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The design of output constrained control system for unmanned aerial vehicles deployed in confined areas is an important issue in practice and not taken into account in many autopilot systems. In this study, the authors address a neural networks-based adaptive trajectory tracking control algorithm for multi-rotors systems in the presence of various uncertainties in their dynamics. Given any sufficient smooth and bounded reference trajectory input, the proposed algorithm achieves that (i) the system output (Euclidean position) tracking error converges to a neighbourhood of zero and furthermore (ii) the system output remains uniformly in a prescribed set. Instead of element-wise estimation, a norm estimation approach of unknown weight vectors is incorporated into the control system design to relieve the onboard computation burden. The convergence property of the closed-loop system subject to output constraint is analysed via a symmetric barrier Lyapunov function augmented with several quadratic terms. Simulation results are demonstrated on a quadrotor model to validate the effectiveness of the proposed algorithm.

A novel trajectory tracking control problem based on constraint modeling and analysis is addressed by the way of constraint-following control for the unmanned tracked vehicle in this paper. The unmanned tracked vehicle system contains time-varying uncertainty which is possibly swift but bounded, and the bound is possibly unknown. First, the coupled dynamics model of unmanned tracked vehicle is established. By taking into account the kinematic characteristics, it makes the motion control of unmanned tracked vehicles more precise. Meanwhile, a 3D virtual prototype model is established for the unmanned tracked vehicle. Second, for the control objective of trajectory tracking, the related problem is converted into a constraint-following problem, and an adaptive robust controller is therefore proposed based on this for the controlled unmanned tracked vehicle system to satisfy the trajectory tracking constraint. Finally, it is proved that the controlled unmanned tracked vehicle system can achieve accurate trajectory tracking with the proposed adaptive robust control, even under the interference of complex time-varying uncertainties. Modeling accurate dynamics and trajectory tracking constraints for unmanned tracked vehicles while designing an adaptive robust controller to realize accurate motion control for unmanned tracked vehicles even under strong external disturbances are the main contributions of this paper.

This paper investigates the model-free trajectory tracking control problem for an autonomous underwater vehicle (AUV) subject to the ocean currents, external disturbances, measurement noise, model parameter uncertainty, initial tracking errors, and thruster malfunction. A novel control architecture based on model-free control principles is presented to guarantee stable and precise trajectory tracking performance in the complex underwater environment for AUVs. In the proposed hybrid controller, intelligent-PID (i-PID) and PD feedforward controllers are combined to achieve better disturbance rejections and initial tracking error compensations while keeping the trajectory tracking precision. A mathematical model of an AUV is derived, and ocean current dynamics are included to obtain better fidelity when examining ocean current effects. In order to evaluate the trajectory tracking control performance of the proposed controller, computer simulations are conducted on the LIVA AUV with a compelling trajectory under various disturbances. The results are compared with the two degrees-of-freedom (DOF) i-PID, i-PID, and PID controllers to examine control performance improvements with the guaranteed trajectory tracking stability. The comparative results revealed that the i-PID with PD feedforward controller provides an effective trajectory tracking control performance and excellent disturbance rejections for the entire trajectory of the AUV.

For long horizontal-section drilling in reservoirs and complex formations, efficient and robust trajectory planning with real-time closed-loop control must be achieved under curvature and mechanical constraints. This study systematically investigates the application of the Dubins curve, a shortest-path model satisfying a minimum curvature constraint, in closed-loop wellbore trajectory control. Six canonical configurations (LSL, RSR, LSR, RSL, LRL, and RLR) are analyzed, and a standardized procedure for path solution and coordinate reconstruction is established. Parametric analyses reveal the effects of curvature limit, target direction, and target distance on trajectory feasibility and path length. Case studies show that unoptimized Dubins trajectories can achieve a high reservoir-contact ratio (99.69%) but exhibit curvature discontinuities at segment junctions, which induce torque and friction peaks. By introducing a multi-objective optimization strategy combining minimum turning-radius expansion and adaptive target adjustment, these curvature discontinuities are effectively mitigated: the maximum curvature was reduced to 11.15°/30 m, the average curvature to 2.57°/30 m, the average friction to 1118.7 N, and the cumulative torque to 31,468 Nm, while maintaining nearly unchanged reservoir contact. The proposed method effectively improves trajectory smoothness and mechanical drillability while preserving real-time computational efficiency, offering a practical approach for closed-loop trajectory optimization in complex geological settings.

This paper investigates the finite-time position trajectory tracking control problem of quadrotor unmanned aerial vehicles (UAVs). Different from the standard inner–outer-loop control scheme, the proposed finite-time controller is constructed with an order-supplementary mechanism. Concretely, some virtual extended states with second-order dynamics are utilized in the controller design of the UAV’s position-loop subsystem, to replace the original feedback part of tracking errors. Then, the adding a power integrator technique is used in the establishment of the virtual state dynamics, such that the position loop of quadrotor UAVs achieves the trajectory tracking tasks in finite time. Meanwhile, the attitude command references are directly formulated from the virtual extended states. Moreover, to deal with disturbances or unknown velocities, some finite-time observers are further combined in the proposed approach to obtain the corresponding estimates of disturbances and velocities. Compared with the existing results, the proposed order-supplementary finite-time trajectory tracking approach can remove the use of filters in the attitude command resolution and realize strict finite-time convergence. The thrust control input for the position-loop subsystem can be adjusted more flexibly by setting the initial values of the introduced virtual states. In addition, the finite-time velocity observer provided in this paper takes aerodynamic damping into account and has more accurate estimation in practice. Some simulations are given to validate the effectiveness of the proposed approach.

This paper proposes a smooth mode-switching method based on state trajectory control to suppress overshoot and to shorten switching time during the mode switching of LLC-C resonant converters. First, the resonant tank trajectories of the LLC and LCCL are analyzed. Second, through a transformation of the resonant tank trajectory, the optimal trajectory of the resonant tank switching is drawn. Then the switching optimization cycle time is calculated by a diagram of the optimal trajectory. Thus, the PWM conversion to the optimization cycle is controlled directly when the switching signal comes. By this control method, a smooth transition of the resonant tank voltage and current between the two modes is achieved. At last, a prototype with a rated power of 500 W is built to check the feasibility and effectiveness of the proposed switching method. Experiment results show that the current surge of the resonant tank is reduced from 12.3 to 8.6 A when the state trajectory control is applied. The voltage surge of the second resonant capacitor in parallel is reduced from 906 to 712 V. Meanwhile, the switching time is shortened by 0.21 ms, which speeds up the switching process.

Background The ankle joint complex (AJC) is of fundamental importance for balance, support, and propulsion. However, it is particularly susceptible to musculoskeletal and neurological injuries, especially neurological injuries such as drop foot following stroke. An important factor in ankle dysfunction is damage to the central nervous system (CNS). Correspondingly, the fundamental goal of rehabilitation training is to stimulate the reorganization and compensation of the CNS, and to promote the recovery of the motor system’s motor perception function. Therefore, an increasing number of ankle rehabilitation robots have been developed to provide long-term accurate and uniform rehabilitation training of the AJC, among which the parallel ankle rehabilitation robot (PARR) is the most studied. The aim of this study is to provide a systematic review of the state of the art in PARR technology, with consideration of the mechanism configurations, actuator types with different trajectory tracking control techniques, and rehabilitation training methods, thus facilitating the development of new and improved PARRs as a next step towards obtaining clinical proof of their rehabilitation benefits. Methods A literature search was conducted on PubMed, Scopus, IEEE Xplore, and Web of Science for articles related to the design and improvement of PARRs for ankle rehabilitation from each site’s respective inception from January 1999 to September 2020 using the keywords “ parallel”, “ ankle”, and “ robot”. Appropriate syntax using Boolean operators and wildcard symbols was utilized for each database to include a wider range of articles that may have used alternate spellings or synonyms, and the references listed in relevant publications were further screened according to the inclusion criteria and exclusion criteria. Results and discussion Ultimately, 65 articles representing 16 unique PARRs were selected for review, all of which have developed the prototypes with experiments designed to verify their usability and feasibility. From the comparison among these PARRs, we found that there are three main considerations for the mechanical design and mechanism optimization of PARRs, the choice of two actuator types including pneumatic and electrically driven control, the covering of the AJC’s motion space, and the optimization of the kinematic design, actuation design and structural design. The trajectory tracking accuracy and interactive control performance also need to be guaranteed to improve the effect of rehabilitation training and stimulate a patient’s active participation. In addition, the parameters of the reviewed 16 PARRs are summarized in detail with their differences compared by using figures and tables in the order they appeared, showing their differences in the two main actuator types, four exercise modes, fifteen control strategies, etc., which revealed the future research trends related to the improvement of the PARRs. Conclusion The selected studies showed the rapid development of PARRs in terms of their mechanical designs, control strategies, and rehabilitation training methods over the last two decades. However, the existing PARRs all have their own pros and cons, and few of the developed devices have been subjected to clinical trials. Designing a PARR with three degrees of freedom (DOFs) and whereby the mechanism’s rotation center coincides with the AJC rotation center is of vital importance in the mechanism design and optimization of PARRs. In addition, the design of actuators combining the advantages of the pneumatic-driven and electrically driven ones, as well as some new other actuators, will be a research hotspot for the development of PARRs. For the control strategy, compliance control with variable parameters should be further studied, with sEMG signal included to improve the real-time performance. Multimode rehabilitation training methods with multimodal motion intention recognition, real-time online detection and evaluation system should also be further developed to meet the needs of different ankle disability and rehabilitation stages. In addition, the clinical trials are in urgent need to help the PARRs be implementable as an intervention in clinical practice.

A wheelchair is a special form of Personal Mobility Vehicle (PMV) for people with disabilities to help them move safely to their desired location. However, unlike conventional PMVs, wheelchairs for people with disabilities when moving in places with heavy traffic will be difficult to control manually. This study focuses on the designing and feasibility testing of PMV's control systems for people with disabilities to meet safety and efficiency standards even in complex environments. First, the control structure model required for PMV is proposed. This is followed by the application of the combination between First In First Out (FIFO) and Dijkstra traffic control methods to determine effective trajectory for PMVs. After that, the proposed wheelchair trajectory control system is automatically tested on the campus of the University of Transport and Communications. The simulation results show that if the time of 10 PMVs is taken as the comparative time, when the number of PMVs is from 5 to 20, the order execution time of each PMV hardly changes much (no more than 10%), when the number of PMW increases to 25, 30, 35, 40, the order execution time of all vehicles increased from 35%, 55%, 103%, 119%. It is shown that the system can safely control a large number of PMVs at standards such as efficiency, safety, flexibility.

PurposeThis study discusses the tracking trajectory issue of the exoskeleton under the bounded disturbance and designs an useful tracking trajectory control method to solve it. By using the proposed control method, the tracking error can be successfully convergence to the assigned boundary. Meanwhile, the chattering effect caused by the actuators is already reduced, and the tracking performance of the pneumatic artificial muscles (PAMs) elbow exoskeleton is improved effectively.Design/methodology/approachA prescribed performance sliding mode control method was developed in this study to fulfill the joint position tracking trajectory task on the elbow exoskeleton driven by two PAMs. In terms of the control structure, a dynamic model was built by conforming to the adaptive law to compensate for the time variety and uncertainty exhibited by the system. Subsequently, a super-twisting algorithm-based second-order sliding mode control method was subjected to the exoskeleton under the boundedness of external disturbance. Moreover, the prescribed performance control method exhibits a smooth prescribed function with an error transformation function to ensure the tracking error can be finally convergent to the pre-designed requirement.FindingsFrom the theoretical perspective, the stability of the control method was verified through Lyapunov synthesis. On that basis, the tracking performance of the proposed control method was confirmed through the simulation and the manikin model experiment.Originality/valueAs revealed by the results of this study, the proposed control method sufficiently applies to the PAMs elbow exoskeleton for tracking trajectory, which means it has potential application in the actual robot-assisted passive rehabilitation tasks.

This paper investigates the trajectory tracking control problem of a four-wheel independently driven skid-steering mobile robot (FWID-SSMR) while considering friction resistance, parameter variation and external disturbances. Unlike previous studies that only achieved stable tracking control of FWID-SSMR, this paper accomplishes prescribed steady-state and transient performance. Based on the dynamic model of FWID-SSMR, an integer-order prescribed-time controller (IOPTC) is developed first, which can make the tracking errors converge to a predetermined residual set with a preset convergence rate in a prescribed time. Motivated by it, a fractional-order prescribed-time controller (FOPTC) is developed by exploiting the genetic attenuation properties of fractional calculus (FC) for improving the control performance. The feasibility and effectiveness of the developed controller are verified by Lyapunov theoretical analysis and numerical simulation studies. The simulation results show that both the IOPTC and FOPTC outperform the feedback controller (FBC). Moreover, the influence of the performance function on control performance is also tested, which can serve as a reference for selecting the appropriate performance function to use in future applications.