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This paper concentrates on synthesizing robust predictive controllers in uncertain models with time‐delay and saturated input. To handle the complexities simultaneously and reach the desired objective, a state‐feedback structure with unknown gains is considered the compensator. Then, to specify the instantaneous values of the regulator's gains, an optimization issue will be derived relying on linear matrix inequality. Accordingly, in uncertain continuous‐time systems with some constraints and time‐delays, the controller's coefficients would be found in an online way from such an optimization. Numerous continuous‐time simulations are numerically performed to reveal the merit and robustness of the suggested methodology over similar techniques. [ABSTRACT FROM AUTHOR]

In this article, to enhance the transient performances in constrained uncertain systems, a control technique based on linear matrix inequality (LMI) is characterized to the robust integral composite nonlinear feedback (CNF) controller design. To achieve this aim, considering the inputs limitations and the uncertainties, the regulation is transformed into a minimization subjected to several LMIs. Solving an optimization in real‐time, the gains of the controller are calculated. So, the regulation and the disturbance rejection are effectively handled employing the presented control law. Compared with the existing integral methodology, the provided simulations validate the applicability of the robust integral CNF control law in uncertain systems. [ABSTRACT FROM AUTHOR]

The transient response improvement is the most crucial feature of the composite nonlinear feedback (CNF) controllers. This study proposes a novel CNF tuning method for systems subject to saturation. To achieve the goals, the linear part is optimally obtained via a performance index and the linear matrix inequality (LMI). Then, employing the passivity property, a heuristic way is suggested to adjust the nonlinear term. Thus, the automatic and online features would be the key novelties of this algorithm compared with the offline, trial and error, or nonautomatic ones. Some systems are simulated to evaluate the technique and compare with the existing methods. The outcomes confirm that the proposed tuning yields outstanding improvement concerning the aforementioned systems. [ABSTRACT FROM AUTHOR]

The main focus of this research is on improving the performance of dynamic systems with actuator non‐linearities and time‐varying disturbances. To this end, using the concept of finite‐time stability, a novel observer is presented to estimate the disturbances in systems with input non‐linearities. Then, introducing an innovative sliding manifold, a robust observer‐based technique is derived to guarantee the finite‐time stability of the sliding surface. Hence, the closed‐loop system would be stable in the presence of hard non‐linearities and time‐varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach. [ABSTRACT FROM AUTHOR]

This study addresses a new delay‐dependent non‐fragile robust model predictive controller for a class of uncertain continuous‐time systems with time‐varying delay subjected to constrained input and perturbed controller gain. The uncertainties in system parameters and controller gain are considered as general additive forms. The proposed controller can provide a suitable linear feedback control law depending on delay such that robust performance is achieved to the possible existence of uncertainties. To this end, an optimisation problem is formulated to minimise the upper‐bound of the cost function involving some conditions in terms of linear matrix inequalities, utilising the information of lower‐and upper‐bounds of time‐varying delay. By including information regarding the delay range in the quadratic function, the ability to have less conservative results compared to the related works in literature is provided. The effectiveness of the proposed technique is illustrated through a comparison with recent works. [ABSTRACT FROM AUTHOR]

In this paper, an LMI framework based on model predictive strategy is addressed to design a robust dynamical control law in a typical control system. In the proposed method, instead of traditional static controller, a dynamic control law is used. With a suitable matrix transformation, the controller parameters selection are translated into an optimization problem with some LMI constraints. The plant input and output constraints are also handled with another LMIs. The controller is represented in state space form, and its parameters are computed in real-time operation. For achieving this goal, by solving an optimization problem, a dynamic controller is designed, which meets the required plant performances. These results are used in 2 numerical examples to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In reset control systems, the reset law is a main characteristic of the reset controllers. In this study, a model prediction-based framework is proposed to determine a suitable reset law for the uncertain linear control system. For achieving this goal, first a typical uncertain linear plant is considered and then the state space of the closed-loop system, which consists of the uncertain plant and a linear controller, is formulated. The stability problem of such uncertain system is translated into a feasibility assessment of some linear matrix inequality sets and then based on the model prediction strategy a reset law is derived to minimise a quadratic cost function. When the system states enter into a reset set, according to the computed reset law, the controller states are reset. This method is applied to a typical distillation column to show the effectiveness of the proposed approach and is compared with non-reset control system. [ABSTRACT FROM AUTHOR]