Novel Fractional-Order Model Predictive Control: State-Space Approach
نویسندگان
چکیده
This paper deals with a novel approach to the fractional-order model predictive control in state space. Except well-known models of processes (plants) arbitrary (real) order derivatives fractional differential equations new performance index (cost function) and action are considered. Such combined provides more degrees freedom incorporates dynamics into form memory due property operator. An illustrative example this is presented.
منابع مشابه
Fractional-order State Space Models
In this paper we will present some alternative types of mathematical description and methods of solution of the fractional-order dynamical system in the state space. We point out the difference in the true sense of the name „state“ space for the integer-order and fractional-order system and the importance of the initialization function for the fractionalorder system. Some implications concernin...
متن کاملState-space interpretation of model predictive control
A model predictive control technique based on a step response model is developed using state estimation techniques. The standard step response model is extended so that integrating systems can be treated within the same framework. Based on the modified step response model, it is shown how the state estimation techniques from stochastic optimal control can be used to construct the optimal predic...
متن کاملState-Space Constrained Model Predictive Control
Constrained State-space Model Predictive Control is presented in the paper. Predictive controller based on incremental linear state-space process model and quadratic criterion is derived. Typical types of constraints are considered – limits on manipulated, state and controlled variables. Control experiments with nonlinear model of multivariable laboratory process are simulated first and real ex...
متن کاملOn State Space Model Based Predictive Control
An input and output model is used for the development of a model based predictive control framework for linear model structures. Diierent MPC algorithms which are based on linear state space models or linear polynomial models t into this framework. A new identiication horizon is introduced in order to represent the past.
متن کاملStochastic Model Predictive Control: State space methods
1 Performance objective and closed-loop convergence 1 1.1 Stochastic system models . . . . . . . . . . . . . . . . . . . 1 1.2 Performance cost . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Cost evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4 Unconstrained optimal control . . . . . . . . . . . . . . . . . 12 1.5 Receding horizon control, stability and convergence . . ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3093364