Real-time Trajectory Tracking Research Articles

A Physics-Informed Neural Network Approach to Augmented Dynamics Visual Servoing of Multirotors.

This article presents a visual servoing strategy that integrates the capabilities of a physics-informed neural network (PINN) to estimate system uncertainties and inaccuracies with a dynamics-centered visual servoing technique for multirotors. The proposed method effectively combines these approaches, eliminating the need for inverse Jacobian calculations to determine multirotor motion by directly relating pixel variations to the multirotor's torque and thrust inputs, while also strengthening the method's robustness through the utilization of the PINN to model and address uncertainties in camera and multirotor parameters, as well as the modeling inaccuracies inherent in the dynamics-centered visual servoing technique. In contrast to existing state-of-the-art data-driven approaches, the proposed PINN approach requires, on average, 65% less labeled data to characterize uncertainties and inaccuracies. To ensure real-time implementation of the visual servoing model, the PINN-learned model is combined with an adaptive horizon monotonically weighted nonlinear model predictive controller (NMPC), capable of processing control efforts at rates 10 times faster than existing Tube MPC and Adaptive MPC strategies. These findings are validated through real-time trajectory tracking experiments, which not only highlight the effectiveness of the proposed approach in approximating modeling inaccuracies but also its capability in handling uncertainties upto 70% in camera parameters.

Energy Maximization Absorption of Wave Energy Converter Based on Fourier Pseudo-Spectral Method and Adaptive Dynamic Programming

In this paper, a novel noncausal control framework to address the energy maximization problem of wave energy converters (WECs) with physical constraints is proposed. The energy maximization problem of WECs is a constrained optimal control problem. The proposed control framework converts this problem into a reference trajectory-tracking problem through the Fourier pseudo-spectral method (FPSM) and utilizes the online tracking adaptive dynamic programming (OTADP) algorithm to realize real-time trajectory tracking for practical use in the ocean environment. Using the wave prediction technique, the optimal trajectory is generated online through a receding horizon (RH) implementation. A critic neural network (NN) is applied to approximate the optimal cost value function and calculate the error-tracking control by solving the associated Hamilton-Jacobi-Bellman (HJB) equation. The proposed WEC control framework improves computational efficiency and makes it feasible for the online control of the WEC in practice. Simulation results show the effects of the receding-horizon implementation of FPSM with different window lengths and window functions, while verifying the performances of tracking control and energy absorption of WECs in two different sea conditions.

Real-time control of a soft manipulator based on reduced order extended position-based dynamics

Soft robots are high-dimensional nonlinear systems coupled with both geometric and material nonlinearity. Control of such a system is complex and time-consuming. In this study, a real-time trajectory tracking control framework is established based on the reduced order extended position-based dynamics. In contrast to the common nonlinear model order reduction methods that require to collect a large number of data to create the motion subspace, this article's motion subspace is constructed based on the model configuration and material properties. The linear modes of the model and the related modal derivatives provide the reduced order matrix, which streamlines and increases the efficiency of model construction. Then, coupled with the instantaneous optimal control, a real-time reduced order model-based control framework of soft robots can be constructed. Experiments on trajectory tracking of a soft manipulator are conducted to verify the accuracy and efficiency of the proposed controller. The average error of all experiments is within 1 cm; and the single-step calculation time of the controller is about 0.057 s, which is less than the sampling period 0.1 s.

Gradient projection-based trajectory tracking control for automatic guided vehicle

In order to solve the problems of long operation time for solving the trajectory tracking controller and low real-time tracking. This paper proposes an improved model predictive control algorithm. By introducing the gradient projection method (GPM), the projection direction is used as the fastest descent direction of the objective function, which is used to reduce the number of iterations of the operation. Meanwhile, this paper adds constraints and relaxation factors to improve the trajectory tracking accuracy and prevent the controller operations from falling into local optimal solutions. Simulation by Simulink, the improved model predictive control algorithm reduces the average operation time by 24.74% and the single maximum operation time by 36.04%, effectively improving the real-time trajectory tracking performance. Finally, the verification experiment of trajectory tracking control is carried out by actual vehicle, proving the method’s effectiveness.

Research on two stage obstacle-avoidance trajectory planning and trajectory tracking control in curves

The existing obstacle-avoidance trajectory planning and trajectory tracking control algorithms have limitations such as long-time consumption, high failure rate in dynamic traffic environments, and insufficient trajectory tracking accuracy in curved roads. Based on the above problems, this paper designs a two stage obstacle-avoidance trajectory planner based on nonlinear optimization theory. In first stage Part-NLP, only considering the safety obstacle avoidance, a point mass model and linearization constraints are established to quickly solve the initial trajectory. In the second stage Full-NLP, considering smooth soft constraints comprehensively, the initial trajectory is optimized by establishing driving corridors and a lightweight iterative framework. In control module, this paper selects a linear quadratic form lateral trajectory tracking controller, and the parameters were optimized through the carnivorous plant algorithm. The joint simulation results show that in dynamic traffic environment of curved roads, the two stage planner proposed can accurately plan safe and smooth obstacle avoidance trajectories, and there is a significant reduction in time consumption compared to traditional NLP algorithms. The control strategy can accurately track the planned trajectories, with lateral error controlled within plus or minus 0.1 m, heading error controlled within plus or minus 0.15 rad, speed tracking error controlled within plus or minus 0.15 m/s, and vehicle yaw angle error controlled within plus or minus 0.04 rad; the hardware-in-loop test results indicate that the controller can achieve real-time and accurate trajectory tracking.

Event-Triggered Deep Learning Control of Quadrotors for Trajectory Tracking

This paper proposes an event-triggered deep learning control strategy to achieve real-time trajectory tracking control for quadrotors. In the training data collection phase, the event-triggered model predictive control (MPC) method is applied to the quadrotor in the simulation environment to generate training data. Then, a deep neural network (DNN) controller is trained to approximate the optimal control policy of the event-triggered MPC. To further save computing resources of on-board processor, the event-triggered mechanism is incorporated with the DNN controller, and the dual-mode approach is employed in it. Finally, simulation and experimental results show that the proposed controller can ensure almost similar trajectory tracking performance to the event-triggered MPC controller while requiring a lower control computation cost.

Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment.

In this article, we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic obstacles. We first introduce the design of the bionic leopard cabinet underwater robot developed in our lab. Then, we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical constraints. Furthermore, we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier curves. In addition, based on the improved proximal policy optimization, local dynamic obstacle avoidance trajectory replanning is carried out. In addition, we design the fuzzy proportional-integral-derivative controller for tracking control of the planned trajectory. Finally, the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of UWSim. Among them, the real-time trajectory planning method has advantages in trajectory length, trajectory smoothness, and planning time. The error of trajectory tracking control method is controlled around 0.2 m.

STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection

Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy.

Multiparameter Estimation-Based Sensorless Adaptive Direct Voltage MTPA Control for IPMSM Using Fuzzy Logic MRAS

This paper introduces a parameter-estimation-based sensorless adaptive direct voltage maximum torque per ampere (MTPA) control strategy for interior permanent magnet synchronous machines (IPMSMs). In direct voltage control, the motor’s electrical parameters, speed, and rotor position are of great significance. Thus, any mismatch in these parameters or failure to acquire accurate speed or position information leads to a significant deviation in the MTPA trajectory, causing high current consumption and hence affecting the performance of the entire control system. In view of this problem, a fuzzy logic control-based cascaded model reference adaptive system (FLC-MRAS) is introduced to mitigate the effect of parameter variation on the tracking of the MTPA trajectory and to provide precise information about the rotor speed and position. The cascaded scheme consists of two parallel FLC-MRAS for speed and multiparameter estimation. The first MRAS is utilized to estimate motor speed and rotor position to achieve robust sensorless control. However, the speed estimator is highly dependent on time-varying motor parameters. Therefore, the second MRAS is designed to identify the quadratic inductance and permanent magnet flux and continuously update both the speed estimator and control scheme with the identified values to ensure accurate speed estimation and real-time MTPA trajectory tracking. Unlike conventional MRAS, which uses linear proportional-integral controllers (PI-MRAS), an FLC is adopted to replace the PI controllers, ensuring high estimation accuracy and enhancing the robustness of the control system against sudden changes in working conditions. The effectiveness of the proposed scheme is evaluated under different speed and torque conditions. Furthermore, a comparison against the conventional PI-MRAS is extensively investigated to highlight the superiority of the proposed scheme. The evaluation results and our quantitative assessment show the ability of the designed strategy to achieve high estimation accuracy, less oscillation, and a faster convergence rate under different working conditions. The quantitative assessment reveals that the FLC-MRAS can improve the estimation accuracy of speed, permanent magnet flux, and quadratic inductance by 19%, 55.8% and 44.55%, respectively.

Implementing a Precision Pneumatic Plug Tray Seeder with High Seeding Rates for Brassicaceae Seeds via Real-Time Trajectory Tracking Control

In recent years, the aging of the rural population worldwide has become a major concern, necessitating the development of agricultural automation. Pneumatic energy has emerged as a reliable and environmentally friendly option, aiding in the global effort to reduce carbon emissions. The purpose of this study is to reduce the amount of labor required for plug tray seeding by developing an automated seeder that employs a precision pneumatic servo system via the rod-less actuator with real-time trajectory tracking capabilities. The proposed seeder has a simple structure, is easy to maintain, and saves energy. It mainly consists of a rod-less pneumatic cylinder, a needle seeding mechanism, a soil drilling mechanism and a PC-based real-time controller. Mathematical models of the developed precision pneumatic plug tray seeder are analyzed and established, and an adaptive sliding mode controller is proposed. A PC-based real-time control system is developed using MATLAB/SIMULINK via an optical encoder with a sampling frequency of 1 kHz to enable the development of precise pneumatic plug tray seeder. An optical encoder is used to measure the displacement of the rod-less cylinder which represents real-time positions of the plug tray loading platform. Experiments are conducted using Brassicaceae seeds, and the rates of single seeding, multiple seeding, missed seeding and germination are carried out through manual measurement. The results indicate that the seeder exhibits satisfactory performance, with a root mean square error of less than 0.5 mm and a single-seeding rate of more than 97%. Overall, our findings provide new insights for nurseries and could contribute to the reduction in agricultural carbon emissions.

Deep Learning-Based Trajectory Planning and Control for Autonomous Ground Vehicle Parking Maneuver

In this paper, a novel integrated real-time trajectory planning and tracking control framework capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is presented. In the motion planning component, a newly-proposed idea of utilizing deep neural networks (DNNs) for approximating optimal parking trajectories is further extended by taking advantages of a recurrent network structure. The main aim is to fully exploit the inherent relationships between different vehicle states in the training process. Furthermore, two transfer learning strategies are applied such that the developed motion planner can be adapted to suit various AGVs. In order to follow the planned maneuver trajectory, an adaptive learning tracking control algorithm is designed and served as the motion controller. By adapting the network parameters, the stability of the proposed control scheme, along with the convergence of tracking errors, can be theoretically guaranteed. In order to validate the effectiveness and emphasize key features of our proposal, a number of experimental studies and comparative analysis were executed. The obtained results reveal that the proposed strategy can enable the AGV to fulfill the parking mission with enhanced motion planning and control performance. <i>Note to Practitioners</i>&#x2014;This article was motivated by the problem of optimal automatic parking planning and tracking control for autonomous ground vehicles (AGVs) maneuvering in a restricted environment (e.g., constrained parking regions). A number of challenges may arise when dealing with this problem (e.g., the model uncertainties involved in the vehicle dynamics, system variable limits, and the presence of external disturbances). Existing approaches to address such a problem usually exploit the merit of optimization-based planning/control techniques such as model predictive control and dynamic programming in order for an optimal solution. However, two practical issues may require further considerations: 1). The nonlinear (re)optimization process tends to consume a large amount of computing power and it might not be affordable in real-time; 2). Existing motion planning and control algorithms might not be easily adapted to suit various types of AGVs. To overcome the aforementioned issues, we present an idea of utilizing the recurrent deep neural network (RDNN) for planning optimal parking maneuver trajectories and an adaptive learning NN-based (ALNN) control scheme for robust trajectory tracking. In addition, by introducing two transfer learning strategies, the proposed RDNN motion planner can be adapted to suit different AGVs. In our follow-up research, we will explore the possibility of extending the developed methodology for large-scale AGV parking systems collaboratively operating in a more complex cluttered environment.

A novel tool path planning and feedrate scheduling algorithm for point to point linear and circular motions of CNC-milling machines

The manufacturing process on CNC machines is directed by the Numerical Control (NC) files which include the main tool paths as a combination of numerous instructions such as lines and arcs specified by the G01 and G02/G03 commands. In the literature, the lines and arcs generally are converted to a parametric curve based on the global tool path smoothing or local corner smoothing algorithms. However, the smoothing methods cause the deflection from the tool path in NC-File. In cases where the deflection from the main tool path is not acceptable, point-to-point motion is necessary instead of the smooth trajectory. This study presents a novel trajectory planning and feedrate scheduling algorithm for point-to-point control of linear and circular paths in the NC-File. The proposed method prevents the feedrate fluctuations at the junction points and generates the tool path without deflection from the NC-File. Also, the feedrate scheduling algorithm decrease the operating time of the machine significantly compared to traditional method of point-to-point motion. This study is verified on an industrial 3-Axis CNC-Milling machine by the operating of the roughing milling, drilling, slotting, and face milling process. The measurements of final product and real-time trajectory tracking results show that the proposed method is effective in industrial applications of CNC-Milling machines in which point-to-point motion is necessary.

Optimally Initialized Model Reference Adaptive Controller of Wearable Lower Limb Rehabilitation Exoskeleton

A wearable lower-limb rehabilitation exoskeleton functions to fulfill the recovery process of limb functionality and assist physiotherapists. This paper presents an optimized adaptive control system for a wearable lower-limb rehabilitation exoskeleton. The tuning of the controller gains is defined as an optimization problem for a closed-loop control system of the wearable lower-limb rehabilitation robot by genetic algorithm and particle swarm optimization. We presented a novel initialized model reference adaptive controller (IMRAC) for real-time joint trajectory tracking, in which controller gains are adjusted by the gradient-based method. An experimental test of a 4-degree of freedom lower-limb rehabilitation exoskeleton was carried out to observe the closed-loop performance of IMRAC for bipedal human walking. The statistical comparison between IMRAC and MRAC shows an efficient performance and robustness of our proposed method for the joint trajectory tracking of the lower-limb rehabilitation exoskeleton in real time.

Space Noncooperative Target Trajectory Tracking Based on Maneuvering Parameter Estimation

The space noncooperative target maneuvering trajectory tracking is essential for the safety of the on-orbit spacecraft. For the noncooperative target, the maneuvering model is complex and changeable. Besides, the maneuvering dynamics model, the operation time, and the maneuvering frequency are previously unknown. It is difficult to achieve high-precision maneuvering trajectory tracking. In this paper, a novel real-time maneuvering trajectory tracking algorithm is developed, in which the maneuvering trajectory of the noncooperative target is discretized first, and then the differential algebra method is utilized to estimate the maneuvering parameter of the noncooperative target in the discretized time. Since the discretized period is very short, the maneuvering parameters of the target in the next discretized time are assumed to be the same as those in the previous discretized time, and the estimated maneuvering parameters are utilized to predict the target’s relative state in the next discretized time to achieve maneuvering trajectory tracking. Compared with the interactive multimodel method (IMM), the proposed method can estimate the maneuvering parameter of the noncooperative target in real time, which greatly reduces the tracking error caused by the mismatching of the target’s maneuvering model. In order to verify the effectiveness of the algorithm, a simulation of a noncooperative target’s maneuvering trajectory tracking is provided. The results demonstrated that the proposed method could track the noncooperative target maneuvering in real time, and the estimation accuracy was improved by about 93.07% compared with the IMM.

The Prediction of Badminton Flight Trajectory Based on an Intelligent Compensator

A real-time visual servo badminton trajectory tracking system (VSBTTS) is proposed in this study. VSBTTS is composed of two parts: the image unit and the tracking operation unit. The primary function of the image unit is to segment the moving shuttlecock from a continuous image taken by the camera and detect the moving object. The tracking operation unit is used to track parts of the shuttlecock found in the image unit. Then, the flight trajectory of the shuttlecock is estimated; however, it is difficult for tracking operation units to avoid estimation errors, preventing them from achieving full accuracy. In this study, a fuzzy compensation controller is used to compensate for the system estimation errors of the flight trajectory. Using the fuzzy compensator, the trajectory of the shuttlecock is accurately estimated. Experimental results demonstrate the usefulness of the proposed shuttlecock trajectory tracking system.

Motion control of photovoltaic module dust cleaning robotic arm based on model predictive control

Carbon neutralization has become a global consensus for green development, and solar photovoltaic power generation has become one of the key technologies for carbon reduction. The presence of dust on a photovoltaic module affects power generation, so the trajectory tracking control of dust removal robotic arm for photovoltaic modules is of great significance for improving power generation efficiency. In this study, a composite trajectory tracking strategy based on model predictive control is designed to track the desired angle of each joint, which is the control objective for the trajectory tracking of the photovoltaic module's dust cleaning robotic arm. The control strategy consists of a model predictive controller and a disturbance observer. Firstly, when there is no external disturbance acting on the system, and the robotic arm model is accurate, the trajectory tracking prediction optimization problem is constructed, and an error feedback correction mechanism is introduced so that the dust cleaning robotic arm tracks the desired trajectory asymptotically. Secondly, when there are model parameter deviations, system time variation, external disturbances, or other uncertain factors, a composite control strategy is established by combining the disturbance observer and model predictive control to compensate for the effects of disturbances through feedback, thus improving the stability and accuracy of the robotic arm control system. Finally, the feasibility of the composite control tracking strategy is verified by numerical simulation. The results show that the designed predictive controller has high error control accuracy and fast solution speed, and it can realize real-time and robust trajectory tracking of the robotic arm with a constrained dust cleaning assembly.

Real-Time Local Obstacle Avoidance and Trajectory Tracking Control of Quadrotor UAVs With Suspended Payload in Complex Environments

Real-Time Local Obstacle Avoidance and Trajectory Tracking Control of Quadrotor UAVs With Suspended Payload in Complex Environments

Three-dimensional Dirac semimetal (Cd1−xZnx)3As2/Sb2Se3 back-to-back heterojunction for fast-response broadband photodetector with ultrahigh signal-to-noise ratio

Three-dimensional (3D) Dirac semimetal materials have great application prospects in broad-spectrum photodetectors (PDs) working at wavebands up to the mid/far infrared region owing to their unique topological energy-band architectures and excellent photoelectric properties. However, the relatively high dark current in most Dirac semimetal-based PDs limits their photodetection performance, with poor signal-to-noise ratios (SNRs). Herein, we developed an ultra-low-noise-level PD linear array based on a 3D Dirac semimetal (Cd1−xZnx)3As2/Sb2Se3 back-to-back (BTB) heterojunction using a molecular beam epitaxy (MBE)-grown (Cd1−xZnx)3As2 film. Benefiting from the effective double-heterojunction design strategy, the as-fabricated (Cd1−xZnx)3As2/Sb2Se3 linear-array PD exhibited an outstanding photodetection capacity from the visible to mid-infrared region (450 nm to 4.5 µm), with the highest recorded SNR close to 104, excellent peak specific detectivity of 5.2 × 1012 Jones, and high response speed of about 87.5 µs at room temperature. Furthermore, the PD exhibited long-term stability and uniformity as only minor photocurrent fluctuations occurred among different PD linear-array units demonstrating the feasibility of the PD for advanced optoelectronic applications, such as real-time light trajectory tracking. This work provides a reference strategy for the fabrication of fast-response broadband PDs with ultrahigh SNRs using the 3D Dirac semimetal (Cd1−xZnx)3As2/Sb2Se3 BTB heterojunction and demonstrates the great prospect of 3D Dirac semimetal materials for the manufacture of fast-response uncooled focal-plane-array devices.

A Time-Division Position-Sensitive Detector Image System for High-Speed Multitarget Trajectory Tracking.

High-speed trajectory tracking with real-time processing capability is particularly important in the fields of pilotless automobiles, guidance systems, robotics, and filmmaking. The conventional optical approach to high-speed trajectory tracking involves charge coupled device (CCD) or complementary metal-oxide-semiconductor(CMOS) image sensors, which suffer from trade-offs between resolution and framerates, complexity of the system, and enormous data-analysis processes. Here, a high-speed trajectory tracking system is designed by using a time-division position-sensitive detector (TD-PSD) based on a graphene-silicon Schottky heterojunction. Benefiting from the high-speed optoelectronic response and sub-micrometer positional accuracy of the TD-PSD, multitarget real-time trajectory tracking is realized, with a maximum image output framerate of up to 62000 framesper second. Moreover, multichannel trajectory tracking and image-distortion correction functionalities are realized by TD-PSD systems through frequency-related image preprocessing, which significantly improves the capacity of real-time information processing and image quality in complicated light environments.

Adaptive Swarm Fuzzy Logic Controller of Multi-Joint Lower Limb Assistive Robot

The idea of developing a multi-joint rehabilitation robot is to satisfy the demands for recovery of lower limb functionality in hemiplegic impairments and assist the physiotherapists with their therapy plans. This work aims at to implement the Lyapunov Adaptive and Swarm-Fuzzy Logic Control (LASFC) strategy of 4-degree of freedom (4-DoF) Lower Limb Assistive Robot (LLAR) application, in which the control law is an integration of swarm-fuzzy logic control (SFLC) and Lyapunov adaptive control (LAC) with particle swarm optimization (PSO). The controller is established based on the sliding filtered steady-state error for SFLC. Its parameters are tuned by using PSO for the mathematical model of LLAR. The fuzzy defuzzification membership is set based on the tuned parameters for the real-time control system. LAC strategy is determined using stability analysis of the system to choose the controller’s parameters by observation of the system’s output and reference. The control law implemented in LLAR is the integration of SFLC and LAC to adjust the input voltage of joints. The parameters tuned by PSO are compared with the genetic algorithm (GA) statistically. In addition, the real-time trajectory tracking of the proposed controller for each joint is compared with LAC and SFLC separately. The experiment revealed that the LASFC has superior performance to the other two methods in trajectory tracking. For example, the average error for left hip by LASFC is 53.57% and 68% lower than SFLC and LAC, respectively. By the statistical analysis, it can be ascertained that the LASFC strategy performed efficiently for real-time control of the joint trajectory tracking.