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Driving in interactive dynamic traffic is a huge challenge for autonomous vehicles, especially for motion planning. The autonomous vehicle not only needs to predict the future states of the social vehicles to avoid a collision but also realizes the comfort and continuous plan. To deal with the problem, a spatio‐temporal decision‐making and motion planning framework with flexible constraints based on previous work is proposed. Improvements can be highlighted in three aspects. First, a neural network for trajectory prediction is trained and it is integrated into the framework, which can learn the social vehicles' reactions in the negotiation better and give more accurate predictions. Second, instead of setting the fixed local targets, the flexible‐constraint mechanism is introduced to increase the success rate of continuous planning, specifically, the local planning target is determined conditioned on the situation with an elastic factor. Third, two popular simulators with randomly disposed traffic participants are exploited, where vehicles are mutually influenced, more similar to real‐world traffic. The closed‐loop tests with zero crashes and the comparative experiments with traditional models demonstrate the superior performance and practical significance of the proposed framework. [ABSTRACT FROM AUTHOR]

Summary: To address the difficulties of under‐actuation, nonlinearity, strong coupling, disturbances and measurement noise in four‐wheel steering (4WS) vehicle tracking control, this paper presents an innovative control strategy based on differential flatness and linear active disturbance rejection controller (LADRC). The flatness property of 4WS vehicle is derived from a simplified dynamic model that ignores some complicated dynamics and external disturbances. The impact of these neglected components on the system is treated as part of the total‐disturbance, which is estimated by the observer and compensated by LADRC. Three flatness‐based control schemes, including feedback linearization controller with PID corrective term, traditional LADRC and LADRC with robust generalized proportional integral observer (RGPIO), are designed and investigated in this paper. Moreover, an overtaking scenario is selected as an application example for the tracking controllers, and the corresponding trajectory planner is designed. The tracking performances of these schemes for the overtaking trajectory are simulated on a three‐degree‐of‐freedom dynamic model and a more complete dynamic model of the 4WS vehicle, respectively. Results show the effectiveness of these schemes, especially the excellent tracking performance of LADRC with RGPIO in the presence of strong external disturbances and measurement noise. [ABSTRACT FROM AUTHOR]

The matching area selection algorithm is one of the key technologies for underwater gravity‐aided inertial navigation system, which directly affects the positioning accuracy and matching rate of underwater navigation. The traditional matching area selection algorithms usually use the statistical characteristic parameters of gravity field. However, the traditional algorithms are difficult to reflect the spatial relation characteristic of gravity field, which always miss some latent matching areas with obvious change of gravity field. In order to solve this problem, the matching area selection algorithm based on co‐occurrence matrix is proposed. The proposed algorithm establishes gravity anomaly co‐occurrence matrix and extracts spatial relation characteristic parameters to reflect the gravity field. The comprehensive spatial characteristic parameter is built by entropy and is used to select the matching area by maximization of inter‐class variance. The experimental results show that the proposed algorithm can select more effective matching areas than the traditional algorithms. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose a simultaneous state estimation and fault estimation approach for a class of first‐order hyperbolic partial integral differential equation systems. Specifically, we consider the multiplicative boundary actuator and sensor faults, ie, unknown fault parameters multiplying by the boundary input or boundary state (ie, output). As a consequence, two difficulties arise immediately: (1) simultaneous estimation of both plant state and faults is a nonlinear problem due to the multiplication between fault parameters and plant signals; (2) no prior information is available to determine the type (actuator or sensor) of faults. To overcome these difficulties, this paper develops adaptive fault parameter update laws and embeds the resulting laws into the plant state observer design. First, we propose new approaches to estimate actuator fault and sensor fault, respectively. Next, we develop a novel method to simultaneously estimate actuator and sensor faults. The proposed observer and update laws, designed using only one boundary measurement, ensure both state estimation and fault parameter estimation. By choosing appropriate Lyapunov functions, we prove that the estimates of state and fault parameters converge to an arbitrarily small neighborhood of their true values. Numerical simulations are used to demonstrate the effectiveness of the proposed estimation approaches. [ABSTRACT FROM AUTHOR]

An optimal distributed fusion estimation problem is concerned in this study for a kind of linear dynamic multirate sensors systems with correlated noise and stochastic unreliable measurements. The system is formulated at the finest scale with multiple sensors at different scales observing a common target independently with different sampling rates. The noise between different sensors is relevant, moreover, is also correlated with the system noise. The authors derive the local state estimation algorithms under the circumstance of total reliable measurements and stochastic unreliable measurements occur occasions, and the optimal distributed Kalman filter fusion algorithm, respectively. The authors provide a simulation example to illustrate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with fault detection of a kind of nonlinear dynamic system. Based on the framework of hierarchy information processing, the scope of the fault is first located by use of the presented windowing cubature Kalman filter (WCKF), followed by point-by-point fault detection to locate the fault by use of the residuals of each moment within the suspected windows. Theoretical analysis and experiments show that the presented algorithm is more effective than the traditional point-by-point fault detection method that only uses the moment residuals. The presented algorithm has potential value in many application fields, such as fault detection, reliability evaluation, fault tolerant control, etc. [ABSTRACT FROM AUTHOR]

The attitude stabilization problem for rigid spacecraft in the presence of inertial uncertainties, external disturbances, actuator saturations, and actuator faults is addressed in this paper. First, a novel fast terminal sliding mode manifold is designed to avoid the singularity problem while providing high control ability. In addition, fast terminal sliding mode control laws are proposed to make the spacecraft system trajectory fast converge onto the fast terminal sliding mode surface and finally evolve into small region in finite time, which cannot be achieved by the previous literatures. Based on the real sliding mode context, a practical adaptive fast terminal sliding mode control law is presented to guarantee attitude stabilization in finite time. Also, simulation results are presented to illustrate the effectiveness of the control strategies. [ABSTRACT FROM AUTHOR]

This article is concerned with state estimation and data fusion of a linear dynamic system observed by multirate sensors in the environment of wireless sensor networks. The sampling, estimation, and transmission rates in the wireless sensor networks are different. We derive the optimal linear estimation in the centralized, sequential, and distributed forms. A numerical example is given to show the feasibility and effectiveness of the proposed algorithms. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper investigates the control problem of finite-time attitude synchronization and tracking for a group of rigid spacecraft in the presence of environmental disturbances. A new fast terminal sliding manifold is developed for multiple spacecraft formation flying under the undirected graph topology. On the basis of the finite-time control and adaptive control strategies, two novel decentralized finite-time control laws are proposed to force the spacecraft attitude error dynamics to converge to small regions in finite time, and adaptive control is applied to reject the disturbance. The finite-time convergence and stability of the closed-loop system can be guaranteed by Lyapunov theory. Simulation examples are provided to illustrate the feasibility of the control algorithm. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The digital filtering technology has been widely applied in a majority of signal processing applications. For the linear systems with state‐space model, Kalman filter provides optimal state estimates in the sense of minimum‐mean‐squared errors and maximum‐likelihood estimation. However, only with accurate system parameters and noise statistical properties, the estimation obtained by standard Kalman filter is the optimal state estimate. Most of time, the exact noise statistical properties could not be obtained as a priori information or even wrong statistical properties may be captured by the offline method. This may lead to a poor performance (even divergence) of Kalman filtering algorithm. In this study, a novel real‐time filter, named as fast minimum norm filtering algorithm, has been proposed to deal with the case when the covariance matrices of the process and measurement noises were unknown in the linear time‐invariant systems with state‐space model. Tests have been performed on numerical examples to illustrate that the fast minimum norm filtering algorithm could be used to obtain acceptable precision state estimation in comparison with the standard Kalman filter for the discrete‐time linear time‐invariant systems. [ABSTRACT FROM AUTHOR]

Two finite‐time control algorithms are developed for distributed cooperative attitude synchronisation of multiple spacecraft with a dynamic virtual leader. Each spacecraft is modelled as a rigid body incorporating with model uncertainty and unknown external disturbance. The virtual leader gives commands to some of the follower spacecraft, and the communication network between followers can be an undirected or a directed graph. By using two neighbourhood synchronisation error signals, a finite‐time control algorithm is designed associated with adaptive mechanism such that all follower spacecraft synchronise to the virtual leader in finite time. Then a novel estimator‐based finite‐time distributed cooperative control algorithm is developed by using the followers' estimates of the virtual leader, and the convergence of the attitude and angular velocity errors can be guaranteed in finite time. Moreover, both of the control strategies are chattering‐free for their continuous design. Simulation examples are illustrated to demonstrate the validity of the two algorithms. [ABSTRACT FROM AUTHOR]

SUMMARY This paper is devoted to the attitude control of a quaternion missile model, which is nonlinear in aerodynamics with atmospheric moment uncertainties, inertia uncertainties, bounded disturbances and actuator failures. By employing the back-stepping technique, the corresponding sliding mode controller is designed to guarantee the state variables of the closed loop system to converge to a small region of the reference states with the help of the adaptive law by estimating the total uncertainties, and be chatter-free. Also, simulation results are presented to illustrate the effectiveness of the control strategy. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Many practical non‐linear systems can be described by non‐linear auto‐regressive moving average (NARMA) system models, whose stabilisation problem is challenging in the presence of large parametric uncertainties and non‐parametric uncertainties. In this work, to address this challenging problem for a wide class of discrete‐time NARMA systems, in which there are uncertain periodic parameters as well as uncertain non‐linear part with unknown periodic time delays, we develop adaptive predictive control laws using the key ideas of 'future outputs prediction' and 'nearest‐neighbour compensation', among which the former is carried out to overcome the non‐causalness problem and the latter novel idea is proposed to completely compensate for the effect of non‐linear uncertainties as well as unknown time delays. To achieve the desired asymptotic tracking performance in the presence of semi‐parametric uncertainties with time delays, an 'n ‐step parameter update law' is first designed, based on which an 'one‐step update law' is then elaborately constructed to obtain smoother closed‐loop signals. This study in general develops a systematic adaptive control framework for periodic NARMA systems with guaranteed boundedness stability and asymptotic tracking performance, which are established by rigorous theoretic proof and verified by simulation studies. [ABSTRACT FROM AUTHOR]

SUMMARY This paper considers the containment control problems for both continuous-time and discrete-time multi-agent systems with general linear dynamics under directed communication topologies. Distributed dynamic containment controllers based on the relative outputs of neighboring agents are constructed for both continuous-time and discrete-time cases, under which the states of the followers will asymptotically converge to the convex hull formed by those of the leaders if, for each follower, there exists at least one leader that has a directed path to that follower. Sufficient conditions on the existence of these dynamic controllers are given. Static containment controllers relying on the relative states of neighboring agents are also discussed as special cases. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper is concerned with the state estimation for a kind of nonlinear multirate multisensor asynchronous sampling dynamic system. There are N sensors observing a single target independently at multiple sampling rates, and the dynamic system is formulated at the highest sampling rate. Observations are obtained asynchronously, and each sensor may lose data randomly at a certain probability. The fused state estimate is generated using multiscale system theory and the modified sigma point Kalman filter. It is shown that our main results improve and extend the existing sigma point Kalman filter for which the samples are obtained multirate nonuniformly. Measurements randomly missing with Bernoulli distribution could also be allowed in this paper. Finally, the feasibility and efficiency of the presented algorithm is illustrated by a numerical simulation example.Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The cascade proportional-integral-derivative (PID) control method is widely used in power plant control field. In this paper, an active disturbance rejection control (ADRC) for a power plant with a single loop is introduced for eliminating the shortcomings of the cascade PID control method. The proposed controller based on the ADRC method consists of a tracking differentiator (TD), an extended state observer (ESO) and a nonlinear combination of errors. In this approach, the processes with higher orders, uncertainties and unmodeled dynamics are viewed as lower-ordered systems with general disturbances, and the general disturbances are estimated by ESO, and then actively compensated. Besides, only one measured output is needed in the proposed method instead of two or three in the cascade PID control method. Some simulation studies compared with the regular cascade PID control method show the proposed method has better performance beyond the regular cascade PID control method. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [ABSTRACT FROM AUTHOR]