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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.

This paper proposes an operation safety early warning system based on LabView (2014, National Instruments Corporation, Austin, TX, USA) for vascular interventional surgery (VIS) robotic system. The system not only provides intuitive visual feedback information for the surgeon, but also has a safety early warning function. It is well known that blood vessels differ in their ability to withstand stress in different age groups, therefore, the operation safety early warning system based on LabView has a vascular safety threshold function that changes in real-time, which can be oriented to different age groups of patients and a broader applicable scope. In addition, the tracing performance of the slave manipulator to the master manipulator is also an important index for operation safety. Therefore, we also transformed the slave manipulator and integrated the displacement error compensation algorithm in order to improve the tracking ability of the slave manipulator to the master manipulator and reduce master–slave tracking errors. We performed experiments “in vitro” to validate the proposed system. According to previous studies, 0.12 N is the maximum force when the blood vessel wall has been penetrated. Experimental results showed that the proposed operation safety early warning system based on LabView combined with operating force feedback can effectively avoid excessive collisions between the surgical catheter and vessel wall to avoid vascular puncture. The force feedback error of the proposed system is maintained between ±20 mN, which is within the allowable safety range and meets our design requirements. Therefore, the proposed system can ensure the safety of surgery.

In this article, we describe a new method for detecting global navigation satellite system (GNSS) spoofing using an inertial navigation system. We specifically address the most difficult-to-detect scenario, in which a spoofer replicates the authentic GNSS signal with only additive errors due to the spoofer’s uncertainty in knowledge of the target’s position. We derive an optimal monitor to detect the anomalous temporal structure of the spoofed measurements caused by the spoofer’s target tracking errors. This new monitor uses accumulated Kalman filter innovations projected into the position state domain. We demonstrate how the monitor window length can be set to achieve any required missed detection probability, and we evaluate the performance of the monitor for both white and colored tracking error. Finally, we present a complementary solution separation monitoring concept to detect rapid-onset spoofing and to achieve protection levels in real time.

This paper proposes a tracking error prediction and control dynamic speed planning method based on the theoretical model of servo control system. Compared to the traditional approach of sacrificing processing efficiency to improve processing accuracy by reducing kinematic constraints, this method achieves a balance between processing efficiency and processing accuracy. Firstly, this study introduces a time-optimised algorithm based on velocity, acceleration and jerk and uses the pseudo-jump method to linearise nonlinear problems. Secondly, based on the commonly used PID servo control system in industry, a kinematic parameter tracking error prediction method that considers axis dynamic performance is proposed. Based on the prediction results, a new feed speed planning and control algorithm based on dynamic characteristics is further proposed, which achieves control of tracking errors. Thirdly, since the proposed feed speed control method based on dynamic characteristics is not a linear constraint, this study proposes a convex relaxation method combined with the pseudo-jerk method to linearize the nonlinear problem, and effectively obtains a feed speed profile that is close to optimal. Finally, through experiments compared with traditional kinematic methods, the results show that the new method has smaller tracking errors and higher processing efficiency.

In the applications of multi-axis computer-numerical-control (CNC) precision machining, one of the important issues in the multi-axis contour following task is to reduce the contour error in the machining process. A popular method to solve this problem is the cross-coupling control (CCC). As the traditional CCC method cannot meet the requirements for tracking accuracy and contour control accuracy for large curvature positions, a novel integrated control strategy of cross-coupling contour error compensation, which consists of an improved real-time contour error estimation algorithm based on arc length parameters, an improved position error compensator (PEC) and a single neuron cross-coupling controller, is proposed. To improve the accuracy of contour error estimation for large curvature trajectories, an improved real-time estimation algorithm of contour error based on arc length parameters is proposed. The method first finds the nearest interpolation point by backtracking method and calculates the backward reference point by using the method based on arc length parameters. Then, the obtained backward reference point is used as the desired command point by arc approximation method to find the estimated value of contour error. Moreover, a single-neuron adaptive cross-coupling controller is designed, which continuously adjusts the weights through a single-neuron learning algorithm to reach the effect of improving the control accuracy. In addition, an improved PEC method is further presented, which improves the tracking accuracy by compensating the tracking error in advance. The feasibility of the proposed integrated control strategy is verified with several non-uniform rational B-spline (NURBS) parametric curve contour following experiments. Moreover, experimental results indicate that the proposed integrated control strategy can significantly improve the tracking and contour control accuracy of biaxial contour following tasks compared with None CCC method and CCC method and has better contour control performance in large curvature positions.

This paper presents a Tracking-Error Learning Control (TELC) algorithm for precise mobile robot path tracking in off-road terrain. In traditional tracking error-based control approaches, feedback and feedforward controllers are designed based on the nominal model which cannot capture the uncertainties, disturbances and changing working conditions so that they cannot ensure precise path tracking performance in the outdoor environment. In TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics and the plant-model mismatch problem is thus discarded. Therefore, the feedforward controller gradually eliminates the feedback controller from the control of the system once the mobile robot has been on-track. In addition to the proof of the stability, it is proven that the cost functions do not have local minima so that the coefficients in TELC algorithm guarantee that the global minimum is reached. The experimental results show that the TELC algorithm results in better path tracking performance than the traditional tracking error-based control method. The mobile robot controlled by TELC algorithm can track a target path precisely with less than 10 cm error in off-road terrain.

Recently, numerous investors have shifted from active strategies to passive strategies because the passive strategy approach affords stable returns over the long term. Index tracking is a popular passive strategy. Over the preceding year, most researchers handled this problem via a two-step procedure. However, such a method is a suboptimal global-local optimization technique that frequently results in uncertainty and poor performance. This paper introduces a framework to address the comprehensive index tracking problem (IPT) with a joint approach based on metaheuristics. The purpose of this approach is to globally optimize this problem, where optimization is measured by the tracking error and excess return. Sparsity, weights, assets under management, transaction fees, the full share restriction, and investment risk diversification are considered in this problem. However, these restrictions increase the complexity of the problem and make it a nondeterministic polynomial-time-hard problem. Metaheuristics compose the principal process of the proposed framework, as they balance a desirable tradeoff between the computational resource utilization and the quality of the obtained solution. This framework enables the constructed model to fit future data and facilitates the application of various metaheuristics. Competitive results are achieved by the proposed metaheuristic-based framework in the presented simulation.

Abstract Contour error compensation is an active research topic in five-axis CNC machining, especially in the manufacturing of sculptured surface parts. Nevertheless, current methods are mainly based on the mirror compensation principle, and fail to obtain a desired level of accuracy when processing parts with tight curvature feature. To address this issue, a global toolpath modulation-based contour error pre-compensation method is developed in this paper, which incorporates the error compensation issue into the stage of toolpath planning with a linear analytical solution. In this method, the nominal toolpaths used to machine the products is first expressed by dual B-spline curves, and then the instantaneous tracking error model of each individual drive is built with respect to control points of splined path. Afterward, the satisfaction condition of the spline control points for eliminating the contour error is yielded, which provides a possibility for compensating contour error in a global manner, and the neighbor-dependent coupling issue in error compensation between adjacent cutter location points is capable of being handled as well. On this basis, by applying the least-squares technique, the complicated contour error pre-compensation problem is further converted into a solution of simpler linear equation system. For enhancing its robustness when processing long toolpaths, an adaptive piecewise modulation strategy is also developed. Finally, both experiment and simulation are conducted to validate the proposed method, and the results demonstrate that the proposed method can significantly improve contour precision at low computational costs when compared with the existing pre-compensation method.

Single-point diamond turning technology has been widely used in processing microstructures. The accuracy of ultraprecision cutting machine tools affects the performance of the microstructure. By improving the structure of the machine tool itself, the machining accuracy of ultraprecision diamond lathes has almost become optimized. Therefore, this research identifies and analyzes the linear axis tracking error through the dynamic modeling of a macro/micro cutting system. Furthermore, the impact of the tracking error on machining accuracy is obtained. Based on the macro/micro cutting system, a servo tracking error compensation method is proposed, and the effectiveness of this error compensation strategy is verified by simulation. The proposed experimental approach includes cutting experiments of tracking error compensation for a hyperbolic sine wave surface structure, verifying the surface profile accuracy of the workpiece with and without tracking error compensation. Additionally, this study proposes a profile evaluation method for microstructure. Experimental results show that the proposed tracking error compensation strategy effectively reduces the tracking error of ultraprecision cutting machine tools. Additionally, the proposed approach significantly improves the microstructure machining profile accuracy and can be used for ultraprecision lathes with high precision.

In order to achieve accurate control of uncertain mechanical systems, nonlinearity and uncertainty are two major challenges. To address these challenges, this paper proposes a “dynamic” performance metric, denoted as ϑ ^ , which differs from the previous performance metric ϑ, to describe the constraint tracking error in a general way. Based on the metric ϑ ^ , a new robust control method is proposed to improve the control performance. The control design process consists of two stages. In the first stage, a robust control scheme is designed to provide guaranteed performance in the presence of uncertainty. This control scheme includes a tunable parameter that can be selected within a certain range. In the second stage, an intelligent multi-agent method is employed to determine the optimal control parameter. This method considers multiple performance indexes, each of which involves different design considerations that may be opposed to each other. Adjusting parameters may improve one performance index while reducing another. Therefore, the intelligent multi-agent method is used to optimize performance indexes and obtain the optimal control parameter. The proposed design method is implemented on a mobile robot, and its performance is verified by comparing with other methods.