Hierarchical Optimization-Based Model Predictive Control for a Class of Discrete Fuzzy Large-Scale Systems Considering Time-Varying Delays and Disturbances

Altres ajuts: Acord transformatiu CRUE-CSICIn this manuscript, model predictive control for class of discrete fuzzy large-scale systems subjected to bounded time-varying delay and disturbances is studied. The considered method is Razumikhin for time-varying delay large-scale systems, in which it includes a Lyapunov function associated with the original non-augmented state space of system dynamics in comparison with the Krasovskii method. As a rule, the Razumikhin method has a perfect potential to avoid the inherent complexity of the Krasovskii method especially in the presence of large delays and disturbances. The considered large-scale system in this manuscript is decomposed into several subsystems, each of which is represented by a fuzzy Takagi-Sugeno (TS) model and the interconnection between any two subsystems is considered. Because the main section of the model predictive control is optimization, the hierarchical scheme is performed for the optimization problem. Furthermore, persistent disturbances are considered that robust positive invariance and input-to-state stability under such circumstances are studied. The linear matrix inequalities (LMIs) method is performed for our computations. So the closed-loop large-scale system is asymptotically stable. Ultimately, by two examples, the effectiveness of the proposed method is illustrated, and a comparison with other papers is made by remarks

Decentralized robust interval type-2 fuzzy model predictive control for Takagi–Sugeno large-scale systems

Here, decentralized robust interval type-2 (IT2) fuzzy model predictive control (MPC) for Takagi–Sugeno (T-S) large-scale systems is studied. The large-scale system consists of many IT2 fuzzy T–S subsystems. Important necessities that limit the practical application of MPC are the online computational cost and burden of the frameworks. For MPC of T–S fuzzy large-scale systems, the online computational burden is even worse, and in some cases, they cannot be solved timely. Especially for severe, large-scale systems with disturbances, the MPC of T–S fuzzy large-scale systems usually give a conservative solution. So, researchers have many challenges and in finding a reasonable solution in a short time. Although more comfortable results can be achieved by the proposed fuzzy MPC approach, which adopts T–S large-scale systems with nonlinear subsystems, many restrictions are not considered. In this paper, challenges are solved, and the MPC is designed for a nonlinear IT2 fuzzy large-scale system with uncertainties and disturbances. Besides, the online optimization problem is solved, and results are proposed. Consequently, the online computational cost of the optimization problem is reduced considerably. Finally, the effectiveness of the proposed algorithm is illustrated with two practical examples

Nonlinear Pseudo State-Feedback Controller Design for Affine Fuzzy Large-Scale Systems with H∞ Performance

Acord transformatiu CRUE-CSICThis paper treats robust controller design for Affine Fuzzy Large-Scale Systems (AFLSS) composed of Takagi-Sugeno-Kang type fuzzy subsystems with offset terms, disturbances, uncertainties, and interconnections. Instead of fuzzy parallel distributed compensation, a decentralized nonlinear pseudo state-feedback is developed for each subsystem to stabilize the overall AFLSS. Using Lyapunov stability, sufficient conditions with low codemputational effort and free gains are derived in terms of matrix inequalities. The proposed controller guarantees asymptotic stability, robust stabilization, and H∞ control performance of the AFLSS. A numerical example is given to illustrate the feasibility and effectiveness of the proposed approach

cycle text2face: cycle text-to-face gan via transformers

Text-to-face is a subset of text-to-image that require more complex architecture due to their more detailed production. In this paper, we present an encoder-decoder model called Cycle Text2Face. Cycle Text2Face is a new initiative in the encoder part, it uses a sentence transformer and GAN to generate the image described by the text. The Cycle is completed by reproducing the text of the face in the decoder part of the model. Evaluating the model using the CelebA dataset, leads to better results than previous GAN-based models. In measuring the quality of the generate face, in addition to satisfying the human audience, we obtain an FID score of 3.458. This model, with high-speed processing, provides quality face images in the short time

A novel robust extended dissipativity state feedback control system design for interval type-2 fuzzy Takagi-Sugeno large-scale systems

Recently, systems have become large in their model and dynamic. To apply control algorithms, serious problems appear that need to be solved. Two significant problems are modelling the dynamics of the large-scale system and reducing effects of perturbations. In this paper, we use the advantage of large-scale systems modelling based on the type-2 fuzzy Takagi–Sugeno model to cover the uncertainties caused by large-scale systems modelling. The advantage of using membership function information is the reduction of conservatism resulting from stability analysis. Also, this paper uses the extended dissipativity robust control performance index to reduce the effect of external perturbations on the large-scale system, which is a generalization of [Formula: see text], [Formula: see text], passive and dissipativity performance indexes and control gains can be achieved through solving linear matrix inequalities (LMIs). Hence, the whole closed-loop system is asymptotically stable. Finally, the effectiveness of the proposed method is demonstrated by two practical examples