Tradeoffs and limitations in Networked Control Systems

Control and communication have been increasingly studied together in the recent past. The \u27Satellite - Ground Controller\u27 is a classic example of a remote control problem. Another closely related topic of study is control over an unreliable communication network and its applications seem to grow continuously. Networked Control Systems (NCS) is a rapidly growing research topic and examples of practical significance include multiple vehicle coordination, air-traffic control, and Micro-Electro-Mechanical Systems (MEMS). The work presented consists of two problems (1) The Remote control problem and (2) The Networked control problem. The remote control problem we address is the feedback stabilization of a linear discrete-time plant in feedback with a linear controller via unreliable communication channels. The channels are modeled as analog packet-drop (erasure) channels. Erasure channel is a good model of loss of data packets, typical in large networks like the Internet. We analyze the maximum sustainable erasure on the sensor and actuator channels given a level of erasure on the feedback (Service) channel and synthesize controllers. The networked control problem we address is a special case of spatially invariant plants interconnected through a static network. We consider a circular topology of interconnection and introduce a new notion of Bode sensitivity integral for networks of spatially invariant plants in feedback with static networks and comment on the fundamental limitations of design of such feedback networks

Analysis and Synthesis Methods for Nonlinear Network Systems

Over the past two decades the interactions between systems and their control components have undergone some significant changes. These interactions are no more localized, but usually take place over a network and even the control components may be remotely located, thus involving aspects of communication in control systems. Furthermore, the last decade has also seen a surge in intermingling ideas from control and communication and their application to biological systems, power systems giving rise to new research areas like Networked Control Systems (NCS), Cyber-Physical Systems (CPS), Gene Regulatory Networks (GRN) to name a few. This has led researchers to study control systems with practical constraints imposed on them. One such practical constraint identified as a major challenge, is the fragility of control systems and performance degradation, when the interconnection is not reliable. Design of controllers and estimators for such systems needs to take into account these constraints and mitigate them, to ensure sufficient robustness against unreliability of the interconnection. Considerable research has been done over the past decade in analyzing these new challenges and developing design tools to extract desired performance. Control over communication channels is one such widely researched area where the effect of unreliable interconnection on the stability performance of the system has been studied. The reliability of communication could manifest in various ways like sensor failure at output measurement, control actuator failure, interconnection links failures in the form of packet erasure channel, fading channel, quantization etc. Significant research progress has been made, in areas of control and estimation over unreliable communication links, consensus over unreliable network interconnections, etc., albeit the work has dealt with linear time invariant (LTI) systems theory. This has led to fruitful results for special cases of packet-drop communication channel modeled as a Bernoulli erasure channel. In the case of linear systems these results have demonstrated a connection between the performance characteristics of the interconnection and the expansion or destabilizing characteristics of the linear system, in obtaining desired performance of the closed loop system. Most of the current research for control over communication channels have focused on LTI plant dynamics. Furthermore the results involving nonlinear plant dynamics have reverted to local linearization techniques. It is well-known that for nonlinear systems, results based on local linearization at an equilibrium point will be local in nature and does not account for the global dynamics of the nonlinear system. For the proposed applications of network control systems to electric power grid and biological networks it is essential to develop results for the analysis of nonlinear systems over networks. In this work, we are primarily interested in the interaction of nonlinear systems and controllers over unreliable interconnections modelled as a stochastic multiplicative uncertainty. We provide analysis and synthesis methods for the control and observation of uncertain nonlinear network controlled systems. Our analysis methods indicate, fundamental limitations arise in the stabilization and observation of nonlinear systems over uncertain channels. Our main result provides the limitation for observation of nonlinear system over erasure channel expressed in terms of the probability of erasure and positive Lyapunov exponents of the open loop nonlinear plant. The positive Lyapunov exponents are measure of dynamical complexity and comparing our results with existing results for LTI systems, we show that Lyapunov exponents emerge as a natural generalization of eigenvalues from linear to nonlinear systems. Entropy is another measure of dynamical complexity. Using results from ergodic theory of dynamical systems we also relate the limitation for stabilization and observation with the entropy corresponding to the invariant measure capturing the global dynamics of the nonlinear systems. Existing Bode-like fundamental limitation results for nonlinear systems relate limitation for stabilization with the entropy corresponding to the invariant measure at the equilibrium point. Our results are the first to connect the limitation for stabilization with the entropy corresponding to invariant measure other than the one associated with equilibrium point. Our synthesis methods for the design of robust controller and observer against uncertain channels revolves around special class of nonlinear systems -Lure systems. These systems are essentially linear systems with sector-bounded nonlinearity in the feedback loop. For this special class of nonlinear systems, we delve into the theoretical tools of absolute stability to obtain some synthesis methods which provide design criteria for nonlinear systems over unreliable interconnections. Stability of Lur\u27e systems is a special case of the stability of interconnected passive systems. Thus we can characterize the unreliability of the interconnection, that guarantees the desired performance for Lur\u27e systems, in terms of the passivity of the linear system. Passivity theory is a rich theory with wide spread applications to nonlinear controller design and observation, which extends ideas of system stability to input-output systems using the ideas of dissipativity. Our synthesis methods developed for Lure systems with input and output stochastic channel uncertainties provide natural extension of the powerful passivity based synthesis tools developed for deterministic Lure systems. In particular, our results help understand the trade-off between passivity and stochastic uncertainty in feedback control systems

Stabilization of Linear Systems Over Gaussian Networks

The problem of remotely stabilizing a noisy linear time invariant plant over a Gaussian relay network is addressed. The network is comprised of a sensor node, a group of relay nodes and a remote controller. The sensor and the relay nodes operate subject to an average transmit power constraint and they can cooperate to communicate the observations of the plant's state to the remote controller. The communication links between all nodes are modeled as Gaussian channels. Necessary as well as sufficient conditions for mean-square stabilization over various network topologies are derived. The sufficient conditions are in general obtained using delay-free linear policies and the necessary conditions are obtained using information theoretic tools. Different settings where linear policies are optimal, asymptotically optimal (in certain parameters of the system) and suboptimal have been identified. For the case with noisy multi-dimensional sources controlled over scalar channels, it is shown that linear time varying policies lead to minimum capacity requirements, meeting the fundamental lower bound. For the case with noiseless sources and parallel channels, non-linear policies which meet the lower bound have been identified

Optimal mean-square performance for networked control systems with unreliable communication

In this work we study mean-square (MS) stability and mean-square (MS) performance for discrete-time, finite-dimensional linear time-varying systems with dynamics subject to i.i.d. random variation. We do so primarily in the context of networked control systems (NCS) where the network communication channels are unreliable and modeled as multiplicative stochastic uncertainties, e.g. wireless links subject to packet dropouts and modeled as Bernoulli processes. We first focus on the analysis problem in general. We derive a convex feasibility problem and associated convex optimization problem which can be used to determine the MS stability and MS performance respectively of a given system. Since this analysis theory is derived in terms of the feasibility of and optimization of a linear cost subject to linear matrix inequalities (LMIs), it serves as the foundation from which a solution methodology for numerous controller synthesis problems can be derived. Next we formulate the main synthesis problem we consider in this work: a networked control system where both the sensor measurements for the plant(s) and the commands from the controller are transmitted via unreliable communication channels. We treat the unreliable communication links as i.i.d. random processes. We assume that the plant(s) and links are subject to additive exogenous noise, and that we have access to a reliable but delayed acknowledgment of whether or not the controller commands were received by the plant(s) on the previous time step. Finally we restrict the controller to be finite-dimensional, linear, have no structural dependence on the particular path history of the random processes, and scale in size and complexity linearly with the number of random channels. We then show that this synthesis problem has a MS stabilizing solution if and only if two simpler convex problems have MS stabilizing solutions, and moreover that the optimal MS performance solution to this synthesis problem if it exists can be obtained by solving a sequence of these simpler convex problems. Additionally, we show that the overall optimal MS performance cost is the sum of two components which can be determined from the solutions to the special problems. That is, we derive a separation principle for our problem analogous to the classical H2 synthesis

Fundamental limits in Gaussian channels with feedback: confluence of communication, estimation, and control

The emerging study of integrating information theory and control systems theory has attracted tremendous attention, mainly motivated by the problems of control under communication constraints, feedback information theory, and networked systems. An often overlooked element is the estimation aspect; however, estimation cannot be studied isolatedly in those problems. Therefore, it is natural to investigate systems from the perspective of unifying communication, estimation, and control;This thesis is the first work to advocate such a perspective. To make Matters concrete, we focus on communication systems over Gaussian channels with feedback. For some of these channels, their fundamental limits for communication have been studied using information theoretic methods and control-oriented methods but remain open. In this thesis, we address the problems of characterizing and achieving the fundamental limits for these Gaussian channels with feedback by applying the unifying perspective;We establish a general equivalence among feedback communication, estimation, and feedback stabilization over the same Gaussian channels. As a consequence, we see that the information transmission (communication), information processing (estimation), and information utilization (control), seemingly different and usually separately treated, are in fact three sides of the same entity. We then reveal that the fundamental limitations in feedback communication, estimation, and control coincide: The achievable communication rates in the feedback communication problems can be alternatively given by the decay rates of the Cramer-Rao bounds (CRB) in the associated estimation problems or by the Bode sensitivity integrals in the associated control problems. Utilizing the general equivalence, we design optimal feedback communication schemes based on the celebrated Kalman filtering algorithm; these are the first deterministic, optimal communication schemes for these channels with feedback (except for the degenerated AWGN case). These schemes also extend the Schalkwijk-Kailath (SK) coding scheme and inherit its useful features, such as reduced coding complexity and improved performance. Hence, this thesis demonstrates that the new perspective plays a significant role in gaining new insights and new results in studying Gaussian feedback communication systems. We anticipate that the perspective could be extended to more general problems and helpful in building a theoretically and practically sound paradigm that unifies information, estimation, and control

Linear control of the Pendubot over packet-drop networks

In this thesis we investigate control across stochastic drop-out channels. We seek optimal linear controllers for mean-square stability that make use of the knowledge of whether a packet is received. We establish a fundamental bound on drop-out probability allowable for stabilization, which in some cases is tight. When tight, a convex optimization provides controller design. The main result is a remote stabilization technique that always achieves this bound via acknowledgement from the actuation receiver. Controller information structure and decentralization issues are considered. The theory is then applied to the inverted pendulum experiment Pendubot. The Pendubot is a nonlinear plant that balances the links of an inverted pendulum via a control torque and optical sensors for position. A control strategy is developed using the stabilization technique derived above together with linearization and discretization of the apparatus. The complete design procedure is documented leading to a successful controller. Included are the encountered hardware issues, software issues in Matlab and C programming, theoretical issues, and experiment results. A mock drop-out network is simulated via C programming. The experiments validate that the theoretical design technique actually works, and that the theoretical bounds on allowable drop-out have significant practical bearing