Digital versus analog control of bilateral teleoperation systems: A task performance comparison

Controller discretization has the potential to jeopardize the stability of a bilateral teleoperation system. As reported in the literature, stability conditions impose bounds on the gains of the discrete-time controller and the sampling period and also a trade-off between the two. This paper shows a choice of task for which large sampling periods, necessitating low control gains for maintaining stability, lead to low teleoperation transparency and unacceptable task performance. It continues to show that users can successfully perform the same task if the controller is implemented using analog components. This highlights the advantages of analog haptics in tasks involving the display of highly stiff environments. The paper also highlights the constraints in designing analog haptic teleoperation controllers and proposes design guidelines to address them.     

Multiple integral inequalities and stability analysis of time delay systems

This paper is devoted to stability analysis of continuous-time delay systems based on a set of Lyapunov–Krasovskii functionals. New multiple integral inequalities are derived that involve the famous Jensen’s and Wirtinger’s inequalities, as well as the recently presented Bessel–Legendre inequalities of Seuret and Gouaisbaut (2015) and the Wirtinger-based multiple-integral inequalities of Park et al. (2015) and Lee et al. (2015). The present paper aims at showing that the proposed set of sufficient stability conditions can be arranged into a bidirectional hierarchy of LMIs establishing a rigorous theoretical basis for comparison of conservatism of the investigated methods. Numerical examples illustrate the efficiency of the method.     

可调节型低抖动时钟占空比稳定电路的设计

介绍了一种用于高速流水线ADC双沿采样的时钟占空比稳定电路。在传统占空比稳定电路的基础上,增加含连续时间积分器的反馈环路,并设计了时钟周期检测电路,同时可通过SPI配置积分器的参考电压,在片外调节芯片制造过程中产生的误差,并在前端增设一个高增益带宽时钟放大器,用来放大幅度很小(Vp-p<100 mV)的差分输入时钟信号。电路采用0.18 μm 1.8 V 1P5M CMOS工艺,可对频率范围为50~250 MHz、占空比范围为10% ~ 90%的输入时钟进行稳定调节,时钟峰-峰值抖动约为0.3 ps @ 250 MHz。     

Distributed predictive control of continuous-time systems

In the last years focus has been put in the development of distributed Model Predictive Control (MPC) algorithms. With a few exceptions, they have been mostly developed in the discrete-time framework. However, discretization of large-scale systems may destroy the sparsity of the original continuous-time models, making distributed control design and implementation more difficult. Also, more in general, discrete-time control of continuous-time systems does not allow to consider the process inter-sampling behavior. In this paper we present a novel non-cooperative distributed predictive control algorithm for continuous-time systems based on robust MPC concepts. The convergence properties of the proposed control scheme are stated, and its realizability is tested through a simulation case study.     

Distributed receding horizon control of large-scale nonlinear systems: Handling communication delays and disturbances

This paper studies the robust distributed receding horizon control (DRHC) problem for large-scale continuous-time nonlinear systems subject to communication delays and external disturbances. A dual-mode robust DRHC strategy is designed to deal with the communication delays and the external disturbances simultaneously. The feasibility of the proposed DRHC and the stability of the closed-loop system are analyzed, and the sufficient conditions for ensuring the feasibility and stability are developed, respectively. We show that: (1) the feasibility is affected by the bounds of external disturbances, the sampling period and the bound of communication delays; (2) the stability is related to the bounds of external disturbances, the sampling period, the bound of communication delays and the minimum eigenvalues of the cooperation matrices; (3) the closed-loop system is stabilized into a robust invariant set under the proposed conditions. A simulation study is conducted to verify the theoretical results.     

Third-order cumulants based methods for continuous-time errors-in-variables model identification

In this paper, the problem of identifying stochastic linear continuous-time systems from noisy input/output data is addressed. The input of the system is assumed to have a skewed probability density function, whereas the noises contaminating the data are assumed to be symmetrically distributed. The third-order cumulants of the input/output data are then (asymptotically) insensitive to the noises, that can be coloured and/or mutually correlated. Using this noise-cancellation property two computationally simple estimators are proposed. The usefulness of the proposed algorithms is assessed through a numerical simulation.     

Orthonormal Basis Functions for Continuous-Time Systems and Lp Convergence

In this paper, model sets for linear-time-invariant continuous-time systems that are spanned by fixed pole orthonormal bases are investigated. These bases generalize the well-known Laguerre and two-parameter Kautz cases. It is shown that the obtained model sets are everywhere dense in the Hardy space H ₁(Π) under the same condition as previously derived by the authors for the denseness in the (Π is the open right half plane) Hardy spaces H _p(Π), 1<p<∞. As a further extension, the paper shows how orthonormal model sets, that are everywhere dense in H _p(Π), 1≤p<∞, and which have a prescribed asymptotic order, may be constructed. Finally, it is established that the Fourier series formed by orthonormal basis functions converge in all spaces H _p(Π) and (D is the open unit disk) H _p(D), 1<p<∞. The results in this paper have application in system identification, model reduction, and control system synthesis. Date received: June 16, 1998. Date revised February 4, 1999.     

Maximal solution to algebraic Riccati equations linked to infinite Markov jump linear systems

In this paper, we deal with a perturbed algebraic Riccati equation in an infinite dimensional Banach space. Besides the interest in its own right, this class of equations appears, for instance, in the optimal control problem for infinite Markov jump linear systems (from now on iMJLS). Here, infinite or finite has to do with the state space of the Markov chain being infinite countable or finite (see Fragoso and Baczynski in SIAM J Control Optim 40(1):270–297, 2001). By using a certain concept of stochastic stability (a sort of L ₂-stability), we prove the existence (and uniqueness) of maximal solution for this class of equation and provide a tool to compute this solution recursively, based on an initial stabilizing controller. When we recast the problem in the finite setting (finite state space of the Markov chain), we recover the result of de Souza and Fragoso (Syst Control Lett 14:233–239, 1999) set to the Markovian jump scenario, now free from an inconvenient technical hypothesis used there, originally introduced in Wonham in (SIAM J Control 6(4):681–697). Research supported by grants CNPq 520367-97-9, 300662/2003-3 and 474653/2003-0, FAPERJ 171384/2002, PRONEX and IM-AGIMB.     

Development of a Novel HVDC System Model for Control Design

The operation of HVDC converters is discrete in nature. It is, therefore, proper to take into account the discrete behavior of converter operation while developing the system model for small signal stability analysis. This paper presents the development of such a system model, which also explicitly includes the effect of phase angle of respective AC bus voltages. A modular approach has been adopted for the formulation of complete state space system model. Utilizing the system model, the stability is examined in the controller parameter plane using eigenvalue analysis. The stability domains thus obtained are validated through detailed dynamic digital simulation.     

Hybridizing discrete- and continuous-time models for batch sizing and scheduling problems

This paper proposes a new hybrid technique called “partial parameter uniformization” (hereafter PPU). The technique simplifies problems by ignoring the different values that certain problem parameters can take, which may facilitate the solution of some hard combinatorial optimization problems. PPU is applied to complex batch sizing and scheduling problems. Some information can be obtained from a discrete-time model in which job durations have been made uniform. This information is then exploited by a more detailed continuous-time model to generate feasible solutions and further improve these solutions. Good, or optimal solutions to the Westenberger and Kallrath Benchmark problems have been obtained in this way, at relatively low computational cost, as have solutions to the newer problems of Blömer and Günther.