An iterative model predictive control algorithm for constrained nonlinear systems

An iterative model predictive control (MPC) scheme for constrained nonlinear systems is presented. The idea of the method is to detour from the solution of a non‐convex optimization problem using a time‐variant linearization of the nonlinear system model that is adjusted iteratively by solving an iterative quadratic programming optimization problem at each sampling time. The main advantage is the faster resolution of the optimization problem by using quadratic programming instead of non‐convex programming and yet, properly describing the nonlinear dynamics of the process being controlled. In this article, a general framework of the method is presented together with a discussion on the conditions under which the iterations converge and on the uncertainty of its results due to the linearization used, as well as some practical considerations about its implementation. The performance of the proposed controller is illustrated via two examples.     

Suboptimal dual adaptive control for blood pressure management

Presents a new approach for adaptive control of blood pressure using vasoactive drugs. The idea is to use an adaptive controller that incorporates the concept of duality in the sense of Feldbaum (1965) and to consider the cost functional M-steps ahead in time. The dual property means that the control signal is chosen in such a way that estimation of the model parameters and regulation of the output signals are optimally balanced. Extensive computer simulations for different values of M shows that the sample mean of the achieved cost tends asymptotically to a limiting value while the variance is reduced. The proposed suboptimal adaptive controller has also an improved transient response when compared to a certainty equivalent controller     

A multistage suboptimal dual controller using optimal predictors

This work concerns the control of stochastic systems with unknown and randomly time-varying parameters. Cost functions which consider the sum of output variances up to M steps ahead in time are adopted in the optimization of the control performance. Optimal predictors are used to replace the future outputs which are needed in the solution of the optimization problem. The consequences of this simplification are investigated. A formula is obtained for the computation of the control signal in the case of M-steps-ahead optimization. The relationships between the controller presented here and other classical suboptimal dual controllers are analyzed. Simulation results illustrate the actual performance of the new controller     

Unknown Input Observer-based Actuator and Sensor Fault Estimation Technique for Uncertain Discrete Time Takagi-Sugeno Systems

This paper presents an Unknown Input robust Observer (UIO) capable of simultaneously estimate both sensor fault and system states. The system is assumed to be discrete-time Takagi-Sugeno (T-S) Fuzzy with uncertainties. An augmented system is obtained from the dynamic fault model and original system. Afterward, a UIO is designed for the augmented system aiming at decoupling process disturbances. Its design is obtained by using an H_∞ optimization technique and developed to maintain the observer stable, reducing the non-decoupled process disturbances effect. The proposed method is validated by two numerical examples as it is compared to a regular UIO technique and the extended Kalman filter. Results show the proposed technique presents better performance when the dynamic system is not purely nonlinear even if the same tuning parameters are chosen. Although other techniques are not able to ensure the error limitation, the proposed one is capable of it even in nonlinear systems.

     

Hybrid Methods for Detection and Identification of Faults in Dynamic Systems

Since the 1960s, when automation became essential to productivity, methods for the detection and identification of faults have been proposed. Physical systems are diversified and can be mechanical, electrical, pneumatic, electronic, or a combination of these. In addition, real plants have a large number of these devices, which are for its own operation, sensoring or control. Therefore the solutions given for detection of faults are generally very specific or particular. This paper aims to describe and analyze two hybrid methods of detection and fault identification based on residue and to check whether their inclusion with other methods, combining different techniques, can produce a better fault detection and identification system. The methods use the state observers for the generation of residues, which serve for the detection and identification and the set called the bank of signatures to identify the faults. Thereafter, the methods use different approaches to diagnose the fault: the first uses the approach of the mean square error, and the second uses a decision tree.