A Tractable Fault Detection and Isolation Approach for Nonlinear Systems with Probabilistic Performance

This article presents a novel perspective along with a scalable methodology to design a fault detection and isolation (FDI) filter for high dimensional nonlinear systems. Previous approaches on FDI problems are either confined to linear systems or they are only applicable to low dimensional dynamics with specific structures. In contrast, shifting attention from the system dynamics to the disturbance inputs, we propose a relaxed design perspective to train a linear residual generator given some statistical information about the disturbance patterns. That is, we propose an optimization-based approach to robustify the filter with respect to finitely many signatures of the nonlinearity. We then invoke recent results in randomized optimization to provide theoretical guarantees for the performance of the proposed filer. Finally, motivated by a cyber-physical attack emanating from the vulnerabilities introduced by the interaction between IT infrastructure and power system, we deploy the developed theoretical results to detect such an intrusion before the functionality of the power system is disrupted

Control and structural optimization for maneuvering large spacecraft

Presented here are the results of an advanced control design as well as a discussion of the requirements for automating both the structures and control design efforts for maneuvering a large spacecraft. The advanced control application addresses a general three dimensional slewing problem, and is applied to a large geostationary platform. The platform consists of two flexible antennas attached to the ends of a flexible truss. The control strategy involves an open-loop rigid body control profile which is derived from a nonlinear optimal control problem and provides the main control effort. A perturbation feedback control reduces the response due to the flexibility of the structure. Results are shown which demonstrate the usefulness of the approach. Software issues are considered for developing an integrated structures and control design environment

Quasi feedback forms for differential-algebraic systems

We investigate feedback forms for linear time-invariant systems described by differential-algebraic equations. Feedback forms are representatives of certain equivalence classes. For example state space transformations, invertible transformations from the left, and proportional state feedback constitute an equivalence relation. The representative of such an equivalence class, which we call proportional feedback form for the above example, allows to read off relevant system theoretic properties. Our main contribution is to derive a quasi proportional feedback form. This form is advantageous since it provides some geometric insight and is simple to compute, but still allows to read off the relevant structural properties of the control system. We also derive a quasi proportional and derivative feedback form. Similar advantages hold

Sampled-data and discrete-time H2H_2 optimal control

This paper deals with the sampled-data H2 optimal control problem. Given a linear time-invariant continuous-time system, the problem of minimizing the H2 performance over all sampled-data controllers with a fixed sampling period can be reduced to a pure discrete-time H2 optimal control problem. This discrete-time H2 problem is always singular. Motivated by this, in this paper we give a treatment of the discrete-time H2 optimal control problem in its full generality. The results we obtain are then applied to the singular discrete-time H2 problem arising from the sampled-data H2 problem. In particular, we give conditions for the existence of optimal sampled data controllers. We also show that the H2 performance of a continuous-time controller can always be recovered asymptotically by choosing the sampling period sufficiently small. Finally, we show that the optimal sampled-data H2 performance converges to the continuous-time optimal H2 performance as the sampling period converges to zero

Observers for linear time-varying systems

AbstractWe give characterizations and necessary and sufficient existence conditions for tracking and asymptotic observers for linear functions of the state of a linear finite-dimensional time-varying state space system. We specialize the results to affine parameter varying systems and bilinear control systems

Robust structural feedback linearization based on the nonlinearities rejection

International audienceIn this paper, we consider a class of affine control systems and propose a new structural feedback linearization technique. This relatively simple approach involves a generic linear-type control scheme and follows the classic failure detection methodology. The robust linearization idea proposed in this contribution makes it possible an effective rejection of nonlinearities that belong to a specific class of functions. The nonlinearities under consideration are interpreted here as specific signals that affect the initially given systems dynamics. The implementability and efficiency of the proposed robust control methodology is illustrated via the attitude control of a PVTOL

Max-plus algebra in the history of discrete event systems

This paper is a survey of the history of max-plus algebra and its role in the field of discrete event systems during the last three decades. It is based on the perspective of the authors but it covers a large variety of topics, where max-plus algebra plays a key role

An Adaptive Liquid Level Controller Using Multi Sensor Data Fusion

This paper describes a design of adaptive liquid level control system using the concept of Multi Sensor Data Fusion (MSDF). Purpose of the work is to design a controller for accurately controlling the level of liquid in a process tank with liquid temperature changes. The proposed objective is obtained by i) implementing a MSDF framework using Pau’s framework for measuring liquid level and temperature, ii) analyzing the behavior of actuator output for variation in liquid temperature, and iii) designing a suitable adaptive controller which will produce desired control action for controlling liquid level accurately using neural network algorithms. Outputs from sensors are fused to obtain the fluid level output and also relation of level transmitter output for change in temperature. This information is used by controller to train the neural network so as to tune the controller parameters (proportional gain, integral constant, and differential constant), to drive the actuator. Results obtained show that the system is able to control liquid level within range of 1.915% of set point even with variations in liquid temperature