Development of vehicle maneuvering system for autonomous driving

Recently, autonomous driving technology has received significant interest, and consequently, various autonomous driving algorithms have been developed by researchers. An experimental (conventional) vehicle verifies the strategy for autonomous driving by testing the performance and feasibility of the corresponding algorithms. Implementation of the autonomous driving strategy in the conventional vehicle system requires modification of the electronic control unit (ECU); however, this is limited by security issues and the high cost of development.In this paper, a maneuvering system that can control the steering angle, braking force, and accelerating signal so that autonomous driving does not operate via the ECU is proposed. Because the steering angle is an important factor in terms of the vehicle motion behavior, a robust angle controller based on the disturbance observer is adopted. Moreover, to ensure safety and user convenience, a user intention recognition algorithm is proposed. Based on this algorithm, the maneuvering system can be disabled during control without using any perception sensor. Finally, several experiments are conducted to evaluate the functions of the maneuvering system. A scenario-based driving test is also performed to verify the feasibility of the maneuvering system.     

非线性系统的多模型DMC解耦设计

针对工业生产过程大多为多输入多输出非线性系统,干扰因素多,耦合作用强,常规控制方法难以获得满意控制效果的特.提出一种基于动态矩阵控制(DMC)的多模型控制方法,采用误差指标切换函数作为切换准则,根据其最小值来选择与实际最接近模型,并将基于此模型的控制器切换为当前控制器.结合目标函数解耦方法,在模型中考虑其他通道输入对当...     

Adaptive control for a class of nonlinear systems with time-varying delays in state and input

This paper is concerned with the adaptive stabilization problem of uncertain input delayed systems.A solution to this problem is given for a class of uncertain nonlinear systems with time-varying delays in both state and input.An adaptive asymptotically stabilizing controller,which can guarantee the stability of the closed-loop system and the convergence of the original system state,is designed by means of the Lyapunov-Krasovskii functional stability theory combined with linear matrix inequalities (LMIs) an...     

Ecological assessment of Phragmites australis wetlands using multi-season SPOT-5 scenes

Ecologists and conservationists need accurate and replicable tools for monitoring wetland conditions in order to develop and implement adaptive management strategies efficiently. The Rhone Delta (Camargue) in southern France encloses 9200 ha of fragmented reed marshes actively managed for reed harvesting, waterfowl hunting or cattle grazing, and holding significant numbers of vulnerable European birds. We used multi-season SPOT-5 data in conjunction with ground survey to assess the predictive power of satellite imagery in modelling indicators of reed structure (height, diameter, density and cover of green/dry stems) relevant to ecosystem management and bird ecology. All indicators could be predicted accurately with a combination of bands (SWIR, NIR) and indices (SAVI, OSAVI, NDWI, DVI, DVW, MSI) issued from scenes of March, June, July, September or December and subtraction between these. All models were robust when validated with an independent set of satellite and field data. The high spatial resolution of SPOT-5 scenes (pixel of 10 × 10 m) permits the monitoring of detailed attributes characterizing the reed ecosystem across a large spatial extent, providing a scientifically-based, replicable tool for managers, stakeholders and decision-makers to follow wetland conditions in the short and long-term. Combined with models on the ecological requirements of vulnerable bird species, these tools can provide maps of potential species ranges at spatial extents that are relevant to ecosystem functioning and bird populations.     

Multiple sliding mode observers and unknown input estimations for Lipschitz nonlinear systems

Multiple sliding mode observers for state and unknown input estimations of a class of MIMO nonlinear systems are systematically developed in this paper. A new nonlinear transformation is formulated to divide the original system into two interconnected subsystems. The unknown inputs are assumed to be bounded and not necessarily Lipschitz, and do not require any matching condition. Under structural assumptions for the unknown input distribution matrix, the sliding mode terms of the nonlinear observer are designed to track their respective unknown inputs. Also, the unknown inputs can be reconstructed from the multiple sliding mode structurally. The conditions for asymptotic stability of estimation error dynamics are derived. Finally, simulation results are given to demonstrate the effectiveness of the proposed method. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive discrete neural observer design for nonlinear systems with unknown time‐delay

This paper focuses on the adaptive observer design for nonlinear discrete‐time MIMO systems with unknown time‐delay and nonlinear dynamics. The delayed states involved in the system are arguments of a nonlinear function and only the estimated delay is utilized. By constructing an appropriate Lyapunov–Krasovskii function, the delay estimation error is considered in the observer parameter design. The proposed method is then extended to the system with a nonlinear output measurement equation and the delayed dynamics. With the help of a high‐order neural network (HONN), the requirement for a precise system model, the linear‐in‐the‐parameters (LIP) assumption of the delayed states, the Lipschitz or norm‐boundedness assumption of unknown nonlinearities are removed. A novel converse Lyapunov technical lemma is also developed and used to prove the uniform ultimate boundedness of the proposed observer. The effectiveness of the proposed results is verified by simulations. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive NN output‐feedback decentralized stabilization for a class of large‐scale stochastic nonlinear strict‐feedback systems

In this paper, the decentralized adaptive neural network (NN) output‐feedback stabilization problem is investigated for a class of large‐scale stochastic nonlinear strict‐feedback systems, which interact through their outputs. The nonlinear interconnections are assumed to be bounded by some unknown nonlinear functions of the system outputs. In each subsystem, only a NN is employed to compensate for all unknown upper bounding functions, which depend on its own output. Therefore, the controller design for each subsystem only need its own information and is more simplified than the existing results. It is shown that, based on the backstepping method and the technique of nonlinear observer design, the whole closed‐loop system can be proved to be stable in probability by constructing an overall state‐quartic and parameter‐quadratic Lyapunov function. The simulation results demonstrate the effectiveness of the proposed control scheme. Copyright © 2010 John Wiley & Sons, Ltd.     

Semi‐global output feedback tracking control for fully actuated ships

An output feedback trajectory tracking controller for a kind of 3 degrees of freedom (DOF) ship motion nonlinear model is designed. The closed‐loop control system is proven to be uniformly semi‐globally exponential stable with a separate stability result for the observer by applying nonlinear cascaded system theory. Simulation results on a model ship, Cybership II, are presented to validate the proposed control scheme. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

面向钢铁集团测量过程的工作流设计

为实现钢铁集团测量过程的流程化管理,提出一种基于扩展有限状态机的工作流元模型,给出元模型的元素定义与迁移原理。根据ISO10012测量过程的受控条件,利用该模型对测量过程进行工作流与角色权限设计,实现基于浏览器/服务器的3层工作流系统。结果表明,该系统能满足测量过程的7个受控条件。     

结合免疫优化和LS-SVRM观测器的非线性系统自适应鲁棒控制

针对一类单输入单输出不确定非线性控制系统提出了一种自适应鲁棒控制算法. 由于最小均方支持向量回归机(LS-SVRM)的最终解可以化为一个具有线性约束的二次规划问题, 不存在局部极小, 所以该算法在不要求假设系统的状态向量是可测的条件下通过设计基于LS-SVRM的观测器来估计系统的状态向量; 同时在算法中假设LS-SVRM的最优逼近参数向量和标称参数向量之差的范数和逼近误差的界限是未知的, 因此可通过对未知界限估计的调节来提高系统的鲁棒性. 考虑到LS-SVRM本身参数对LS-SVRM性能的影响, 本文应用一种新的免疫优化算法对LS-SVRM的参数进行优化, 从而提高LS-SVRM的逼近能力. 理论研究和仿真例子证实了所提方法的可行性和有效性.