Controllability of Linear Distributed Systems in Classes of Generalized Actions

A review of several methods for investigation of controllability of linear systems with generalized control actions (pulse and pointwise control) is given. The approach proposed is based on the theory of negative and positive spaces and a priori inequalities with negative norms. Some concrete results are given for pseudo-parabolic and pseudohyperbolic systems.     

Computation of discriminating kernel and robust capture basin with regulation map by interval methods

This paper proposes a novel algorithm that characterizes the robust capture basin and the discriminating kernel for constrained nonlinear systems with uncertainties based on viability theory. For nonlinear systems with constrained inputs and bounded uncertainties, the viability kernel is the largest set of states possessing a possibility to be viable in a set, and the capture basin is the largest set of states possessing a possibility to reach a target in a finite time, and keeping viable in a set before reaching the target. However, in the viability theory, both control and uncertainty in a parameterized system are considered as parameters: the discriminating kernel and the proposed robust capture basin link viability theory with robust control, which take both control and uncertainties into account. For the constrained uncertain nonlinear systems, the discriminating kernel is the largest set of states that is robust invariant in a set with proper control, and the robust capture basin is the largest set of states reaching their target in finite time with proper control despite of uncertainties and keeping viable in a set before reaching the target. Furthermore, we map all the states to optimal regulatory control such that the systems are regulated by a regulation map. To compute the robust capture basin and the discriminating kernel, we use interval methods to provide guaranteed solutions. The proposed algorithms in this paper approximate an outer approximation of the minimum reachable target and inner approximations of the robust capture basin and the discriminating kernel in a guaranteed way.     

High-order repetitive control for discrete-time linear switched systems

This paper is concerned with the design of a high-order repetitive control (RC) law for a class of discrete-time linear switched systems with repetition-varying reference trajectories. First, a high-order RC law, which embeds the characteristic of known variation of the reference trajectories, is proposed to the system, and a two-dimensional (2D) model is presented to describe the control and learning actions of the repetitive control system by using the lifting technique. By choosing appropriate multiple Lyapunov–Krasovskii functions, sufficient conditions for asymptotic stability of the 2D system are derived in the form of a set of linear matrix inequalities. Finally, an example is given to illustrate the effectiveness of the proposed results.     

XCS-based reinforcement learning algorithm for motion planning of a spherical mobile robot

A Reinforcement Learning (RL) algorithm based on eXtended Classifier System (XCS) is used to navigate a spherical robot. Traditional motion planning strategies rely on pre-planned optimal trajectories and feedback control techniques. The proposed learning agent approach enjoys a direct model-free methodology that enables the robot to function in dynamic and/or partially observable environments. The agent uses a set of guard-action rules that determines the motion inputs at each step. Using a number of control inputs (actions) and the developed RL scheme, the agent learns to make near-optimal moves in response to the incoming position/orientation signals. The proposed method employs an improved variant of the XCS as its learning agent. Results of several simulated experiments for the spherical robot show that this approach is capable of planning a near-optimal path to a predefined target from any given position/orientation.     

Non-linear feedback control of a novel chaotic system

From the classical Lü chaotic system a new simple three-dimensional autonomous system is derived, which exhibits a three-scroll chaotic attractor. An approach to control this novel attractor by non-linear feedback functions is proposed. The results obtained reveal that the trajectories of the chaotic attractor can be controlled to reach certain target periodic orbits or points. Finally, some numerical simulations are provided to show the effectiveness and feasibility of the controller design method developed.     

基于模糊聚类和卡尔曼滤波方法的模糊辨识*

本文提出一种通用的基于模糊聚类和卡尔曼滤波方法的模糊辨识方法。模糊聚类方法在给定的广义目标下按线性簇对被辨识的样本数据进行聚类，这样使得被辨识模型可用基干局部线性模型表示，然后，利用卡尔曼滤波方法拟合这些线性模型。本文给出了详细的模糊辨识算法。为了验证该辨识方法的有效性，本文最后给出了熟知的Ｂｏｘ－Ｊｅｎｋｉｎｓ数据的辨识结果。     

复杂系统的质量生存可达性决策

为进一步刻画复杂系统的有限理性决策，在复杂系统生存可达性决策的基础上，引入生存质量函数，提出质量生存可达性决策的概念，并给出 了质量生存可达性决策存在的充要条件。讨论了复杂系统质量生存可达性决策的一般特点和决策方法。     

A new convolution approach for the time-delay identification of systems with arbitrary entries

In this paper, an online delay identification approach of continuous-time linear systems with unstructured entries is achieved via a new algebraic technique. The arbitrary input and output trajectories are chosen with close and abundant crossing zero. Initial conditions and static disturbances are taken into account in the design of the identification approach. The proposed method is based on a distributional algebraic technique and a convolution approach. A proposed theorem is hence enounced to identify a single time-delay of such systems. The effectiveness of the proposed approach is demonstrated by an illustrative example. The obtained results show the high performances of the proposed time-delay identification approach in severe operation conditions of the considered system.     

线性控制系统多面体区域的生存性判别

利用非光滑分析, 讨论线性控制系统多面体区域的生存性判别. 对于有界多面体(利用有限点集的凸包来表示), 其生存性判别只需检验其在极点处是否满足生存性条件, 去掉了以往对输入集合为多面体的要求, 这种生存性判别方法简便易行. 最后利用所给出的生存性条件讨论了生存性设计.

     

Constructing NARMAX models using ARMAX models

This paper outlines how it is possible to decompose a complex non-linear modelling problem into a set of simpler linear modelling problems. Local ARMAX models valid within certain operating regimes are interpolated to construct a global NARMAX (non-linear NARMAX) model. Knowledge of the system behaviour in terms of operating regimes is the primary basis for building such models, hence it should not be considered as a pure black-box approach, but as an approach that utilizes a limited amount of a priori system knowledge. It is shown that a large class of non-linear systems can be modelled in this way, and indicated how to decompose the systems range of operation into operating regimes. Standard system identification algorithms can be used to identify the NARMAX model, and several aspects of the system identification problem are discussed and illustrated by a simulation example.     

Design of stabilizing switching control laws for discrete- and continuous-time linear systems using piecewise-linear Lyapunov functions

In this paper, the stability of switched linear systems is investigated using piecewise linear Lyapunov functions. In particular, we identify classes of switching sequences that result in stable trajectories. Given a switched linear system, we present a systematic methodology for computing switching laws that guarantee stability based on the matrices of the system. In the proposed approach, we assume that each individual subsystem is stable and admits a piecewise linear Lyapunov function. Based on these Lyapunov functions, we compose 'global' Lyapunov functions that guarantee stability of the switched linear system. A large class of stabilizing switching sequences for switched linear systems is characterized by computing conic partitions of the state space. The approach is applied to both discrete-time and continuous-time switched linear systems.     

基于Frenet坐标系的自动驾驶轨迹规划与优化算法

针对公路场景下作速度保持的自动驾驶汽车实时轨迹规划问题,提出一种基于Frenet坐标系的优化轨迹规划算法.首先,利用Frenet坐标系将车辆运动解耦,构建无约束横向/纵向独立积分系统;然后,根据初始配置和可内嵌到行为层的目标配置,通过采样生成有限的4次、5次多项式候选轨迹集合;最后,利用以高斯卷积、加速度变化率为核心的安全性和舒适性评价指标构建损失函数,评价轨迹成本,并结合曲率、加速度检查,选择能够最小化损失的最优解.结果表明,该算法能满足公路型场景的规划需求,车辆运动轨迹平滑、舒适、安全性更高.     

Fault detection and isolation using viability theory and interval observers

This paper proposes the use of interval observers and viability theory in fault detection and isolation (FDI). Viability theory develops mathematical and algorithmic methods for investigating the viability constraints characterisation of dynamic evolutions of complex systems under uncertainty. These methods can be used for checking the consistency between observed and predicted behaviour by using simple sets that approximate the exact set of possible behaviour (in the parameter or state space). In this paper, FDI is based on checking for an inconsistency between the measured and predicted behaviours using viability theory concepts and sets. Finally, an example is provided in order to show the usefulness of the proposed approach.     

Exploring Temporal Structure of Trajectory Components for Action Recognition

Action recognition is one of the most important components for video analysis. In addition to objects and atomic actions, temporal relationships are important characteristics for many actions and are not fully exploited in many approaches. We model the temporal structures of midlevel actions (referred to as components) based on dense trajectory components, obtained by clustering individual trajectories. The trajectory components are a higher level and a more stable representation than raw individual trajectories. Based on the temporal ordering of trajectory components, we describe the temporal structure using Allen's temporal relationships in a discriminative manner and combine it with a generative model using bag of components. The main idea behind the model is to extract midlevel features from domain‐independent dense trajectories and classify the actions by exploring the temporal structure among these midlevel features based on a set of relationships. We evaluate the proposed approach on public data sets and compare it with a bag‐of‐words–based approach and state‐of‐the‐art application of the Markov logic network for action recognition. The results demonstrate that the proposed approach produces better recognition accuracy.     

Recognition, prediction, and planning for assisted teleoperation of freeform tasks

The approach of inferring user’s intended task and optimizing low-level robot motions has promise for making robot teleoperation interfaces more intuitive and responsive. But most existing methods assume a finite set of candidate tasks, which limits a robot’s functionality. This paper proposes the notion of freeform tasks that encode an infinite number of possible goals (e.g., desired target positions) within a finite set of types (e.g., reach, orient, pick up). It also presents two technical contributions to help make freeform UIs possible. First, an intent predictor estimates the user’s desired task, and accepts freeform tasks that include both discrete types and continuous parameters. Second, a cooperative motion planner continuously updates the robot’s trajectories to achieve the inferred tasks by repeatedly solving optimal control problems. The planner is designed to respond interactively to changes in the indicated task, avoid collisions in cluttered environments, handle time-varying objective functions, and achieve high-quality motions using a hybrid of numerical and sampling-based techniques. The system is applied to the problem of controlling a 6D robot manipulator using 2D mouse input in the context of two tasks: static target reaching and dynamic trajectory tracking. Simulations suggest that it enables the robot to reach intended targets faster and to track intended trajectories more closely than comparable techniques.     

Real coded chemical reaction based optimization for short-term hydrothermal scheduling

This paper presents a real coded chemical reaction based (RCCRO) algorithm to solve the short-term hydrothermal scheduling (STHS) problem. Hydrothermal system is highly complex and related with every problem variables in a nonlinear way. The objective of the hydro thermal scheduling is to determine the optimal hourly schedule of power generation for different hydrothermal power system for certain intervals of time such that cost of power generation is minimum. Chemical reaction optimization mimics the interactions of molecules in term of chemical reaction to reach a low energy stable state. A real coded version of chemical reaction optimization, known as real-coded chemical reaction optimization (RCCRO) is considered here. To check the effectiveness of the RCCRO, 3 different test systems are considered and mathematical remodeling of the algorithm is done to make it suitable for solving short-term hydrothermal scheduling problem. Simulation results confirm that the proposed approach outperforms several other existing optimization techniques in terms quality of solution obtained and computational efficiency. Results also establish the robustness of the proposed methodology to solve STHS problems.     

Fault coverage modeling in nonlinear dynamical systems

In this paper, we propose an approach for modeling fault coverage in nonlinear dynamical systems. Fault coverage gives a measure of the likelihood that a system will be able to recover after a fault occurrence. In our setup, the system dynamics are described by a standard state-space model. The system input (disturbance) is considered to be unknown but bounded at all times. Before any fault occurrence, the vector field governing the system dynamics is such that, for any possible input signal, the corresponding system reach set is contained in some region of the state space defined by the system performance requirements. When a fault occurs, the vector field that governs the system dynamics might be altered. Fault coverage is defined as the probability that, given a fault has occurred, the system trajectories remain, at all times, within the region of the state-space defined by the performance requirements. Input-to-state stability (ISS) concepts are used to compute estimates of the proposed coverage model. Several examples are discussed in order to illustrate the proposed modeling approach.     

From objects to services: toward a stepwise migration approach for Java applications

Migrating legacy applications toward service-oriented systems is a hard task complicated by the lack of appropriate approaches and tools. In this paper, a stepwise approach is proposed to migrate a Java application into an equivalent application composed of a set of Web services invoked by an orchestrator. In each migration step, a portion of the target application is identified and migrated into a Web service. In this approach, the role of testing is central since after each migration step the new service-oriented application is tested with the aim of checking “its equivalence” with the original version. An experiment based on four Java applications has been conducted to tune the approach and evaluate applicability and effort involved in the migration process. The obtained results confirm the viability of the proposed approach and highlight some encountered SOA migration difficulties.     

Model predictive control for constrained uncertain piecewise linear systems

For constrained piecewise linear (PWL) systems, the possible existing model uncertainty will bring the difficulties to the design approaches of model predictive control (MPC) based on mixed integer programming (MIP). This paper combines the robust method and hybrid method to design the MPC for PWL systems with structured uncertainty. For the proposed approach, as the system model is known at current time, a free control move is optimized to be the current control input. Meanwhile, the MPC controller uses a sequence of feedback control laws as the future control actions, where each feedback control law in the sequence corresponds to each partitions and the arbitrary switching technique is adopted to tackle all the possible switching. Furthermore, to reduce the online computational burden of MPC, the segmented design procedure is suggested by utilizing the characteristics of the proposed approach. Then, an offline design algorithm is proposed, and the reserved degree of freedom can be online used to optimize the control input with lower computational burden.