control time domains. When the prediction time domain increases, the calculation time shows no significant difference. This verifies the effectiveness of the ADMM in improving the real-time performance of MPC.

Research on automatic pilot repetition generation method based on deep reinforcement learning.

Using computers to replace pilot seats in air traffic control (ATC) simulators is an effective way to improve controller training efficiency and reduce training costs. To achieve this, we propose a deep reinforcement learning model, RoBERTa-RL (RoBERTa with Reinforcement Learning), for generating pilot repetitions. RoBERTa-RL is based on the pre-trained language model RoBERTa and is optimized through transfer learning and reinforcement learning. Transfer learning is used to address the issue of scarce data in the ATC domain, while reinforcement learning algorithms are employed to optimize the RoBERTa model and overcome the limitations in model generalization caused by transfer learning. We selected a real-world area control dataset as the target task training and testing dataset, and a tower control dataset generated based on civil aviation radio land-air communication rules as the test dataset for evaluating model generalization. In terms of the ROUGE evaluation metrics, RoBERTa-RL achieved significant results on the area control dataset with ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.9962, 0.992, and 0.996, respectively. On the tower control dataset, the scores were 0.982, 0.954, and 0.982, respectively. To overcome the limitations of ROUGE in this field, we conducted a detailed evaluation of the proposed model architecture using keyword-based evaluation criteria for the generated repetition instructions. This evaluation criterion calculates various keyword-based metrics based on the segmented results of the repetition instruction text. In the keyword-based evaluation criteria, the constructed model achieved an overall accuracy of 98.8% on the area control dataset and 81.8% on the tower control dataset. In terms of generalization, RoBERTa-RL improved accuracy by 56% compared to the model before improvement and achieved a 47.5% improvement compared to various comparative models. These results indicate that employing reinforcement learning strategies to enhance deep learning algorithms can effectively mitigate the issue of poor generalization in text generation tasks, and this approach holds promise for future application in other related domains.

Hypertracking and Hyperrejection: Control of Signals Beyond the Nyquist Frequency

This paper studies the problem of signal tracking and disturbance rejection for sampled-data control systems, where the pertinent signals can reside beyond the so-called Nyquist frequency. In light of the sampling theorem, it is generally understood that manipulating signals beyond the Nyquist frequency is either impossible or at least very difficult. On the other hand, such control objectives often arise in practice, and control of such signals is much desired. This paper examines the basic underlying assumptions in the sampling theorem and pertinent sampled-data control schemes, and shows that the limitation above can be removed by assuming a suitable analog signal generator model. Detailed analysis of multirate closed-loop systems, zeros and poles are given, which gives rise to tracking or rejection conditions. Robustness of the new scheme is fully characterized; it is shown that there is a close relationship between tracking/rejection frequencies and the delay length introduced for allowing better performance. Examples are discussed to illustrate the effectiveness of the proposed method here.

Electromechanical Devices of Adaptive and Control-Tracking Systems

In the presented work, some characteristics of devices of adaptive and control-tracking control systems are considered. The characteristic features of angle sensors, methods of converting analog signals with high accuracy (for example, sine-cosine transformers) occupies a leading position among the development and research of tracking systems. The modern electronic element base opens up new possibilities - the creation of tracking digital angle converters (TDAC) using the principles of digital tracking and adaptive control in them. Stability, efficiency, load form determines the reliability, accuracy, economy, service life of electromechanical automation devices, test equipment, etc. Determining the characteristics, establishing analytical relationships between the initial data and output parameters is one of the stages of the algorithm for solving the problems of designing equipment parameters for monitoring and tracking control systems, which in turn contributes to the development of a mathematical model from a system of equations, the joint solution of which allows you to establish analytical relationships between the initial data and settings.