[Show abstract][Hide abstract] ABSTRACT: In this work, we consider the design of a distributed model predictive control scheme using multirate sampling for large-scale nonlinear systems composed of several coupled subsystems. Specifically, we assume that the states of each local subsystem can be divided into fast sampled states (which are available every sampling time) and slowly sampled states (which are available every several sampling times). The distributed model predictive controllers are connected through a shared communication network and cooperate in an iterative fashion, at time instants in which full system state measurements (both fast and slow) are available and the network closes, to guarantee closed-loop stability. When the communication network is open, the distributed controllers operate in a decentralized fashion based only on local subsystem fast sampled state information to improve closed-loop performance. In the proposed design, the controllers are designed via Lyapunov-based model predictive control. Sufficient conditions under which the state of the closed-loop system is ultimately bounded in an invariant region containing the origin are derived. The theoretical results are demonstrated through a nonlinear chemical process example.
[Show abstract][Hide abstract] ABSTRACT: In this work, we develop a data-based monitoring and reconfiguration system for a distributed model predictive control system in the presence of control actuator faults. Specifically, we first design fault detection filters and filter residuals, which are computed via exponentially weighted moving average, to effectively detect faults. Then, we propose a fault isolation approach which uses adaptive fault isolation time windows to quickly and accurately isolate actuator faults. Simultaneously, we estimate the magnitudes of the faults using a least-squares method and based on the estimated fault values, we design appropriate fault-tolerant control strategies to handle the actuator faults and maintain the closed-loop system state within a desired operating region. A nonlinear chemical process example is used to demonstrate the approach.
[Show abstract][Hide abstract] ABSTRACT: This work focuses on a general class of nonlinear process systems controlled by a two-tier networked control system integrating a local control system using continuous sensing/actuation with a networked control system using asynchronous sensing/actuation. To deal with control actuator faults that may occur in the closed-loop system and eliminate the ability of the two-tier networked control system to stabilize the process, a networked fault detection and isolation (FDI) and fault-tolerant control (FTC) system is designed which detects and isolates actuator faults and determines how to reconfigure the two-tier networked control system to handle the actuator faults and ensure closed-loop stability. The method is demonstrated using a reactor-separator process example.
Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on; 01/2010
[Show abstract][Hide abstract] ABSTRACT: This work applies the method of fault-detection, and isolation for nonlinear processes when some process variable measurements are available at regular sampling intervals and the remaining process variables are measured at an asynchronous rate to a gas-phase polyethylene reactor model. First, the fault-detection and isolation (FDI) scheme that employs model-based techniques for the isolation of faults is reviewed. The FDI scheme provides detection and isolation of any fault that enters into the differential equation of only synchronously measured states, and grouping of faults that enter into the differential equation of any asynchronously measured state. For a fully coupled process system, fault-detection occurs shortly after a fault takes place, and fault isolation, limited by the arrival of asynchronous measurements, occurs when asynchronous measurements become available. Fault-tolerant control methods with a supervisory control component are then employed to achieve stability in the presence of actuator failures using control system reconfiguration. Numerical simulations of the polyethylene reactor are performed, demonstrating the applicability and performance of the proposed fault-detection and isolation and fault-tolerant control method in the presence of asynchronous measurements.
American Control Conference, 2009. ACC '09.; 07/2009
[Show abstract][Hide abstract] ABSTRACT: In this work, we introduce a two-tier control architecture for nonlinear process systems with both continuous and asynchronous sensing and/or actuation. This class of systems arises naturally in the context of process control systems based on hybrid communication networks (i.e, point-to-point wired links integrated with networked wired/wireless communication) and utilizing multiple heterogeneous measurements (e.g., temperature and concentration). Assuming that there exists a lower-tier control system which relies on point-to-point communication and continuous measurements to stabilize the closed-loop system, we propose to use Lyapunov-based model predictive control to design an upper-tier networked control system to profit from both the continuous and the asynchronous measurements as well as from additional networked control actuators. The proposed two-tier control system architecture preserves the stability properties of the lower-tier controller while improving the closed-loop performance. The theoretical results are demonstrated using a chemical process example.
American Control Conference, 2009. ACC '09.; 01/2009
[Show abstract][Hide abstract] ABSTRACT: Accurate detection and isolation of faults is a critical component of a reliable fault-tolerant control system. It has been demonstrated that using a nonlinear controller to enforce a specific structure in the closed-loop system allows data-based detection and isolation of certain faults that would otherwise not be isolable using data-based techniques without the necessary closed-loop system structure. In this work, we demonstrate through a multi-unit chemical process example how this approach can be applied in a plant- wide setting. Nonlinear, model-based control laws are used to enforce a decoupling structure in the closed-loop system, and data-based statistical process monitoring methods are used for fault detection with isolation of the faults based on the imposed closed-loop system structure.
[Show abstract][Hide abstract] ABSTRACT: The present work proposes a method for data-based fault diagnosis that takes into account the design of the feedback control law in order to perform fault detection and isolation. This method allows isolating certain faults in a specifically structured closed-loop system using only a data-based approach. This is achieved through the design of appropriate nonlinear control laws that allow isolating given faults by effectively decoupling the dependency between certain process state variables. The theoretical results are demonstrated through a gas-phase polyethylene reactor example.
[Show abstract][Hide abstract] ABSTRACT: This work focuses on state feedback model predictive control of particulate processes subject to asynchronous measurements. A population balance model of a typical continuous crystallizer is taken as an application example. Three controllers, a standard model predictive controller and two recently proposed Lyapunov-based model predictive controllers, are applied to stabilize the crystallizer at an open-loop unstable steady-state in the presence of asynchronous measurements. The stability and robustness properties of the closed-loop system under the three controllers are compared extensively under two different assumptions on how the measurements from the crystallizer are obtained.
[Show abstract][Hide abstract] ABSTRACT: In this work, we focus on model predictive control of nonlinear systems subject to time-varying measurement delays. The motivation for studying this control problem is provided by networked control problems and the presence of time-varying delays in measurement sampling in chemical processes. We propose a Lyapunov-based model predictive controller which is designed taking time-varying measurement delays explicitly into account, both in the optimization problem formulation and in the controller implementation. The proposed predictive controller guarantees that the closed-loop system is ultimately bounded in a region that contains the origin if the maximum delay is smaller than a given constant. The theoretical results are illustrated through a chemical process example.
[Show abstract][Hide abstract] ABSTRACT: This work considers the problem of control of nonlinear process systems subject to input constraints and sensor faults (complete failure or intermittent unavailability of measurements). To clearly illustrate the importance of accounting for the presence of input constraints, we first consider the problem of sensor faults that necessitate sensor recovery to maintain closed-loop stability. We address the problem of determining, based on stability region characterizations for the candidate control configurations, which control configuration should be activated (reactivating the primary control configuration may not preserve stability) after the sensor is rectified. We then consider the problem of asynchronous measurements, i.e., of intermittent unavailability of measurements. To address this problem, the stability region (that is, the set of initial conditions starting from where closed-loop stabilization under continuous availability of measurements is guaranteed) as well as the maximum allowable data loss rate which preserves closed-loop stability for the primary and the candidate backup configurations are computed. This characterization is utilized in identifying the occurrence of a destabilizing sensor fault and in activating a suitable backup configuration that preserves closed-loop stability. The proposed method is illustrated using a chemical process example
Decision and Control, 2006 45th IEEE Conference on; 01/2007
[Show abstract][Hide abstract] ABSTRACT: This work considers the problem of implementing fault-tolerant control on a multi-input multi-output nonlinear system subject to multiple faults in the control actuators and constraints on the manipulated inputs. We design output-feedback fault-detection and isolation filters and output-feedback controllers via a combination of state-feedback fault-detection and isolation filters and controllers, and state estimators. The fault-detection and isolation filters essentially capture the difference between fault-free evolution of the system and the true evolution of the system to detect and isolate faults in the control actuators. The state estimates are used in devising the reconfiguration rule that determines which of the backup control configurations should be implemented in the closed-loop system. Specifically, a configuration is chosen that 1) does not use the failed control actuator, and 2) guarantees stability of the closed-loop system starting from the system state at the time of the failure (this is ascertained via the use of feedback controllers that provides an explicit characterization of the output-feedback stability region). The implementation of the fault-detection and isolation filters and reconfiguration strategy is demonstrated on a chemical reactor network example