A controller management scheme for active fault-tolerant tracking: Linear discrete-time systems.

This paper proposes an active fault-tolerant control (FTC) approach based on the controller management and virtual actuator idea for linear discrete-time systems subject to unknown L2-bounded disturbances, input constraint, and time-varying additive actuator faults. The closed-loop faulty system, which includes the modified nominal controller, the fault and state estimator, and the virtual actuator, suppresses the effects of disturbances and faults, while ensuring input-constraint satisfaction. The management of the nominal controller is performed through an online optimization method - in the form of a standard quadratic programming problem - by manipulating the reference input and intervening in the nominal controller evolution. The proposed method proves the input-to-state stability (ISS) criterion of the overall closed-loop faulty system. The problem of minimizing the ultimate bound of the ISS criterion is formulated in terms of tractable linear matrix inequality (LMI) conditions that allow the fault and state estimation errors to converge to a small neighborhood of the origin. To illustrate the capabilities and advantages of the proposed control strategy, comparative simulation results are presented for a flexible joint robotic system, tracking control of a DC motor's angular velocity, and the multivariable VTOL aircraft.

Multi-objective T-S fuzzy control of Covid-19 spread model: An LMI approach.

Due to the importance of control actions in spreading coronavirus disease, this paper is devoted to first modeling and then proposing an appropriate controller for this model. In the modeling procedure, we used a nonlinear mathematical model for the covid-19 outbreak to form a T-S fuzzy model. Then, for proposing the suitable controller, multiple optimization techniques including Linear Quadratic Regulator (LQR) and mixed H 2 - H ∞ are taken into account. The mentioned controller is chosen because the model of corona-virus spread is not only full of disturbances like a sudden increase in infected people, but also noises such as unavailability of the exact number of each compartment. The controller is simulated accordingly to validate the results of mathematical calculations, and a comparative analysis is presented to investigate the different situations of the problem. Comparing the results of controlled and uncontrolled situations, it can be observed that we can tackle the devastating hazards of the covid-19 outbreak effectively if the suggested approaches and policies of controlling interventions are executed, appropriately.

Optimal robust model predictive reset control design for performance improvement of uncertain linear system.

This study aims to design a robust reset dynamic output feedback control (DOFC) for a class of uncertain linear systems. This procedure is performed as following. First, the elements of the robust DOFC are designed via the linear matrix inequality (LMI) technique such that closed-loop exponential stability is achieved. Second, reset law which contains value of after reset and a constraint for the reset action is determined. Genetic algorithm (GA) is applied to minimize the proposed objective function to find the reset times by using the specified after reset value for individual reset instances. To do this, a model-predictive-based optimization is adopted by using output information. The proposed robust controller is applied to two uncertain systems; distillation column, and B747-100/200 aircraft model. The merits of the proposed robust reset controller in improving transient performance are demonstrated by comparing its results with state-of-the-art methods.