No Arabic abstract
This paper investigates bilateral control of teleoperators with closed architecture and subjected to arbitrary bounded time-varying delay. A prominent challenge for bilateral control of such teleoperators lies in the closed architecture, especially in the context not involving interaction force/torque measurement. This yields the long-standing situation that most bilateral control rigorously developed in the literature is hard to be justified as applied to teleoperators with closed architecture. With a new class of dynamic feedback, we propose kinematic and adaptive dynamic controllers for teleoperators with closed architecture, and we show that the proposed kinematic and dynamic controllers are robust with respect to arbitrary bounded time-varying delay. In addition, by exploiting the input-output properties of an inverted form of the dynamics of robot manipulators with closed architecture, we remove the assumption of uniform exponential stability of a linear time-varying system due to the adaptation to the gains of the inner controller in demonstrating stability of the presented adaptive dynamic control. The application of the proposed approach is illustrated by the experimental results using a Phantom Omni and a UR10 robot.
In this paper, we equip the conventional discrete-time queueing network with a Markovian input process, that, in addition to the usual short-term stochastics, governs the mid- to long-term behavior of the links between the network nodes. This is reminiscent of so-called Jump-Markov systems in control theory and allows the network topology to change over time. We argue that the common back-pressure control policy is inadequate to control such network dynamics and propose a novel control policy inspired by the paradigms of model-predictive control. Specifically, by defining a suitable but arbitrary prediction horizon, our policy takes into account the future network states and possible control actions. This stands in clear contrast to most other policies which are myopic, i.e. only consider the next state. We show numerically that such an approach can significantly improve the control performance and introduce several variants, thereby trading off performance versus computational complexity. In addition, we prove so-called throughput optimality of our policy which guarantees stability for all network flows that can be maintained by the network. Interestingly, in contrast to general stability proofs in model-predictive control, our proof does not require the assumption of a terminal set (i.e. for the prediction horizon to be large enough). Finally, we provide several illustrating examples, one of which being a network of synchronized queues. This one in particular constitutes an interesting system class, in which our policy exerts superiority over general back-pressure policies, that even lose their throughput optimality in those networks.
We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based evolving Modeling (SS-FBeM) approach. The resulting fuzzy model is structurally and parametrically developed from a data stream with focus on memory and data coverage. The fuzzy controller also evolves, based on the data instances and fuzzy model parameters. Its local gains are redesigned in real-time -- whenever the corresponding local fuzzy models change -- from the solution of a linear matrix inequality problem derived from a fuzzy Lyapunov function and bounded input conditions. We have shown one-step prediction and asymptotic stabilization of the Henon chaos.
This paper presents a method for controlling the voltage of inverter-based Microgrids by proposing a new scale-free distributed cooperative controller. The communication network is modeled by a general time-varying graph which enhances the resilience of the proposed protocol against communication link failure, data packet loss, and fast plug and play operation in the presence of arbitrarily communication delays. The proposed scale-free distributed cooperative controller is independent of any information about the communication system and the size of the network (i.e., the number of distributed generators). The stability analysis of the proposed protocol is provided. The proposed method is simulated on the CIGRE medium voltage Microgrid test system. The simulation results demonstrate the feasibility of the proposed scale-free distributed nonlinear protocol for regulating the voltage of Microgrids in presence of communication failures, data packet loss, noise, and degradation.
This paper introduces a closed-loop frequency analysis tool for reset control systems. To begin with sufficient conditions for the existence of the steady-state response for a closed-loop system with a reset element and driven by periodic references are provided. It is then shown that, under specific conditions, such a steady-state response for periodic inputs is periodic with the same period as the input. Furthermore, a framework to obtain the steady-state response and to define a notion of closed-loop frequency response, including high order harmonics, is presented. Finally, pseudo-sensitivities for reset control systems are defined. These simplify the analysis of this class of systems and allow a direct software implementation of the analysis tool. To show the effectiveness of the proposed analysis method the position control problem for a precision positioning stage is studied. In particular, comparison with the results achieved using methods based on the Describing Function shows that the proposed method achieves superior closed-loop performance.
This paper studies coordination problem for time-varying networks suffering from antagonistic information, quantified by scaling parameters. By such a manner, interacting property of the participating individuals and antagonistic information can be quantified in a fully decoupled perspective, thus benefiting from merely directed spanning tree hypothesis is needed, in the sense of usual algebraic graph theory. We start with rigorous argument on the existence of weighted gain, and then derive relation among weighted gain, scaling parameter and Laplacian matrix guaranteeing antagonistic information cannot diverge system state. Based on these arguments, we devise coordination algorithm constrained by topology-dependent average time condition, thus relaxing the examination of directed spanning tree requirement for the union graph that is usually intractable. Moreover, the induced theoretical results are applied to time-varying networks with several mutually uninfluenced agents, in accompanying with some discussions and comparisons with respect to the existing developments.