基于粒子滤波的欠驱动移动机器人多目标点跟踪控制

针对欠驱动移动机器人的多目标点跟踪问题,提出了一种基于粒子滤波的高精度跟踪控制方法;具体地,在考虑移动机器人采样噪声的情况下,首先利用粒子滤波对移动机器人的位置信息进行处理,得到精准可靠的移动机器人状态信息;在此基础上,根据欠驱动移动机器人的运动学模型以及目标点的分布状况,设计基于反馈控制的多目标点跟踪控制方法;相对于传统的欠驱动移动机器人目标点跟踪控制算法,改进了该控制方法中增益参数的约束条件,有效避免了移动机器人在接近目标点时产生的奇异现象,有效提高了移动机器人对目标点的跟踪精度;此外,分析了该目标点跟踪控制系统的稳定性,并通过数值仿真验证了所提方法的可行性与有效性.     

无机械辅助结构自行车机器人控制仿真及实现

为实现自行车机器人的平稳直线行驶,论证了无机械辅助结构、仅靠调整车把维持自平衡的后驱自行车机器人动力学建模、姿态控制、系统仿真及实物样机实验.针对具有典型对称性欠驱动非完整约束的自行车机器人系统难于实现平衡控制问题,首先基于拉格朗日方法分析系统力学机理,建立简化动力学模型.然后基于部分反馈线性化原理,对车体横滚角与转把力矩的欠驱动子系统进行线性化处理及模糊自适应控制.仿真及实验结果表明,有效地实现了自行车机器人直线运动自平衡控制,为进一步开展自行车机器人以及其他欠驱动系统平衡运动控制奠定理论基础.     

Virtual control strategy and conditioning technique for tracking and learning controls under input restrictions

The virtual control strategy for mechanical systems has been recently proposed (Gnucci and Marino, 2021) in the context of under-actuated mechanical systems. Such a strategy views and represents an under-actuated mechanical system as a fully actuated system with virtually added inputs and outputs having to satisfy, through a suitable choice of the virtual output reference signals, the virtual input zero-equality constraint: the related adaptive tracking control problem is then solved through standard design techniques. This paper exhibits a twofold aim. The first one is: to enlarge the concept of zero-input constraint and thus naturally adapt the virtual control approach to the case in which an actuator fault can occur. The second aim is: to show how the application and transposition of such an adaptation to two well-known classes of nonlinear systems (special systems in multi-variable tracking form with two inputs and outputs under actuator faults; one-relative-degree, single-input, single-output systems in output feedback form under input saturation) not only own strong connections with the conditioning technique, originally conceived in the context of anti-windup problems under input constraints, but they also gain original results.     

Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem

In this paper we discuss an online algorithm based on policy iteration for learning the continuous-time (CT) optimal control solution with infinite horizon cost for nonlinear systems with known dynamics. That is, the algorithm learns online in real-time the solution to the optimal control design HJ equation. This method finds in real-time suitable approximations of both the optimal cost and the optimal control policy, while also guaranteeing closed-loop stability. We present an online adaptive algorithm implemented as an actor/critic structure which involves simultaneous continuous-time adaptation of both actor and critic neural networks. We call this ‘synchronous’ policy iteration. A persistence of excitation condition is shown to guarantee convergence of the critic to the actual optimal value function. Novel tuning algorithms are given for both critic and actor networks, with extra nonstandard terms in the actor tuning law being required to guarantee closed-loop dynamical stability. The convergence to the optimal controller is proven, and the stability of the system is also guaranteed. Simulation examples show the effectiveness of the new algorithm.     

The Multi-scale Method for Solving Nonlinear Time Space Fractional Partial Differential Equations

In this paper, we present a new algorithm to solve a kind of nonlinear time space-fractional partial differential equations on a finite domain. The method is based on B-spline wavelets approximations, some of these functions are reshaped to satisfy on boundary conditions exactly. The Adams fractional method is used to reduce the problem to a system of equations. By multiscale method this system is divided into some smaller systems which have less computations. We get an approximated solution which is more accurate on some subdomains by combining the solutions of these systems. Illustrative examples are included to demonstrate the validity and applicability of our proposed technique, also the stability of the method is discussed.      

粗糙集近似与信息粒度

用粗糙集近似描述了三类常见信息系统(即Pawlak信息系统、不完备信息系统、不完备模糊信息系统)中对象的基本信息粒度.通过信息系统中对象属性值关于对象属性近似空问的上近似可以得到与对象具有相同或相似信息的对象集,即利用对象属性值关于对象属性近似空间的上近似将对象属性值信息变换成为对象的基本信息粒度. .所得结论对信息系统中基本信息粒度的物理意义有了比较清楚和更加合理的解释.     

Formation control of VTOL Unmanned Aerial Vehicles with communication delays

The formation control problem of a team of Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) with communication delays is addressed. Based on the extraction algorithm presented in Abdessameud and Tayebi (2010a), we propose a new design methodology that simplifies the design of formation control laws with delayed communication for this class of under-actuated systems. Three control schemes are presented that provide delay-dependent and delay-independent results with constant and time-varying communication delays. The stability of the overall closed loop system in each scheme is established using Lyapunov–Krasovskii functionals. The proposed design methodology achieves global results in terms of the position and removes the requirement of the linear-velocity measurements. Simulation results are provided to show the effectiveness of the proposed control schemes.     

GBF-CMAC和滑模控制的柔性结构系统控制

针对一类不确定系统的跟踪控制,设计了一种将GBF-CMAC（cerebellar model articulation controller with Gauss basis function）与滑模控制相结合的控制系统。利用符号距离和分层结构减少了神经网络所需存储器的数量,并提出了一种神经网络参数的自适应学习律。将设计的控制器用于含有不确定性和欠驱动结构的高阶柔性直线结构系统的跟踪控制,并与一般滑模控制和积分滑模控制进行了比较。实验结果表明,所设计的控制器不仅具有较好的鲁棒性,而且改善了滑模控制存在的抖振问题。同时通过调整神经网络的参数对抖振进行控制,实现了抖振和跟踪性能之间的最优选择。     

Waiting-time approximations for multiple-server polling systems

We consider multiple-server polling systems, in which each of the servers visits the queues according to its own cyclic schedule. Such systems appear to completely defy the derivation of exact waiting-time results, which motivates the search for accurate approximations. In the present paper, we derive waiting-time approximations for asymmetric systems with the exhaustive and gated service discipline. The approximations are tested for a wide range of parameter combinations.     

Computational Methods for Verification of Stochastic Hybrid Systems

Stochastic hybrid system (SHS) models can be used to analyze and design complex embedded systems that operate in the presence of uncertainty and variability. Verification of reachability properties for such systems is a critical problem. Developing sound computational methods for verification is challenging because of the interaction between the discrete and the continuous stochastic dynamics. In this paper, we propose a probabilistic method for verification of SHSs based on discrete approximations focusing on reachability and safety problems. We show that reachability and safety can be characterized as a viscosity solution of a system of coupled Hamilton-Jacobi-Bellman equations. We present a numerical algorithm for computing the solution based on discrete approximations that are derived using finite-difference methods. An advantage of the method is that the solution converges to the one for the original system as the discretization becomes finer. We also prove that the algorithm is polynomial in the number of states of the discrete approximation. Finally, we illustrate the approach with two benchmarks: a navigation and a room heater example, which have been proposed for hybrid system verification.     

Issues in closed-loop needle steering

Percutaneous needle insertion is amongst the most prevalent clinical procedures. The effectiveness of needle-base interventions heavily relies on needle targeting accuracy. However, the needle interacts with the surrounding tissue during insertion and deflects away from its intended trajectory. To overcome this problem, a significant research effort has been made towards developing robotic systems to automatically steer bevel-tipped needles percutaneously, which is a comprehensive and challenging control problem. A flexible needle inserted in soft tissue is an under-actuated system with nonholonomic constraints. Closed-loop feedback control of needle in tissue is challenging due to measurement errors, unmodelled dynamics created by tissue heterogeneity, and motion of targets within the tissue. In this paper, we review recent progress made in each of the complementary components that constitute a closed-loop needle steering system, including modelling needle-tissue interaction, sensing needle deflection, controlling needle trajectory, and hardware implementation.     

Generalized predictive control for non-uniformly sampled systems

In this paper, we study digital control systems with non-uniform updating and sampling patterns, which include multirate sampled-data systems as special cases. We derive lifted models in the state-space domain. The main obstacle for generalized predictive control (GPC) design using the lifted models is the so-called causality constraint. Taking into account this design constraint, we propose a new GPC algorithm, which results in optimal causal control laws for the non-uniformly sampled systems. The solution applies immediately to multirate sampled-data systems where rates are integer multiples of some base period.     

Analysis of cyclic service systems with limited service: Bounds and approximations

In this paper we analyze cyclic service queueing systems in which the server serves at most a prespecified number of jobs when it visits a queue. The system parameters are not required to be symmetric. Both exhaustive-limited and gated-limited disciplines are studied, which include well-known disciplines such as exhaustive, gated and serve-at-most-one as special cases. We derive tight bounds for the conservation laws in such systems. These bounds reduce to exact equalities for several known special cases. Then, simple approximations are proposed for the mean waiting times of jobs at each queue in such systems. Extensive numerical examples indicate that the accuracy of the approximations is well within 20% of the simulated results provided they are not extremely asymmetric and the switchover times are not large relative to the service times.     

Factorial Hidden Markov Models

Hidden Markov models (HMMs) have proven to be one of the most widely used tools for learning probabilistic models of time series data. In an HMM, information about the past is conveyed through a single discrete variable—the hidden state. We discuss a generalization of HMMs in which this state is factored into multiple state variables and is therefore represented in a distributed manner. We describe an exact algorithm for inferring the posterior probabilities of the hidden state variables given the observations, and relate it to the forward–backward algorithm for HMMs and to algorithms for more general graphical models. Due to the combinatorial nature of the hidden state representation, this exact algorithm is intractable. As in other intractable systems, approximate inference can be carried out using Gibbs sampling or variational methods. Within the variational framework, we present a structured approximation in which the the state variables are decoupled, yielding a tractable algorithm for learning the parameters of the model. Empirical comparisons suggest that these approximations are efficient and provide accurate alternatives to the exact methods. Finally, we use the structured approximation to model Bach's chorales and show that factorial HMMs can capture statistical structure in this data set which an unconstrained HMM cannot.     

Multi-player non-zero-sum games: Online adaptive learning solution of coupled Hamilton–Jacobi equations

In this paper we present an online adaptive control algorithm based on policy iteration reinforcement learning techniques to solve the continuous-time (CT) multi player non-zero-sum (NZS) game with infinite horizon for linear and nonlinear systems. NZS games allow for players to have a cooperative team component and an individual selfish component of strategy. The adaptive algorithm learns online the solution of coupled Riccati equations and coupled Hamilton–Jacobi equations for linear and nonlinear systems respectively. This adaptive control method finds in real-time approximations of the optimal value and the NZS Nash-equilibrium, while also guaranteeing closed-loop stability. The optimal-adaptive algorithm is implemented as a separate actor/critic parametric network approximator structure for every player, and involves simultaneous continuous-time adaptation of the actor/critic networks. A persistence of excitation condition is shown to guarantee convergence of every critic to the actual optimal value function for that player. A detailed mathematical analysis is done for 2-player NZS games. Novel tuning algorithms are given for the actor/critic networks. The convergence to the Nash equilibrium is proven and stability of the system is also guaranteed. This provides optimal adaptive control solutions for both non-zero-sum games and their special case, the zero-sum games. Simulation examples show the effectiveness of the new algorithm.     

Improved Algorithms for Polynomial-Time Decay and Time-Decay with Additive Error

We consider the problem of maintaining polynomial and exponential decay aggregates of a data stream, where the weight of values seen from the stream diminishes as time elapses. These types of aggregation were discussed by Cohen and Strauss (J. Algorithms 1(59), 2006), and can be used in many applications in which the relative value of streaming data decreases since the time the data was seen. Some recent work and space efficient algorithms were developed for time-decaying aggregations, and in particular polynomial and exponential decaying aggregations. All of the work done so far has maintained multiplicative approximations for the aggregates. In this paper we present the first O(log N) space algorithm for the polynomial decay under a multiplicative approximation, matching a lower bound. In addition, we explore and develop algorithms and lower bounds for approximations allowing an additive error in addition to the multiplicative error. We show that in some cases, allowing an additive error can decrease the amount of space required, while in other cases we cannot do any better than a solution without additive error.     

Robust control of a class of under-actuated mechanical systems with model uncertainty

Robust control of under-actuated mechanical systems (UMSs) with model uncertainty is still a challenging problem. For UMSs, the model parametric uncertainties make it difficult to precisely calculate the isolated equilibrium point corresponding to a fixed input. Without an accurate destination state, many set-point control methods cannot eliminate the positioning errors. An improved sliding mode control (ISMC) method is proposed to solve the robust control problem for a class of UMSs with model uncertainty and input disturbance. A balance variable is introduced in the sliding surface design to compensate for the disturbance caused by the inaccurate destination state, and the ISMC method is proposed to make the system state reach the sliding surface in finite time. Linear matrix inequality approach and particle swarm optimisation algorithm are applied to design the sliding mode surface parameters. The simulation results on an UMS are presented to show the effectiveness of the proposed scheme.     

A novel image encryption algorithm based on piecewise linear chaotic maps and least squares approximation

In this paper, we propose a novel image encryption algorithm based on chaotic maps and least squares approximations. The proposed algorithm consists of two main phases, which are applied sequentially in several rounds, namely a shuffling phase and a masking phase. Both phases are based on 1–dimensional piecewise linear chaotic maps and act on the rows/columns of the input plain image. Least squares approximations are used to strengthen the security of the proposed algorithm by providing strong mixing between the rows/columns of the image. Simulation results show that the proposed image encryption algorithm is robust against common statistical and security attacks. We present thorough comparison of the proposed algorithm with some existing image encryption algorithms.

     

带输入饱和的欠驱动吊车非线性信息融合控制

针对欠驱动吊车系统的控制问题,提出了一种非线性信息融合控制方法. 通过融合二次型性能指标函数中包含的未来参考轨迹和控制能量的软约束信息,以及吊车系统状态方程和输出方程的硬约束信息,获得协状态和控制量的最优估计. 针对控制量输入饱和的问题,提出了一种控制能量软约束信息自适应调节算法,使求出的控制量满足限制要求. 信息融合控制方法基于被控对象的离散模型设计,具有易于实现的特点. 仿真结果表明了该方法的有效性.     

离散非线性系统的迭代学习轨迹跟踪鲁棒算法优化及应用

针对一类存在随机输入状态扰动、输出扰动及系统初值与给定期望值不严格一致的离散非线性重复系统,提出了一种P型开闭环鲁棒迭代学习轨迹跟踪控制算法．基于λ范数理论证明了算法的严格鲁棒稳定性,并通过多目标函数性能指标优化P型开闭环迭代学习控制律的增益矩阵参数,保证了优化算法下系统输出期望轨迹跟踪误差的单调收敛性,达到提高学习算法收敛速度和跟踪精度的目的．最后应用于二维运动移动机器人的实例仿真,验证了本文算法的可行性和有效性．