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In this paper, a novel robust predictive control strategy is proposed for the synchronization of fractional-order time-delay chaotic systems. A recurrent non-singleton type-2 fuzzy neural network (RNT2FNN) is used for the estimation of the unknown ...

In this work, we present a control-theoretic algorithm to improve the energy efficiency of the GPU targeting deadline-driven graphics applications. Our algorithm dynamically controls multiple power knobs within the GPU (DVFS and number of active slices) ...

In this paper, a model-based predictive control approach for fault management in computing systems is presented. The proposed approach can incorporate existing fault diagnosis methods and fault recovery actions to facilitate the recovery process. When a ...

High precision temperature control of the RF GUN is necessary to optimally accelerate thousands of electrons

within the injection part of the European X-ray free-electron laser XFEL and the Free Electron Laser FLASH.

A difference of the RF GUN ...

Multiple model adaptive control procedures have been considered for a computer-based feedback system,

which regulates the infusion rate of a drug (nitroprusside) in order to maintain the blood pressure as close as

possible to the desirable value. ...

The subject of the article concerns a predictive contol with feedback linearization apllied for many input -

many output system. It rely on finding compromise in every step beetween feasible and optimal LQ control

by minimization of one variable. ...

A widely recognized advanced control methodology model predictive control is applied to solve a classical

servo problem in the context of linear fractional-order (FO) system with the help of an approximation method.

In model predictive control, the ...

A model predictive control design for nonlinear systems based on artificial neural networks is discussed. The Feedforward neural networks are used to describe the unknown nonlinear dynamics of the real system. The backpropagation algorithm is used, ...

Network time-delay is usually uncertain or random, packet loss and timing disorder also can be summed up in a certain degree of time-delay. In this paper, we focused on networked control systems (NCS) with bounded uncertain time delays. Utilizing ...

This paper proposes the use of an explicit dynamic predictive control which is a PWA (PieceWise Affine) function of the dynamic model states of a robotic wheelchair. The optimization algorithmic is based on Multi-parametrical Programming, allowing all ...

It is difficult to establish accurate and practical mathematic models for the process of grinding and classification due to their complicated mechanism and interrelated affecting factors. A new control algorithm is put forward to deal with the complex ...

Modern cost-conscious dynamic systems incorporate knobs that allow run-time trade-offs between system metrics of interest. In these systems regular knob tuning to minimize costs while satisfying hard system constraints is an important aspect. Knob ...

Advanced technologies such as sub-45nm CMOS and 3D integration are known to have more accelerated and increased number of reliability failure mechanisms. Classical reliability assessment methodology, which assumes ad-hoc failure criteria and worst-case ...

The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first ...

In this paper, the problems on the model predictive control (MPC) of nonlinear systems are studied. A direct weight optimization (DWO) method based predictive control for nonlinear systems is presented. The approach gives a direct and effective ...

The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback ...

A model predictive control (MPC) strategy based on a novel Bayesian-Gaussian neural network (BGNN) model was proposed for the controller design of hydraulic turbine in this paper. The BGNN was used to learn the nonlinear dynamic model of controlled ...

CMAC computational model that is based on the cerebellum structure is known as a Neural Network with high computation and learning speed. Fuzzy CMAC, by introducing fuzzy reasoning to CMAC, converts it from a black box to a white box whose performance ...

This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and ...

This paper proposes a method for reducing the trajectory tracking errors of robotic systems in presence of input saturation and state constraints. Basing on a finite horizon prediction of the future evolution of the robot dynamics, the proposed device ...