Trajectory Tracking Capability Research Articles

Ultrathin Ga2O3 Photodetector with Fast Response and Trajectory Tracking Capability Fabricated by Liquid Metal Oxidation.

Low-dimensional Ga2O3 demonstrates a unique ultraviolet photoresponse and could be used in various electronic and optical systems. However, the low-dimensional Ga2O3 photodetector is faced with the challenges of a complex preparation process and poor device performance. In this work, ultrathin Ga2O3 layers with ∼7 nm thickness are prepared on quartz rods by UV exposure to liquid gallium. Benefiting from low-density oxygen vacancy defects cured by UV exposure, the low-dimensional Ga2O3 photodetector exhibits a high response speed (rise: 64.7 μs; fall: 51.4 μs) and an exceptional linear dynamic range of 120 dB. Furthermore, the photodetector array based on these ultrathin Ga2O3 shows an effective trajectory tracking capability by monitoring UV source motion. This work develops a simple preparation method to construct a low-dimensional UV photodetector array with fast response and useful trajectory tracking capability, exhibiting the significance of ultrathin Ga2O3 in UV optoelectronics.

An Advanced Control Method for Aircraft Carrier Landing of UAV Based on CAPF–NMPC

This paper investigates a carrier landing controller for unmanned aerial vehicles (UAVs), and a nonlinear model predictive control (NMPC) approach is proposed considering a precise motion control required under dynamic landing platform and environment disturbances. The NMPC controller adopts constraint aware particle filtering (CAPF) to predict deck positions for disturbance compensation and to solve the nonlinear optimization problem, based on a model establishment of carrier motion and wind field. CAPF leverages Monte Carlo sampling to optimally estimate control variables for improved optimization, while utilizing constraint barrier functions to keep particles within a feasible domain. The controller considers constraints such as fuel optimization, control saturation, and flight safety to achieve trajectory control. The advanced control method enhances the solution, estimating optimal control sequences of UAV and forecasting deck positions within a moving visual field, with effective trajectory tracing and higher control accuracy than traditional methods, while significantly reducing single-step computation time. The simulation is carried out using UAV “Silver Fox”, considering several scenarios of different wind scales compared with traditional CAPF–NMPC and the nlmpc method. The results show that the proposed NMPC approach can effectively reduce control chattering, with a landing error in rough marine environments of around 0.08 m, and demonstrate improvements in trajectory tracking capability, constraint performance and computational efficiency.

Design and control of the Six-DOF robotic manipulator

This paper presents the building process and simulation of a robotic manipulator. Robotic manipulators are used in various scenarios, such as industry and agriculture. It would be of particular interest to learn how to systematically design and control a manipulator to follow a planned trajectory. Hence, a CAD model is developed in this paper. There is a rough finite element analysis for two cases in this study, which could easily find out how the product reacts to real-world forces and vibration. In order to solve real-world problems safely and effectively, this effort intends to build the mechanical design of the manipulator by SOLIDWORKS and simulate trajectory tracking capability using MATLAB.

Model-based trajectory tracking of a compliant continuum robot.

Introduction: Compliant mechanisms, especially continuum robots, are becoming integral to advancements in minimally invasive surgery due to their ability to autonomously navigate natural pathways, significantly reducing collision severity. A major challenge lies in developing an effective control strategy to accurately reflect their behavior for enhanced operational precision. Methods: This study examines the trajectory tracking capabilities of a tendon-driven continuum robot at its tip. We introduce a novel feedforward control methodology that leverages a mathematical model based on Cosserat rod theory. To mitigate the computational challenges inherent in such models, we implement an implicit time discretization strategy. This approach simplifies the governing equations into space-domain ordinary differential equations, facilitating real-time computational efficiency. The control strategy is devised to enable the robot tip to follow a dynamically prescribed trajectory in two dimensions. Results: The efficacy of the proposed control method was validated through experimental tests on six different demand trajectories, with a motion capture system employed to assess positional accuracy. The findings indicate that the robot can track trajectories with an accuracy within 9.5%, showcasing consistent repeatability across different runs. Discussion: The results from this study mark a significant step towards establishing an efficient and precise control methodology for compliant continuum robots. The demonstrated accuracy and repeatability of the control approach significantly enhance the potential of these robots in minimally invasive surgical applications, paving the way for further research and development in this field.

Prediction Horizon-Varying Model Predictive Control (MPC) for Autonomous Vehicle Control

The prediction horizon is a key parameter in model predictive control (MPC), which is related to the effectiveness and stability of model predictive control. In vehicle control, the selection of a prediction horizon is influenced by factors such as speed, path curvature, and target point density. To accommodate varying conditions such as road curvature and vehicle speed, we proposed a control strategy using the proximal policy optimization (PPO) algorithm to adjust the prediction horizon, enabling MPC to achieve optimal performance, and called it PPO-MPC. We established a state space related to the path information and vehicle state, regarded the prediction horizon as actions, and designed a reward function to optimize the policy and value function. We conducted simulation verifications at various speeds and compared them with an MPC with fixed prediction horizons. The simulation demonstrates that the PPO-MPC proposed in this article exhibits strong adaptability and trajectory tracking capability.

Indirect Adaptive Control Using Neural Network and Discrete Extended Kalman Filter for Wheeled Mobile Robot

This paper presents a novel approach to address the challenges associated with the trajectory tracking control of wheeled mobile robots (WMRs). The proposed control approach is based on an indirect adaptive control PID using a neural network and discrete extended Kalman filter (IAPIDNN-DEKF). The proposed IAPIDNN-DEKF scheme uses the NN to identify the system Jacobian, which is used for tuning the PID gains using the stochastic gradient descent algorithm (SGD). The DEKF is proposed for state estimation (localization), and the NN adaptation improves the tracking error performance. By augmenting the state vector, the NN captures higher-order dynamics, enabling more accurate estimations, which improves trajectory tracking. Simulation studies in which a WMR is used in different scenarios are conducted to evaluate the effectiveness of the IAPIDNN-DEKF control. In order to demonstrate the effectiveness of the IAPIDNN-DEKF control, its performance is compared with direct adaptive NN (DA-NN) control, backstepping control (BSC) and an adaptive PID. On lemniscate, IAPIDNN-DEKF achieves RMSE values of 0.078769, 0.12086 and 0.1672. On sinusoidal trajectories, the method yields RMSE values of 0.01233, 0.015138 and 0.088707, and on sinusoidal with perturbation, RMSE values are 0.021495, 0.016504 and 0.090142 in x, y and θ, respectively. These results demonstrate the superior performance of IAPIDNN-DEKF for achieving accurate control and state estimation. The proposed IAPIDNN-DEKF offers advantages in terms of accurate estimation, adaptability to dynamic environments and computational efficiency. This research contributes to the advancement of robust control techniques for WMRs and showcases the potential of IAPIDNN-DEKF to enhance trajectory tracking and state estimation capabilities in real-world applications.

Development of a Large-Range XY-Compliant Micropositioning Stage with Laser-Based Sensing and Active Disturbance Rejection Control.

This paper presents a novel design and control strategies for a parallel two degrees-of-freedom (DOF) flexure-based micropositioning stage for large-range manipulation applications. The motion-guiding beam utilizes a compound hybrid compliant prismatic joint (CHCPJ) composed of corrugated and leaf flexures, ensuring increased compliance in primary directions and optimal stress distribution with minimal longitudinal length. Additionally, a four-beam parallelogram compliant prismatic joint (4BPCPJ) is used to improve the motion decoupling performance by increasing the off-axis to primary stiffness ratio. The mechanism's output compliance and dynamic characteristics are analyzed using the compliance matrix method and Lagrange approach, respectively. The accuracy of the analysis is verified through finite element analysis (FEA) simulation. In order to examine the mechanism performance, a laser interferometer-based experimental setup is established. In addition, a linear active disturbance rejection control (LADRC) is developed to enhance the motion quality. Experimental results illustrate that the mechanism has the capability to provide a range of 2.5 mm and a resolution of 0.4 μm in both the X and Y axes. Furthermore, the developed stage has improved trajectory tracking and disturbance rejection capabilities.

Inverter PQ Control With Trajectory Tracking Capability for Microgrids Based on Physics-Informed Reinforcement Learning

The increasing penetration of inverter-based resources (IBRs) calls for an advanced active and reactive power (PQ) control strategy in microgrids. To enhance the controllability and flexibility of the IBRs, this paper proposed an adaptive PQ control method with trajectory tracking capability, combining model-based analysis, physics-informed reinforcement learning (RL), and power hardware-in-the-loop (HIL) experiments. First, model-based analysis proves that there exists an adaptive proportional-integral controller with time-varying gains that can ensure any exponential PQ output trajectory of IBRs. These gains consist of a constant factor and an exponentially decaying factor, which are then obtained using a model-free deep reinforcement learning approach known as the twin delayed deeper deterministic policy gradient. With the model-based derivation, the learning space of the RL agent is narrowed down from a function space to a real space, which reduces the training complexity significantly. Finally, the proposed method is verified through numerical simulation in MATLAB-Simulink and power HIL experiments in the CURENT center.With the physics-informed learning method, exponential response time constants can be freely assigned to IBRs, and they can follow any predefined trajectory without complicated gain tuning.

Trajectory Tracking Control of a Mobile Robot using Neural Networks

This paper presents a novel soft computing-based machine learning technique designed to enhance the trajectory tracking capabilities of mobile robots through the application of neural networks. The goal of this approach is to enhance the accuracy and overall performance of trajectory tracking without the need for manual gain recalibration, which is a tedious and time-consuming task for the designer when setting up the robot. This improvement is achieved by creating a kinematic controller based on neural networks, which are constructed using the kinematic model of the robot. In the initial phase, the controller requires gains defined by the designer. Subsequently, during the application phase, the backpropagation algorithm is used to dynamically adjust the gains of the neural network, aiming to minimize the closed-loop error. One of the key innovations introduced by this controller is the potential for automatic online gain tuning, thereby eliminating the need for a pre-learning phase, typically required by traditional neural controllers. To validate the effectiveness of this approach, the results are systematically analyzed and compared against those obtained using a conventional kinematic controller. Performance metrics reveal the improved precision in trajectory tracking achieved by the controller, with reduced effort, highlighting the performance enhancements in different trajectories. Doi: 10.28991/ESJ-2023-07-06-01 Full Text: PDF

A micro scratching platform with cross-scale plane trajectory tracking capability

A micro scratching platform with cross-scale plane trajectory tracking capability

Fractional order sliding mode control for an omni-directional mobile robot based on self-organizing interval type-2 fuzzy neural network

Four-wheeled omnidirectional mobile robots (FM-OMR) are widely used in various fields due to their excellent steering ability and adaptability. As application scenarios expand, higher requirements are placed on their trajectory tracking capabilities. FM-OMR is highly complex and difficult to model accurately, and the system contains uncertainties. Therefore, achieving accurate trajectory tracking in the presence of system uncertainty is a pressing problem. Traditional control methods struggle to accurately estimate uncertainty, and existing controllers often rely on model parameters, resulting in poor transferability. To address this issue, this paper proposes a new control method based on an online self-organizing interval type II fuzzy neural network (SOIT2FNNFSMC). This method employs an online self-organizing algorithm to dynamically compensate for uncertainty, providing greater flexibility and accuracy compared to traditional approximation networks in terms of network construction and approximation performance. A new combined reaching law is proposed based on the characteristics of traditional reaching laws, resulting in improved chattering reduction. Additionally, fractional calculus is introduced to enhance the dynamic characteristics of the controller. Simulation and experimental results demonstrate the effectiveness of this method in achieving precise trajectory tracking while handling uncertainty and not relying on model parameter features. The self-organizing algorithm proposed in this method also provides a reference for the application of SOIT2FNN in FM-OMR.

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.

A yaw stability-guaranteed hierarchical coordination control strategy for four-wheel drive electric vehicles using an unscented Kalman filter

A novel hierarchical coordination control strategy (HCCS) is offered to guarantee the stability of four-wheel drive electric vehicles (4WD-EVs) combining the Unscented Kalman filter (UKF). First, a dynamics model of the 4WD-EVs is established, and the UKF-based estimator of sideslip angle and yaw rate is constructed concurrently. Second, the equivalent cornering stiffness coefficients are jointly estimated to consider the impact of vehicle uncertainty parameters on the estimator design. Afterwards, a HCCS with two-level controller is presented. The sideslip angle and yaw rate are controlled by an adaptive backstepping-based yaw moment controller, and the computational burden is relieved by an improved adaptive neural dynamic surface control technology in the upper-level controller. Simultaneously, the optimal torque distribution controller of hub motors is developed to optimize the adhesion utilization ratio of tire in the lower-level controller. Finally, the proposed HCCS has shown effective improvement in the trajectory tracking capability and yaw stability of the 4WD-EVs under various maneuver conditions compared with the traditional Luenberger observer-based and the federal-cubature Kalman filter-based hierarchical controller.

Robust Nonlinear Predictive Control Applied to Induction Motors

In this paper, a nonlinear predictive controller is proposed for a variable-speed induction motor. The research work is directed towards improving the trajectory tracking capability, stability guarantee, robustness to parameter variations, and disturbance rejection. The Generalized Predictive Control (GPC) without constraint and output-constrained controller to induction motor drive is illustrated. The variables to be controlled are rotor speed and flux trajectory. The load torque is considered an unknown disturbance. Finally, the tuning parameter of GPC is automatically determined. The simulation results show a good performance for the nonlinear dynamic system.

Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach

To enable mobile robots to effectively complete path planning in dynamic environments, a hybrid path planning method based on particle swarm optimization (PSO) and dynamic window approach (DWA) is proposed in this paper. First, an improved particle swarm optimization (IPSO) is proposed to enhance the exploration capability and search accuracy of the algorithm by improving the velocity update method and inertia weight. Secondly, a particle initialization strategy is used to increase population diversity, and an addressing local optimum strategy is used to make the algorithm overcome the local optimum. Thirdly, a method of selecting navigation points is proposed to guide local path planning. The robot selects the appropriate navigation points as the target points for local path planning based on the position of the robot and the risk of collision with dynamic obstacles. Finally, an improved dynamic window approach (IDWA) is proposed by combining the velocity obstacle (VO) with the DWA, and the evaluation function of the DWA is improved to enhance trajectory tracking and dynamic obstacle avoidance capabilities. The simulation and experimental results show that IPSO has greater exploration capability and search accuracy; IDWA is more effective in trajectory tracking and dynamic obstacle avoidance; and the hybrid algorithm enables the robot to efficiently complete path planning in dynamic environments.

Clothoid-Based Path Planning for a Formation of Fixed-Wing UAVs

Unmanned aerial vehicles (UAVs) are playing an increasingly crucial role in many applications such as search and rescue, delivery services, and military operations. However, one of the significant challenges in this area is to plan efficient and safe trajectories for UAV formations. This paper presents an optimization procedure for trajectory planning for fixed-wing UAV formations using graph theory and clothoid curves. The proposed planning strategy consists of two main steps. Firstly, the geometric optimization of paths is carried out using graphs for each UAV, providing piece-wise linear paths whose smooth connections are made with clothoids. Secondly, the geometric paths are transformed into time-dependent trajectories, optimizing the assigned aircraft speeds to avoid collisions by solving a mixed-integer optimal control problem for each UAV of the flight formation. The proposed method is effective in achieving suboptimal paths while ensuring collision avoidance between aircraft. A sensitivity analysis of the main parameters of the algorithm was conducted in ideal conditions, highlighting the possibility of decreasing the length of the optimal path by about 4.19%, increasing the number of points used in the discretization and showing a maximum path length reduction of about 10% compared with the average solution obtained with a similar algorithm using a graph based on random directions. Furthermore, the use of clothoids, whose parameters depend on the UAV performance constraints, provides smoother connections, giving a significant improvement over traditional straight-line or circular trajectories in terms of flight dynamics compliance and trajectory tracking capabilities. The method can be applied to various UAV formation scenarios, making it a versatile and practical tool for mission planning.

Development of a Nonlinear Model Predictive Control-Based Nonlinear Three-Mode Controller for a Nonlinear System.

This paper presents the novelty on a nonlinear proportional integral derivative (NPID) controller developed from the gain values obtained using the Lyapunov-based nonlinear model predictive controller (LyNMPC). The tuning parameters of the proposed controller are taken from the dynamics of the nonlinear system, and these parmeters are dynamic with their value varying according to the error in the system. In this article, the authors have considered two highly nonlinear systems, namely, batch polymerization reactor and quadrotor unmanned aerial vehicle systems. The nonlinear mathematical modeling of the batch reactor as well as the quadrotor system considered from the past literature of authors. The acrylamide polymerization reaction under consideration is an exothermic reaction, thereby making the temperature profile tracking and control a challenging task. The primary aim of this article is to develop the NPID controller based on the LyNMPC algorithm and to validate the NPID on a batch reactor bench-scale plant and on an hardware-in-the-loop platform for the quadrotor hardware. A comparative study of trajectory tracking and control capabilities of LyNMPC on derived non-linear models of the batch reactor and quadrotor system is presented. The system mathematical models are obtained with the help of the first-principle energy balance equation for the batch reactor and with the nonlinear dynamics of the quadrotor which is derived based on Newton-Euler formulations. With LyNMPC, the stability of the nonlinear systems can be improved because the error sensitivity is considered in the cost function.

Fault-tolerant human-machine shared scheme for collision avoidance of intelligent vehicles considering driver error and actuator fault

This paper presents a fault-tolerant human-machine shared scheme for collision avoidance considering driver error and actuator fault. A fault-tolerant method of trajectory planning and decision-making is developed for dealing with the actuator fault. We calculate the remainder trajectory tracking capability of the faulted actuator and utilise the motion redundancy in the system to generate feasible fault-tolerant trajectories for collision avoidance. In addition, an assessment criterion of driver error is conducted to supervise the driver's steering response to the potential collision and actuator fault. The shared system dynamically adjusts the driving authority based on the driver error. A model predictive control (MPC) algorithm is employed for tracking the fault-tolerant trajectory. Simulation and hardware-in-the-loop (HIL) experiment results demonstrate the effectiveness of the presented approach, reducing traffic accidents due to driver error and actuator fault.

Trajectory control of a laparoscopic 3-PUU parallel manipulator based on neural network in SIMSCAPE SIMULINK environment

This paper presents the modeling and control process of recently developed manipulator with 3 limbs having prismatic-universal-universal (3-PUU) joints. It has 3 degrees of freedom (3-DOF), consisting of 2 rotational DOFs and 1 translational DOF (2R1T). To avoid the computational complexity of solving the manipulator’s kinematics in real-time application, two artificial neural networks (ANNs) are trained to estimate the forward and inverse kinematics solutions. Training and testing results show that the developed ANNs have great prediction capabilities. The manipulator’s dynamic model is deduced using MATLAB Simscape environment. Control schemes are investigated starting with motor control, first using PID controller, and then adding feed-forward control which greatly improved the motors’ response. Closed-loop trajectory control based on Cartesian space feedback of the manipulators’ position and orientation and inverse kinematics ANN is then studied. The closed-loop control scheme can enhance the system’s performance, eliminating the error resulting from any change in the manipulator’s actual model due to manufacturing or assembly defects. Simulation results of a defected manipulator model show that the closed-loop control scheme improved the manipulator’s trajectory tracking capability, reducing the z-axis position error by 89.23% and the orientation error by 86.76% and 82.83% about x-axis and y-axis directions respectively.

Model Predictive Longitudinal Motion Control for the Unmanned Ground Vehicle With a Trajectory Tracking Model

Longitudinal motion controllers based on over- simplified models result in steady-state errors, oscillations, and overshoots of the velocity, all of which impair the unmanned ground vehicle (UGV) multiple objectives (trajectory tracking capability, energy economy, and ride comfort). While it is challenging for complicated methods to accomplish real-time control in the vehicle-mounted electronic control unit (ECU), which meets harsh working conditions but has limited computing power. This paper proposes a multi-objective model predictive longitudinal motion control approach for an ECU based on a trajectory tracking model. For the trajectory tracking model, we establish an internal powertrain/brake dynamics model and divide the external resistance into three components. They are identified using a variety of practical and straightforward methods. The control strategy is composed of a high-level multi-objective model predictive controller (MPC) that obtains the optimal internal acceleration and a low-level controller that calculates control inputs based on the powertrain/brake model. To implement real-time control on the ECU, the MPC optimization problem and its solver that combines offline and online calculation are elaborately designed. Finally, the method is validated through simulations in three typical driving scenarios and vehicle tests on a hybrid-electric sport utility vehicle (SUV) and a 6-speed dual- clutch transmission (DCT) fuel sedan. Simulations and experiments demonstrate that the proposed approach is superior to the other four commonly used methodologies in terms of achieving multiple objectives.