Trajectory Control Research Articles

Precision trajectory control in underwater vehicle-manipulator systems using advanced sliding mode techniques and state estimation

Precision trajectory control in underwater vehicle-manipulator systems using advanced sliding mode techniques and state estimation

Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis.

Rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, we demonstrate an approach to enable real-time control of thin film synthesis by combining in situ optical diagnostics with a Bayesian state estimation method. We developed a physical model for film growth and applied the direct filter (DF) method for real-time estimation of nucleation and growth rates during pulsed laser deposition (PLD). We validated the approach using simulated and experimental reflectivity data for WSe2 growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T'-MoTe2. The DF robustly estimates growth parameters in real time at early stages of growth, down to 15% monolayer area coverage. This fusion of in situ diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states.

Research on Trajectory Planning Method Based on Bézier Curves for Dynamic Scenarios

With the increase in car ownership, traffic congestion, and frequent accidents, autonomous driving technology, especially for dynamic driving scenarios in the whole domain, has become a technological challenge for today’s researchers. Trajectory planning, as a crucial component of the autonomous driving technology framework, is gradually becoming a hot topic in intelligent research. In response to the challenges of planning lane-changing trajectories in complex dynamic driving scenarios under emergency evasive maneuvers, where it is difficult to consider surrounding vehicles and achieve dynamic adaptability, this paper proposes a dynamic adaptive trajectory planning method based on Bézier curves. Firstly, a mathematical model of Bézier curves is established and its curve characteristics are analyzed, which facilitates the correlation between the trajectory control points and the vehicle and the surrounding obstacles. Secondly, a mathematical function representing the Bézier curve is formulated, where the control points serve as the input and the lane-changing control curve as the output. Finally, the proposed method is validated through simulations on a jointly established simulation platform. The results indicate that the proposed method can plan lane-changing trajectories that are both safe and efficient under emergency evasive maneuvers, considering both static and complex dynamic conditions. This provides a novel solution for lane-changing trajectory planning in emergency evasive maneuvers for autonomous vehicles and holds significant theoretical research value.

Accurate Tracking of Agile Trajectories for a Tail-Sitter UAV Under Wind Disturbances Environments

To achieve more robust and accurate tracking control of high maneuvering trajectories for a tail-sitter fixed-wing unmanned aerial vehicle (UAV) operating within its full envelope in outdoor environments, a novel control approach is proposed. Firstly, the study rigorously demonstrates the differential flatness property of tail-sitter fixed-wing UAV dynamics using a comprehensive aerodynamics model, which incorporates wind effects without simplification. Then, utilizing the derived flatness functions and the treatments for singularity, the study presents a complete process of the differential flatness transform. This transformation maps the desired maneuver trajectory to a state-input trajectory, facilitating control design. Leveraging an existing controller from the reference literature, trajectory tracking is implemented. Subsequently, a low-cost wind estimation method operating during all flight phases is proposed to estimate the wind effects involved in the model. The wind estimation method involves generating a virtual wind measurement utilizing a low-fidelity tail-sitter model. The virtual wind measurement is integrated with real wind data obtained from the pitot tube and processed through fusion using an extended Kalman filter. Finally, the effectiveness of our methods is confirmed through comprehensive real-world experiments conducted in outdoor settings. The results demonstrate superior robustness and accuracy in controlling challenging agile maneuvering trajectories compared to the existing method. Additionally, the test results highlight the effectiveness of our method in wind estimation.

Dynamic Envelope Optimization of Articulated Vehicles Based on Multi-Axle Steering Control Strategies

Steer-by-wire technology, critical for autonomous driving, enables full-wheel steering in articulated vehicles, significantly enhancing maneuverability in complex driving environments. This study investigates dynamic envelope optimization for articulated multi-body vehicles by integrating coordinated multi-axle steering control strategies with higher-order Bezier curve designs. Unlike traditional approaches that primarily focus on single-axle steering, this research emphasizes the advantages of multi-axle steering control, which significantly reduces the dynamic envelope and enhances maneuverability. To address the challenges posed by constrained road environments, a comparative analysis of Septimic Bezier curves under various control point configurations was conducted, demonstrating their effectiveness in achieving smoother curvature transitions and steering comfort. The results highlight the pivotal role of reducing curvature peaks and increasing curvature continuity in optimizing vehicle performance. Furthermore, advanced steering control strategies, such as Articulation Angle Reference (AAR) and Dual Ackermann Steering (DAS), were shown to outperform conventional methods by ensuring precise trajectory control and improved stability. This study provides actionable insights for enhancing vehicle handling and safety in complex driving scenarios, offering a framework for future road design and multi-axle steering system development.

Nonlinear control of quadrotor trajectory with discrete H∞

Fixed-wing drones generate lift using a wing similar to a conventional airplane, in contrast to rotary helicopters. As a result, these machines use energy solely for propulsion rather than to maintain altitude, making them significantly more efficient. These devices can traverse greater distances and cover larger areas, making them capable of mapping and monitoring specific points over extended periods. This article uses an analytical nonlinear approach to look at how discrete H∞ can be used as a robust controller to manage the path of a quadrotor. The main goal is to create a discrete H∞ nonlinear output feedback algorithm that can accurately track the position of the quadrotor while staying stable, even when there are unknowns, disturbances, or noise. The discrete formulation of this algorithm makes it especially suitable for multi-engine aircraft. By designing the controller in a discrete space first, transitioning to a continuous phase, and then reverting to discrete space for real-world application, more desirable and design-aligned results can be achieved. However, transitioning from continuous to discrete controllers may sometimes cause deviations from the design specifications. Designing the controller directly in the discrete space simplifies the overall process and enhances robustness.

Model inversion for trajectory control of reconfigurable underactuated cable-driven parallel robots

Model inversion for trajectory control of reconfigurable underactuated cable-driven parallel robots

A Robotic Flutist Framework for Exploring Acoustics, Human—Instrument Interaction, and Musical Creation

Abstract This article introduces an experimental robotic setup designed to investigate the performance and sound production of flutes. The flute, renowned for its intricate mechanism enabling musicians to dynamically adjust various parameters, yields a diverse range of rich sounds. A key feature of the flute involves the embouchure—the shaping of the air channel by the musician's lips—providing control over airflow, velocity, distance to the labium, inclination, and position. The robotic testbed manipulates critical parameters of the sound-producing system, incorporating an artificial mouth and a motorized mechanism for adjusting jet length, incident angle, and offset. Specialized software aids in route planning, visualization, and description of the controllable parameter trajectories, also generating automatic positions based on a predefined dictionary. Experimental findings showcase the robot's proficiency in playing simple melodies and producing harmonically rich sounds within the specified range. Spectrogram analysis reveals successful reproduction of intended pitches and timbres. This robotic testbed emerges as a valuable tool for delving into the acoustics of the flute and the interaction between performers and flutes. It provides a robust platform for composers to create music that surpasses normal human capabilities and contributes to the comprehension and design of these intricate instruments.

3D-printed acoustic metasurface with encapsulated micro-air-bubbles for frequency-selective manipulation.

Acoustic waves provide an effective method for object manipulation in microfluidics, often requiring high-frequency ultrasound in the megahertz range when directly handling microsized objects, which can be costly. Micro-air-bubbles in water offer a solution toward low-cost technologies using low-frequency acoustic waves. Owing to their high compressibility and low elastic modulus, these bubbles can exhibit significant expansion and contraction in response to even kilohertz acoustic waves, leading to resonances with frequencies determined and tuned by air-bubble size. The resonances amplify vibrational amplitude and generate localized turbulence, enabling selective, non-invasive, and high-precision manipulation of microsized objects. However, conventional bubble formation relies on the shear force of the liquid flow and bubble surface tension, facing challenges of instability and random vibration that can impair manipulation precision and performance. To address these issues, we propose a coupled vibration structure with 3D-printed circular microsized air holes encapsulated by a PDMS film. These airholes act as artificial micro-air-bubbles, with their expansion and contraction stabilized by acoustic hard boundaries. The PDMS film further regulates vibration modes through the interaction between air movement and the film's vibration, eliminating randomness. Compared to conventional air-bubbles held by surface tension, these artificial air-bubbles are mechanically stable, allowing for enhanced gas volume changes and stronger forces for object manipulation. We experimentally confirm the stable vibration modes and their frequency-dependent behavior using laser Doppler vibrometry. Precise aggregation, rotation, and separation of micro-objects are demonstrated by adjusting the film's vibration mode. Furthermore, we propose a metasurface design featuring a multi-size microbubble array for frequency-selective manipulation, enabling flexible control of sample trajectory by changing the exciting frequency of an embedded piezoelectric transducer. Our low-frequency acoustic metasurface device offers a versatile, cost-effective solution for drug screening and automated sample handling.

Trajectory control scheme for a four-switch buck-boost converter with a capacitive power source

Trajectory control scheme for a four-switch buck-boost converter with a capacitive power source

Efficient Contact Algorithm for 3D Finite Element Modeling of Bottomhole Assemblies: Validation and Application

Summary Static analysis of the lateral deformation of a bottomhole assembly (BHA) is essential for controlling borehole trajectories in directional drilling. A major technical challenge in static BHA modeling is efficiently determining the contact configuration between the BHA and the borehole wall. This configuration, including contact locations and orientations, is not known a priori and introduces nonlinearities into the analysis. Most algorithms addressing the contact problem in BHA modeling are proprietary and lack detailed descriptions. Explicit algorithms based on the Newton-Raphson iteration method and linear/nonlinear complementarity problem formulations have limitations, such as computational inefficiency and the need for predefined contact locations. In this paper, we derive governing differential equations for 3D BHA static deformation, incorporating nonlinear effects from borehole curvature, axial forces, and both discrete and continuous contacts. The finite element method (FEM) is used to solve these equations under appropriate boundary conditions. Within the finite element framework, the Lagrange multiplier method (LMM) is used to impose displacement constraints at contact points, while an innovative iterative process ensures the unilateral nature of the contacts. The algorithm typically converges in O(10) iterations, with each iteration involving the solution of approximately O(102) finite element equations, ensuring high computational efficiency. The algorithm, grounded in principles of structural mechanics, is robust across a wide range of conditions, and its accuracy is validated against a published algorithm. The proposed BHA model is further validated using downhole measurements. In one scenario, bending moment on bit (BOB) measurements from a BHA equipped with a rotary steerable system (RSS) shows strong agreement with the model results, both in magnitude and variation pattern, when a fixed displacement boundary condition is applied at the bit. In another scenario, the BHA model is integrated with a bit-rock interface law to predict borehole propagation trajectories, demonstrating alignment with survey data when the bit side-cutting efficiency is fine-tuned. Additional analyses, including an evaluation of whirling-induced downhole tool failure, are provided in the supplementary material. These validations underscore the versatility and accuracy of the proposed model across various stages of the drilling process. Its computational efficiency makes it suitable for both offline and real-time applications, including prejob BHA designs and the development of operational parameter roadmaps for borehole trajectory control and vibration suppression, real-time integration with downhole measurements to optimize drilling performance, and post-job analysis to diagnose the root causes of undesirable drilling dysfunctions.

Sensitivity Analysis of MTPA Control to Angle Errors for Synchronous Reluctance Machines

This study investigated the sensitivity of maximum torque per ampere (MTPA) control in synchronous reluctance machines (SynRMs) to angle errors, examining specifically how deviations in the reference control trajectory affected performance. Analytical and numerical methods were employed to analyze this sensitivity systematically, including the impact of magnetic saturation. Two MTPA control implementation schemes were evaluated, with torque and current amplitude as the reference variables, using a template SynRM from the open-source simulation tool SyR-e. The results indicated that performance sensitivity to angle errors was moderately low near the MTPA trajectory, allowing for significant angle deviations with minimal performance loss. Although magnetic saturation increased this sensitivity slightly, reducing the allowable error range by up to 25%, the maximum angle deviation for up to 1% of the performance decrease still corresponded to approximately ±3∘ around the MTPA trajectory. The findings of this study suggest potential for simplifying control implementations, reducing component costs through less precise position determination (sensor-based or sensorless), and achieving additional control objectives such as torque ripple reduction.

Collision avoidance control of an automated guided vehicle based on hierarchical trajectory planning and fixed-time prescribed adaptive sliding mode

In this paper, a hybrid collision avoidance control (CAC) approach including trajectory planner and controller is proposed for an automated guided vehicle (AGV). Firstly, by combing the improved A* and timed-elastic-band method, a hierarchical trajectory planner is developed to realize the flexible collision avoidance trajectory planning of AGV. Subsequently, the trajectory controller is constructed to achieve the accurate tracking of the planned trajectory. Since the controller is developed based on a faster fixed-time stable system and a prescribed performance function, both the fixed-time and prescribed bounded convergences of the trajectory tracking system can be realized. In addition, a newly designed sliding mode filter-based nested adaptive law is adopted in the controller to update the control gain, thereby releasing the prior bound information of AGV unknown disturbance and minimizing control chattering. The global stability and prescribed convergence of the system are proved by Lyapunov theory. Finally, the effectiveness and merits of the presented CAC approach in flexible trajectory planning and accurate tracking are demonstrated by experimental validations on an AGV.

Real‐Time Energy Management Strategy for Fuel Cell/Battery Plug‐In Hybrid Electric Buses Based on Deep Reinforcement Learning and State of Charge Descent Curve Trajectory Control

Real‐Time Energy Management Strategy for Fuel Cell/Battery Plug‐In Hybrid Electric Buses Based on Deep Reinforcement Learning and State of Charge Descent Curve Trajectory Control

Control of Cellular Differentiation Trajectories for Cancer Reversion.

Cellular differentiation is controlled by intricate layers of gene regulation, involving the modulation of gene expression by various transcriptional regulators. Due to the complexity of gene regulation, identifying master regulators across the differentiation trajectory has been a longstanding challenge. To tackle this problem, a computational framework, single-cell Boolean network inference and control (BENEIN), is presented. Applying BENEIN to human large intestinal single-cell transcriptome data, MYB, HDAC2, and FOXA2 are identified as the master regulators whose inhibition induces enterocyte differentiation. It is found that simultaneous knockdown of these master regulators can revert colorectal cancer cells into normal-like enterocytes by synergistically inducing differentiation and suppressing malignancy, which is validated by in vitro and in vivo experiments.

Advanced Trajectory Planning and Control for Autonomous Vehicles with Quintic Polynomials.

This paper focuses on the design of vehicle trajectories and their control systems. A method based on quintic polynomials is utilized to develop trajectories for intelligent vehicles, ensuring the smooth continuity of the trajectory and related state curves under varying conditions. The construction of lateral and longitudinal controllers is discussed, which includes a tracking error model derived from the two-degree-of-freedom dynamic model of a two-wheeled vehicle and the application of the Frenet coordinate system transformation. The vehicle tracking performance is regulated by these controllers. Experimental verification on a small intelligent vehicle platform operating on the Ackermann steering principle was conducted. The results confirm the tracking performance of the controllers under different conditions and validate the effectiveness and feasibility of the overall framework of the study.

Research on the Optimal Control of Working Oil Pressure of DCT Clutch Based on Linear Quadratics Form

The control of the vehicle transmission system is of great significance to driving comfort. In order to design a controller for smooth shifting and comfortable driving, a dynamic model of dual-clutch transmission is established in this paper. An optimal control strategy for clutch oil pressure based on linear quadratics is proposed, which is used to optimally control the oil pressure of two clutches in the torque stage and inertia stage. The control strategy selects the slipping work and jerk as evaluation indices of shift quality and establishes an optimization objective function. On the premise of optimizing the input torque, the relative speed difference between the engine and the sliding clutch in the inertia stage is adjusted. Through the optimal trajectory control of the wet clutch oil pressure, slipping work and jerk are reduced, thereby improving driving comfort. The simulation results show that the slipping work and jerk generated by the system during the shift stage are reduced, and the shift quality is improved. Additionally, compared with the controller using the MATLAB particle swarm optimization algorithm, the response speed of the proposed controller is faster, the slipping work and jerk are better reduced, and the shift quality is improved.

Wide-range ZVS control technology of bidirectional dual-bridge series resonant DC/DC converters based on phase-shift frequency modulation control

Abstract Increasing the switching frequency helps optimize the performance of resonant DC-DC converters and reduces the size of resonant components, but it also increases the switching losses. To address the challenge of achieving soft switching in the medium to low power range, this paper proposes a new control strategy. In this paper, it achieves a wide range of Zero Voltage Switching (ZVS). It combines Variable Frequency Modulation (VFM) and Phase Shift Modulation (PSM), utilizing Minimum Current Trajectory (MCT) control to reduce the system’s conduction losses. Besides, VFM is used to extend the ZVS range and reduce switching losses.

Viscoelastic Kelvin–Voigt model on Ulam–Hyer’s stability and T-controllability for a coupled integro fractional stochastic systems with integral boundary conditions via integral contractors

We discuss the existence, uniqueness, Ulam–Hyer’s stability, and Trajectory (T-) controllability for solutions of coupled nonlinear fractional order stochastic differential systems (FSDEs) with integral boundary conditions via integral contractors. Using Banach space, we obtain some relaxed conditions for existence and uniqueness for the mentioned problem via successive approximation techniques. Furthermore, to demonstrate the results, the concept of bounded integral contractors is combined with a fractional order coupled system using regularity conditions. The Green’s function is used to find the solution for the coupled system with boundary conditions. Through Integral Contractors (ICs), we examine the existence and uniqueness results for a higher-order nonlinear fractional coupled stochastic system with integral boundary conditions on Time Scales. Further, we develop some conditions for Ulam–Hyer’s stability for the non-linear fractional order coupled systems. To demonstrate our main result, we provide a proper example. The real-life application of a coupled fractional stochastic Kelvin–Voigt model on viscoelastic elastomer is investigated to justify the theoretical model with the numerical comparison with a single and a double fractional stochastic Kelvin–Voigt model.

Enhancing stability and safety: A novel multi‐constraint model predictive control approach for forklift trajectory

AbstractThe advancements in intelligent manufacturing have made high‐precision trajectory tracking technology crucial for improving the efficiency and safety of in‐factory cargo transportation. This study addresses the limitations of current forklift navigation systems in trajectory control accuracy and stability by proposing the Enhanced Stability and Safety Model Predictive Control (ESS‐MPC) method. This approach includes a multi‐constraint strategy for improved stability and safety. The kinematic model for a single front steering‐wheel forklift vehicle is constructed with all known state quantities, including the steering angle, resulting in a more accurate model description and trajectory prediction. To ensure vehicle safety, the spatial safety boundary obtained from the trajectory planning module is established as a hard constraint for ESS‐MPC tracking. The optimisation constraints are also updated with the key kinematic and dynamic parameters of the forklift. The ESS‐MPC method improved the position and pose accuracy and stability by 57.93%, 37.83%, and 57.51%, respectively, as demonstrated through experimental validation using simulation and real‐world environments. This study provides significant support for the development of autonomous navigation systems for industrial forklifts.