Robust filtering for discrete-time systems with saturation and its application to transmultiplexers

This paper considers the problem of robust filtering for discrete-time linear systems subject to saturation. A generalized dynamic filter architecture is proposed, and a filter design method is developed. Our approach incorporates the conventional linear H/sub 2/ and H/sub /spl infin// filtering as well as a regional l/sub 2/ gain filtering feature developed specially for the saturation nonlinearity and is applicable to the digital transmultiplexer systems for the purpose of separating filterbank design. It turns out that our filter design can be carried out by solving a constrained optimization problem with linear matrix inequality (LMI) constraints. Simulations show that the resultant separating filters possess satisfactory reconstruction performance while working in the linear range and less degraded reconstruction performance in the presence of saturation.     

Robust Fault Detection of Markovian Jump Systems

This paper deals with the problem of observer-based robust fault detection for both continuous- and discrete-time linear Markovian jump systems with an unknown input. By using a fault detection filter as residual generator and a proposed performance index as objective function, the robust fault detection filter design is formulated as an optimization problem. A sufficient condition to solve this problem is established in terms of the feasibility of certain matrix inequalities, which can be solved via iterative linear matrix inequality (LMI) algorithms. The residual evaluation problem is also considered. A numerical example is given to illustrate the effectiveness of the proposed techniques.     

多策略集成的樽海鞘群算法的机器人路径规划

针对求解机器人路径规划问题,本文提出了一种多策略集成的樽海鞘群算法.在该算法中,提出了新的自适应领导者结构,以平衡算法的探索和开发能力;引入可以提高Lyapunov指数的Logistic-Cubic级联混沌映射作为食物源的扰动算子,来避免算法陷入局部最优;采用基于自适应参数的分散觅食策略使部分追随者探索有前景的区域.在CEC 2014测试集的多种函数上,本文算法与3种改进的樽海鞘群算法和5种先进的群智能算法进行比较,结果表明本文算法综合优化性能更好.本文算法2将其用于求解机器人路径规划问题,其中用三次样条插值对路径进行平滑.在障碍是8,9,13的环境下分别进行仿真实验,仿真结果表明,本文算法在给定的仿真场景下与给定的对比算法相比获得了最好的结果.     

Robot path planner based on deep reinforcement learning and the seeker optimization algorithm

Path planning is one of the key technologies for mobile robot applications. However, the traditional robot path planner has a slow planning response, which leads to a long navigation completion time. In this paper, we propose a novel robot path planner (SOA+A2C) that produces global and local path planners with the seeker optimization algorithm (SOA) and the advantage actor-critic (A2C) algorithm, respectively. In addition, to solve the problems of poor convergence performance when training deep reinforcement learning (DRL) agents in complex path planning tasks and path redundancy when metaheuristic algorithms, such as SOA, are used for path planning, we propose the incremental map training method and path de-redundancy method. Simulation results show that first, the incremental map training method can improve the convergence performance of the DRL agent in complex path planning tasks. Second, the path de-redundancy method can effectively alleviate path redundancy without sacrificing the search capability of the metaheuristic algorithm. Third, the SOA+A2C path planner is superior to the Dijkstra & dynamic window approach (Dijkstra+DWA) and the Dijkstra & timed elastic band (Dijkstra+TEB) path planners provided by the robot operating system (ROS) in terms of path length, path planning response time, and navigation completion time. Therefore, the developed SOA+A2C path planner can serve as an effective tool for mobile robot path planning.     

Gait Synthesis and Sensory Control of Stair Climbing for a Humanoid Robot

Stable and robust walking in various environments is one of the most important abilities for a humanoid robot. This paper addresses walking pattern synthesis and sensory feedback control for humanoid stair climbing. The proposed stair-climbing gait is formulated to satisfy the environmental constraint, the kinematic constraint, and the stability constraint; the selection of the gait parameters is formulated as a constrained nonlinear optimization problem. The sensory feedback controller is phase dependent and consists of the torso attitude controller, zero moment point compensator, and impact reducer. The online learning scheme of the proposed feedback controller is based on a policy gradient reinforcement learning method, and the learned controller is robust against external disturbance. The effectiveness of our proposed method was confirmed by walking experiments on a 32-degree-of-freedom humanoid robot.     

Cooperative spectrum sensing in cognitive radio network using multiobjective evolutionary algorithms and fuzzy decision making

The cognitive radio has emerged as a potential solution to the problem of spectrum scarcity. Spectrum sensing unit in cognitive radio deals with the reliable detection of primary user’s signal. Cooperative spectrum sensing exploits the spatial diversity between cognitive radios to improve sensing accuracy. The selection of the weight assigned to each cognitive radio and the global decision threshold can be formulated as a constrained multiobjective optimization problem where probabilities of false alarm and detection are the two conflicting objectives. This paper uses evolutionary algorithms to solve this optimization problem in a multiobjective framework. The simulation results offered by different algorithms are assessed and compared using three performance metrics. This study shows that our approach which is based on the concept of cat swarm optimization outperforms other algorithms in terms of quality of nondominating solutions and efficient computation. A fuzzy logic based strategy is used to find out a compromise solution from the set of nondominated solutions. Different tests are carried out to assess the stability of the simulation results offered by the heuristic evolutionary algorithms. Finally the sensitivity analysis of different parameters is performed to demonstrate their impact on the overall performance of the system.     

Optimization of UPFCs using hierarchical multi-objective optimization algorithms

Optimal location, number, and settings of unified power flow controllers (UPFC) using various multi-objective optimization algorithms is presented in this paper. The UPFC parameters, locations and number are computed to maximize the voltage stability margin and minimize the real power losses at the same time. For this, developed hierarchical optimization versions of three recent multi-objective algorithms are proposed namely: non-dominated genetic algorithms (NSGA-II), non-dominated sorting particle swarm optimization (NSPSO) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The fuzzy logic is proposed to extract the best compromise solution from the Pareto set. The proposed algorithms are applied to IEEE 30-bus power system. The line flow and load bus voltage limits are taken into account. The obtained results show that the installation of the UPFC in the power system minimizes the power losses, enhances the static voltage stability, and improves the voltage profiles. Furthermore, the proposed methods are able to solve a hard discrete?Ccontinuous constrained multi-objective optimization problem. In addition, they do not show any limitation on the number of objective functions under consideration.     

Two Techniques for Fast Computation of Constrained Shortest Paths

Computing constrained shortest paths is fundamental to some important network functions such as QoS routing, MPLS path selection, ATM circuit routing, and traffic engineering. The problem is to find the cheapest path that satisfies certain constraints. In particular, finding the cheapest delay-constrained path is critical for real-time data flows such as voice/video calls. Because it is NP-complete, much research has been designing heuristic algorithms that solve the epsiv-approximation of the problem with an adjustable accuracy. A common approach is to discretize (i.e., scale and round) the link delay or link cost, which transforms the original problem to a simpler one solvable in polynomial time. The efficiency of the algorithms directly relates to the magnitude of the errors introduced during discretization. In this paper, we propose two techniques that reduce the discretization errors, which allows faster algorithms to be designed. Reducing the overhead of computing constrained shortest paths is practically important for the successful design of a high-throughput QoS router, which is limited at both processing power and memory space. Our simulations show that the new algorithms reduce the execution time by an order of magnitude on power-law topologies with 1000 nodes. The reduction in memory space is similar.     

The Application of ODE Method in Constrained Optimization

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基于无线供能反向散射通信系统安全性的稳健机会约束优化算法设计

针对无线供能反向散射通信系统中信道不确定性以及安全性问题,提出一种考虑系统安全性的稳健机会约束优化算法.首先,在考虑窃听数据传输速率约束、最小能量收集约束和设备反射率约束条件下,建立一个基于信道不确定性的最大化系统传输速率最小值的优化问题.为求解该问题,应用基于伯恩斯坦型不等式的安全近似方法,将原不确定性问题转换为确定...     

WLAN中合作式功率控制算法研究

在WLAN中,延迟有限,网络整体消耗能量最小化的功率控制算法研究较少。对于该问题进行建模分析,通过理论推导,将该非凸优化问题转化成广义几何规划问题。经过分析,将问题近似为线性规划,并给出全局优化算法。对于WLAN中,多个无线接入设备在一定传输时间内的信道情况进行预测,给出延迟有限能量最小化的集中式在线算法。实验结果显示,采用提出的信道预测方法,在线算法的计算结果与集中式算法结果较为接近。     

A new approach to the optimization of robust antenna arrayprocessors

The authors present a method for solving a class of optimization problems with nonsmooth constraints. In particular, they apply the method to the design of narrowband minimum-power antenna array processors which are robust in the presence of errors such as array element placement, look direction misalignment, and frequency offset. They first show that the constrained minimum power problem has a unique global minimum provided that the constraint set is nonempty. Then it is shown how the design problem derived directly from considerations of the sensitivity of the antenna array processor to errors can be transformed into a quadratic programming problem with linear inequality constraints which can be solved efficiently by the standard active set strategy. They also present numerical results for two types of nonsmooth constraints developed to provided robustness. These results confirm the effectiveness of the method     

A distributed approach for access and visibility task with a manikin and a robot in a virtual reality environment

This paper presents a new method, based on a multi-agent system and on a digital mock-up technology, to assess an efficient path planner for a manikin or a robot for an access and visibility task taking into account ergonomic constraints or joint and mechanical limits. In order to solve this problem, the human operator is integrated in the process optimization to contribute to a global perception of the environment. This operator cooperates, in real time, with several automatic local elementary agents. The result of this work validates solutions through the digital mock-up; it can be applied to simulate maintainability and mountability tasks.     

无线传感器网络中新的最小暴露路径问题及其求解算法

无线传感器网络中原始的最小暴露路径问题没有考虑对路径的实际限制条件,提出一种要求经过某一特别保护区域部分边界的最小暴露路径问题。由于无法建立相应的图模型,原有求解最小暴露路径问题的经典方法（网格法和维诺图法）对提出的新问题不再起效。先将该问题转化成带约束条件的优化问题,然后针对转化后的数学模型高度非线性、高维度而不好用确定性优化方法的特点,结合问题实际背景设计出混合人工蜂群求解算法。通过在多种情况下的仿真实验发现,设计的带约束条件优化模型和混合人工蜂群求解算法能有效解决提出的最小暴露路径问题。     

Robust H/sub 2/ filtering for a class of systems with stochastic nonlinearities

This paper addresses the robust H/sub 2/ filtering problem for a class of uncertain discrete-time nonlinear stochastic systems. The nonlinearities described by statistical means in this paper comprise some well-studied classes of nonlinearities in the literature. A technique is developed to tackle the matrix trace terms resulting from the nonlinearities, and the well-known S-procedure technique is adopted to cope with the uncertainties. A unified framework is established to solve the addressed robust H/sub 2/ filtering problem by using a linear matrix inequality approach. A numerical example is provided to illustrate the usefulness of the proposed method.     

动平台分布式雷达系统协同跟踪路径优化算法

本文针对动平台分布式雷达系统协同跟踪目标的路径优化问题,提出了一种新颖的基于费歇尔信息矩阵的动平台分布式雷达系统协同跟踪路径优化算法。首先,本文建立了动平台分布式雷达系统协同跟踪目标模型,进而基于贝叶斯理论导出了闭合形式的费歇尔信息矩阵,然后采用D最优准则建立了动平台分布式雷达系统路径优化代价函数,最后提出了一种基于最速下降的方法来解决该优化问题。另外,本文还研究了有约束的动平台分布式雷达系统路径优化问题,利用惩罚函数来修正代价函数来规避障碍,并应用物理约束来限制动平台的转弯角。数值仿真结果验证了所推导的闭式代价函数的精确性和有效性,且所提出的动平台分布式雷达系统路径优化方法可以快速实时的以较为平滑的路径跟踪静止和移动目标;此外,相比于栅格搜索法,所提方法在计算复杂度上具有明显优势。      

Global minimum-jerk trajectory planning of robot manipulators

A new approach based on interval analysis is developed to find the global minimum-jerk (MJ) trajectory of a robot manipulator within a joint space scheme using cubic splines. MJ trajectories are desirable for their similarity to human joint movements and for their amenability to path tracking and to limit robot vibrations. This makes them attractive choices for robotic applications, in spite of the fact that the manipulator dynamics are not taken into account. Cubic splines are used in a framework that assures overall continuity of velocities and accelerations in the robot movement. The resulting MJ trajectory planning is shown to be a global constrained minimax optimization problem. This is solved by a newly devised algorithm based on interval analysis and proof of convergence with certainty to an arbitrarily good global solution is provided. The proposed planning method is applied to an example regarding a six-joint manipulator and comparisons with an alternative MJ planner are exposed     

Base Station Placement for Dynamic Traffic Load Using Evolutionary Algorithms

In this paper, dynamic traffic load is considered to determine optimal location of base station (BS) using evolutionary optimization algorithms. The various parameters such as site coordinates (x, y), transmitting power, height and tilt are taken as design parameters for BS placement. Coverage maximization and cost minimization are considered as two conflicting objectives with inequality constraints such as handover, traffic demand and overlap. RGA and MNSGA-II algorithms are used to solve single objective and multiobjective BS placement problem respectively. A $2 \times 2\, \text{ km}^{2}$ synthetic test system is discretized as hexagonal cell structure for simulation purposes. Receiving field strength for all service testing points is calculated using simulations and path loss is calculated using Hata model. In dynamic traffic model, both vehicle and pedestrian movements in up and side directions are considered. Dynamic movement is achieved by randomly moving vehicles and pedestrians for a fixed speed in each sample time. The results show that the RGA is able to determine the optimal BS location after considering the dynamic traffic load and satisfying inequality constraints for both coverage maximization and cost objectives. MNSGA-II algorithm gives well distributed pareto-front for the multiobjective BS placement in single simulation run. The simulation results reveal that the proposed dynamic traffic model is suitable for the real world BS placement problem.     

Robust Linear Receivers for Multiaccess Space–Time Block-Coded MIMO Systems: A Probabilistically Constrained Approach

Traditional multiuser receiver algorithms developed for multiple-input–multiple-output (MIMO) wireless systems are based on the assumption that the channel state information (CSI) is precisely known at the receiver. However, in practical situations, the exact CSI may be unavailable because of channel estimation errors and/or outdated training. In this paper, we address the problem of robustness of multiuser MIMO receivers against imperfect CSI and propose a new linear technique that guarantees the robustness against CSI errors with a certain selected probability. The proposed receivers are formulated as probabilistically constrained stochastic optimization problems. Provided that the CSI mismatch is Gaussian, each of these problems is shown to be convex and to have a unique solution. The fact that the CSI mismatch is Gaussian also enables to convert the original stochastic problems to a more tractable deterministic form and to solve them using the second-order cone programming approach. Numerical simulations illustrate an improved robustness of the proposed receivers against CSI errors and validate their better flexibility as compared with the robust multiuser MIMO receivers based on the worst case designs.     

A stable transition controller for constrained robots

This paper addresses the problem of contact transition from free motion to constrained motion for robots. Stability of transition from free motion to constrained motion is essential for successful operation of a robot performing general tasks such as surface following and surface finishing. Uncertainty in the location of the constraint can cause the robot to impact the constraint surface with a nonzero velocity, which may lead to bouncing of the robot end-effector on the surface. A new stable discontinuous transition controller is proposed to deal with contact transition problem. This discontinuous transition control algorithm is used when switching from free motion to constrained motion. Control algorithm for a complete robot task is developed. Extensive experiments with the proposed control strategy were conducted with different levels of constraint uncertainty and impact velocities. Experimental results show much improved transition performance and force regulation with the proposed controller. Details of the experimental platform and typical experimental results are given