2,070 research outputs found
Time-and event-driven communication process for networked control systems: A survey
Copyright © 2014 Lei Zou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In recent years, theoretical and practical research topics on networked control systems (NCSs) have gained an increasing interest from many researchers in a variety of disciplines owing to the extensive applications of NCSs in practice. In particular, an urgent need has arisen to understand the effects of communication processes on system performances. Sampling and protocol are two fundamental aspects of a communication process which have attracted a great deal of research attention. Most research focus has been on the analysis and control of dynamical behaviors under certain sampling procedures and communication protocols. In this paper, we aim to survey some recent advances on the analysis and synthesis issues of NCSs with different sampling procedures (time-and event-driven sampling) and protocols (static and dynamic protocols). First, these sampling procedures and protocols are introduced in detail according to their engineering backgrounds as well as dynamic natures. Then, the developments of the stabilization, control, and filtering problems are systematically reviewed and discussed in great detail. Finally, we conclude the paper by outlining future research challenges for analysis and synthesis problems of NCSs with different communication processes.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374127, and 61374010, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Adaptive Control By Regulation-Triggered Batch Least-Squares Estimation of Non-Observable Parameters
The paper extends a recently proposed indirect, certainty-equivalence,
event-triggered adaptive control scheme to the case of non-observable
parameters. The extension is achieved by using a novel Batch Least-Squares
Identifier (BaLSI), which is activated at the times of the events. The BaLSI
guarantees the finite-time asymptotic constancy of the parameter estimates and
the fact that the trajectories of the closed-loop system follow the
trajectories of the nominal closed-loop system ("nominal" in the sense of the
asymptotic parameter estimate, not in the sense of the true unknown parameter).
Thus, if the nominal feedback guarantees global asymptotic stability and local
exponential stability, then unlike conventional adaptive control, the newly
proposed event-triggered adaptive scheme guarantees global asymptotic
regulation with a uniform exponential convergence rate. The developed adaptive
scheme is tested to a well-known control problem: the state regulation of the
wing-rock model. Comparisons with other adaptive schemes are provided for this
particular problem.Comment: 29 pages, 12 figure
A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information
Copyright q 2012 Hongli Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out. © 2012 Hongli Dong et al.This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61134009, 61273201, 61021002, and 61004067, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Science Foundation of the USA under Grant No. HRD-1137732, and the Alexander von Humboldt Foundation of German
Online Optimization of Switched LTI Systems Using Continuous-Time and Hybrid Accelerated Gradient Flows
This paper studies the design of feedback controllers that steer the output
of a switched linear time-invariant system to the solution of a possibly
time-varying optimization problem. The design of the feedback controllers is
based on an online gradient descent method, and an online hybrid controller
that can be seen as a regularized Nesterov's accelerated gradient method. Both
of the proposed approaches accommodate output measurements of the plant, and
are implemented in closed-loop with the switched dynamical system. By design,
the controllers continuously steer the system output to an optimal trajectory
implicitly defined by the time-varying optimization problem without requiring
knowledge of exogenous inputs and disturbances. For cost functions that are
smooth and satisfy the Polyak-Lojasiewicz inequality, we demonstrate that the
online gradient descent controller ensures uniform global exponential stability
when the time-scales of the plant and the controller are sufficiently separated
and the switching signal of the plant is slow on the average. Under a strong
convexity assumption, we also show that the online hybrid Nesterov's method
guarantees tracking of optimal trajectories, and outperforms online controllers
based on gradient descent. Interestingly, the proposed hybrid accelerated
controller resolves the potential lack of robustness suffered by standard
continuous-time accelerated gradient methods when coupled with a dynamical
system. When the function is not strongly convex, we establish global practical
asymptotic stability results for the accelerated method, and we unveil the
existence of a trade-off between acceleration and exact convergence in online
optimization problems with controllers using dynamic momentum. Our theoretical
results are illustrated via different numerical examples
Hybrid Attitude Control and Estimation On SO(3)
This thesis presents a general framework for hybrid attitude control and estimation design on the Special Orthogonal group SO(3). First, the attitude stabilization problem on SO(3) is considered. It is shown that, using a min-switch hybrid control strategy designed from a family of potential functions on SO(3), global exponential stabilization on SO(3) can be achieved when this family of potential functions satisfies certain properties. Then, a systematic methodology to construct these potential functions is developed. The proposed hybrid control technique is applied to the attitude tracking problem for rigid body systems. A smoothing mechanism is proposed to filter out the discrete behaviour of the hybrid switching mechanism leading to control torques that are continuous.
Next, the problem of attitude estimation from continuous body-frame vector measurements of known inertial directions is considered. Two hybrid attitude and gyro bias observers designed directly on SO(3) are proposed. The first observer uses a set of innovation terms and a switching mechanism that selects the appropriate innovation term. The second observer uses a fixed innovation term and allows the attitude state to be reset (experience discrete transition or jump) to an adequately chosen value on SO(3). Both hybrid observers guarantee global exponential stability of the zero estimation errors.
Finally, in the case where the body-frame vector measurements are intermittent, an event-triggered attitude estimation scheme on SO(3) is proposed. The observer consists in integrating the continuous angular velocity during the interval of time where the vector measurements are not available, and updating the attitude state upon the arrival of the vector measurements. Both cases of synchronous and asynchronous vector measurements with possible irregular sampling periods are considered. Moreover, some modifications to the intermittent observer are developed to handle different practical issues such as discrete-time implementation, noise filtering and gyro bias compensation
Advanced Computational-Effective Control and Observation Schemes for Constrained Nonlinear Systems
Constraints are one of the most common challenges that must be faced in control systems design. The sources of constraints in engineering applications are several, ranging from actuator saturations to safety restrictions, from imposed operating conditions to trajectory limitations. Their presence cannot be avoided, and their importance grows even more in high performance or hazardous applications. As a consequence, a common strategy to mitigate their negative effect is to oversize the components. This conservative choice could be largely avoided if the controller was designed taking all limitations into account. Similarly, neglecting the constraints in system estimation often leads to suboptimal solutions, which in turn may negatively affect the control effectiveness. Therefore, with the idea of taking a step further towards reliable and sustainable engineering solutions, based on more conscious use of the plants' dynamics, we decide to address in this thesis two fundamental challenges related to constrained control and observation.
In the first part of this work, we consider the control of uncertain nonlinear systems with input and state constraints, for which a general approach remains elusive.
In this context, we propose a novel closed-form solution based on Explicit Reference Governors and Barrier Lyapunov Functions. Notably, it is shown that adaptive strategies can be embedded in the constrained controller design, thus handling parametric uncertainties that often hinder the resulting performance of constraint-aware techniques.
The second part of the thesis deals with the global observation of dynamical systems subject to topological constraints, such as those evolving on Lie groups or homogeneous spaces. Here, general observability analysis tools are overviewed, and the problem of sensorless control of permanent magnets electrical machines is presented as a case of study. Through simulation and experimental results, we demonstrate that the proposed formalism leads to high control performance and simple implementation in embedded digital controllers
Nonlinear Systems
Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems
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