Search results for "Predictive control"

A Robust Predictive Current Control for Three-Phase Grid-Connected Inverters

2009

This paper presents a new predictive control algorithm for grid-connected current-controlled inverters. The control combines a two-sample deadbeat control law with a Luenberger observer to estimate the future value of the grid currents. The resulting control offers robustness against the computational delay inherent in the digital implementation and considerably enhances the gain and phase margins of the previous predictive controls while maintaining the high-speed response typical of the deadbeat controllers.

An ant colony optimization-based fuzzy predictive control approach for nonlinear processes

2015

In this paper, a new approach for designing an adaptive fuzzy model predictive control (AFMPC) based on the ant colony optimization (ACO) is proposed. On-line adaptive fuzzy identification is introduced to identify the system parameters. These parameters are used to calculate the objective function based on a predictive approach and structure of RST control. Then the optimization problem is solved based on an ACO algorithm, used at the optimization process in AFMPC to determine optimal controller parameters of RST control. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, where the proposed approach provides better performances compared with p…

Multiple UAV cooperative path planning via neuro-dynamic programming

2004

In this paper, a team of n unmanned air-vehicles (UAVs) in cooperative path planning is given the task of reaching the assigned target while i) avoiding threat zones ii) synchronizing minimum time arrivals on the target, and iii) ensuring arrivals coming from different directions. We highlight three main contributions. First we develop a novel hybrid model and suit it to the problem at hand. Second, we design consensus protocols for the management of information. Third, we synthesize local predictive controllers through a distributed, scalable and suboptimal neuro-dynamic programming (NDP) algorithm.

Predictive dead time controller for GaN-based boost converters

2017

A dynamic dead time controller is presented, specifically intended to operate in synchronous boost converters based on GaN field-effect transistor switches. These transistors have a reduced stored charge with respect to silicon metal–oxide–semiconductor field-effect transistors with similar breakdown voltage and series resistance, and can operate at higher frequencies with reduced switching losses. On the other hand, the voltage drop in reverse conduction is typically more than doubled with respect to silicon devices resulting in relevant power losses during the free-wheeling phases. Therefore, dynamic control of dead time can be profitably applied even in converters operating in the tens o…

A simplified predictive control of constrained Markov jump system with mixed uncertainties

2014

Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/475808 Open Access A simplified model predictive control algorithm is designed for discrete-time Markov jump systems with mixed uncertainties. The mixed uncertainties include model polytope uncertainty and partly unknown transition probability. The simplified algorithm involves finite steps. Firstly, in the previous steps, a simplified mode-dependent predictive controller is presented to drive the state to the neighbor area around the origin. Then the trajectory of states is driven as expected to the origin by the final-step mode-independent pre…

Extended Horizon Adaptive Model Algorithmic Control

1997

Abstract A new, original, robust adaptive control strategy termed Extended Horizon Adaptive Model Algorithmic Control is presented. In EHAMAC, a new, combined, ’single-loop’/’cascade’ adaptive least-squares parameter estimator is coupled with a new, simple but powerful Extended Horizon Model Algorithmic Control so that open-loop stable non-minimum phase systems can be effectively controlled in the time-varying environment. In the new, cascade structure of the ALS estimator, the covariance windup and blowup are totally eliminated. Moreover, the sacramental square-root update of the covariance matrix is no longer needed On the other hand, employing EHMAC facilitates robustness design so that …

Adaptive estimation of Laguerre models with time-varying delay

2000

Abstract An Orthonormal Basis Functions (OBF) approach is effectively used in adaptive parameter estimation of linear(ized) open-loop stable, possibly nonminimum phase plants with time-varying delay. In particular, discrete Laguerre models are considered in detail. A special attention is paid to the numerical conditioning issue in case of ’poor’ excitation of a plant under control, where OBF models are of particular value. Closed-loop predictive control simulations confirm the usefulness of adaptive OBF modelling, especially for systems with time-varying delays.

Mixed integer optimal compensation: Decompositions and mean-field approximations

2012

Mixed integer optimal compensation deals with optimizing integer- and real-valued control variables to compensate disturbances in dynamic systems. The mixed integer nature of controls might be a cause of intractability for instances of larger dimensions. To tackle this issue, we propose a decomposition method which turns the original n-dimensional problem into n independent scalar problems of lot sizing form. Each scalar problem is then reformulated as a shortest path one and solved through linear programming over a receding horizon. This last reformulation step mirrors a standard procedure in mixed integer programming. We apply the decomposition method to a mean-field coupled multi-agent s…

Visual servo control using orthinormal polynomial

2003

This paper describes an application of the visual servoing approach to vision-based control in robotics. The basic idea addresses the use of a vision sensor in the feedback loop within the controlled vision framework. It consists in tracking of arbitrary 3-D objects travelling at unknown velocities in a 2-D space (depth is given as known). Once the necessary modeling stage is performed, the framework becomes one of automatic control, and naturally stability, performance and robustness questions arise. Here, we consider to track line segments corresponding to the edges extracted from the image being analyzed. Two representations for a line segment are presented and discussed, and an appropri…

2014

This paper deals with the problem of robust model predictive control (RMPC) for a class of linear time-varying systems with constraints and data losses. We take the polytopic uncertainties into account to describe the uncertain systems. First, we design a robust state observer by using the linear matrix inequality (LMI) constraints so that the original system state can be tracked. Second, the MPC gain is calculated by minimizing the upper bound of infinite horizon robust performance objective in terms of linear matrix inequality conditions. The method of robust MPC and state observer design is illustrated by a numerical example.