Search results for "predictive control"
showing 10 items of 41 documents
Estimation of the mean crystal size and the moments of the crystal size distribution in batch crystallization processes
2016
International audience; A cascade high gain observer is designed to estimate the first four leading moments of the crystal size distribution (CSD) and the mean crystal size in batch crystallization processes. The proposed observer is based on a well-known transformation of the partial differential equation describing the CSD to a set of ordinary differential equations (the method of moments). Due to numerical difficulties resulting from the important differences in the magnitudes of the moments, a set of new variables is computed to allow a good estimation of the moments and thus the mean crystal size. In this work, only solute concentration and crystallizer temperature are used to estimate…
Predictive Intelligent Fuzzy Control for Cooperative Motion of Two Nonholonomic Wheeled Cars
2007
In this paper a problem of intelligent cooperative motion control of two wheeled nonholonomic cars (target and follower) is considered. Once a target car converges to a fixed state (position and orientation), a follower car coming from different position and orientation, converges to the state above, without excessive delay between the known arrival time of the target car and the arrival time of the follower. In this sense we present a new predictive fuzzy control system. A Kalman's filter and an odometric model are used to predict the future position and orientation of the target car. The prediction above is employed to plane a circular nonholonomic reference motion for the follower car. A…
Linear parameter estimation and predictive constrained control of wiener/hammerstein systems
2003
Abstract A new, analytical, orthonormal basis functions (OBF)-based design methodology for adaptive predictive constrained control of open-loop stable, possibly nonminimum phase, time-varying Wiener and Hammerstein systems is presented. A linear adaptive least-squares parameter estimation algorithm is applied both to a nonlinear static part and a linear dynamic, OBF-modeled factor of the Wiener/Hammerstein system. A notion of inverse systems is crucial for linear estimation of both Wiener and Hammerstein systems, with in verses of the nonlinear or linear parts respectively involved. The adaptive estimator is coupled with a simple but robust, predictive control strategy called Extended Horiz…
A Constrained Optimal Model Predictive Control for Mono Inverter Dual Parallel PMSM Drives
2018
The actual trends in the design of AC drives are directed to the reduction of the total weight, volume and cost. Usually, this implies the necessity to adopt new motor topologies and converter architectures. An important role is played by the mono-inverter dual parallel motor (MIDP), which gives the possibility to reduce the total weight and costs of power converters. This paper proposes a novel model predictive control algorithm in order to improve the transient performances of a MIDP used for an overhead carrier. The effectiveness of the proposal control is verified through some numerical simulations.
Development of a predicitive type-2 neurofuzzy controller
2009
A controller that combines the main characteristics and advantages of three different control methodologies is proposed for the control of systems with nonlinearities and uncertainties. A neural network predictive control approach is implemented modifying the output of a controller with a fuzzy logic structure that uses type-2 fuzzy sets. Neural networks are also used to optimize the membership function parameters. The proposed controller is tested by simulation for the control of a bioreactor characterized by bifurcation and parameter uncertainty.
A Model Modulated Predictive Current Control Algorithm for the Synchronous Reluctance Motor
2022
This paper proposes a Model Modulated Predictive Control (M2PC) specifically developed for Synchronous Reluctance Motors (SynRM) drives and based on a purposely developed magnetic model taking into account both self- and cross-saturation. The proposed M2PC exploits the discrete-time version of the dynamic model to compute the current prediction and the resulting predicted current error. The built-in PWM modulator chooses the optimal pair of voltage space-vector to be applied by the inverter to minimize the current error. The magnetic model permits obtaining good dynamic performance in every working condition.
A Fokker–Planck control framework for multidimensional stochastic processes
2013
AbstractAn efficient framework for the optimal control of probability density functions (PDFs) of multidimensional stochastic processes is presented. This framework is based on the Fokker–Planck equation that governs the time evolution of the PDF of stochastic processes and on tracking objectives of terminal configuration of the desired PDF. The corresponding optimization problems are formulated as a sequence of open-loop optimality systems in a receding-horizon control strategy. Many theoretical results concerning the forward and the optimal control problem are provided. In particular, it is shown that under appropriate assumptions the open-loop bilinear control function is unique. The res…
Predictive Chaos Control for the 1D-map of Action Potential Duration
2016
International audience; In the present work, a nonlinear control method namely predictive controlis investigated. The proposed method allows stabilizing unstable period-1 rhythm.Using mathematical analysis and computer simulations, we show that this methodcan be used to control chaotic behavior or pathological rhythms. As example, theresults are illustrated in the case of the 1D-map action potential duration (APDi+1)which modelizes the cardiac action potential duration as the function of the previousone (APDi).
Predictive control of convex polyhedron LPV systems with Markov jumping parameters
2012
The problem of receding horizon predictive control of stochastic linear parameter varying systems is discussed. First, constant coefficient matrices are obtained at each vertex in the interior of linear parameter varying system, and then, by considering semi-definite programming constraints, weight coefficients between each vertex are calculated, and the equal coefficients matrices for the time variable system are obtained. Second, in the given receding horizon, for each mode sequence of the stochastic convex polyhedron linear parameter varying systems, the optimal control input sequences are designed in order to make the states into a terminal invariant set. Outside of the receding horizon…
An analysis of model predictive control with integral action applied to digital displacement cylinders
2020
This article aims to analyze Model Predictive Control (MPC) for the control of multi-chamber cylinders. MPC with and without integral action has been introduced. Three different algorithms have been used to solve the optimization problem in the MPC. The different algorithms have been compared with an industrial solver. The influence of changing mass, choosing a different middle line pressure, system delays, signal noise, velocity estimation, and changing pressure levels has been investigated. It is concluded that for the small prediction horizon used in the paper a simple algorithm such as A can produce results as good as the previously used Differential Evolution algorithm in less than hal…