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This paper presents a self-triggered model predictive control (MPC) algorithm for constrained linear discrete-time systems in the presence of bounded state and output disturbances, when only the system output can be measured at triggering instants. The proposed algorithm mainly relies on a state estimator whose estimation error is bounded by an invariant set, and on the self-triggered robust model...
This paper considers the problem of robust estimation and constrained model predictive control (MPC). The paper deals with a discrete linear time-invariant system affected by additive bounded disturbances, whose states are measurable, but not directly accessible. In order to improve the control performance, a state estimator is desirable. The design problem of an observer based on zonotopes to estimate...
In this paper, a method of unmatched disturbance compensating integral sliding model predictive control for constrained linear discrete-time systems with bounded disturbances is presented. The basic idea is to shape a discrete-time integral sliding mode control into an optimal constrained control problem with model predictive control. By choosing another projection matrix in the integral sliding surface,...
In this work, we propose a novel robust dynamic controller in order to stabilize a walking biped robot with input and output constraints. Firstly, the trajectory of the robot is generated via the Zero Moment Point method based on the resolution of a convex optimization problem with Linear Matrix Inequalities. Then, the tracking of a referential trajectory is insured by the design of an optimal dynamic...
The paper discusses online course materials used in the training for the simplest robust controller. It has been proposed for the plant that can be represented by simple gain and dead time. The controller design is realized by choosing appropriate model parameters and and by specifying the disturbance observer filter characterized by its order and by the time constant. All parameters have to be estimated...
The main objective of this contribution is a new development of the continuous-time generalised predictive control (CGPC) resulting in a Youla-based CGPC design procedure for both minimumphase and nonminimum-phase uncertain SISO systems. The derived design procedure consists of two steps. In the first step nominal stability and nominal performance of the control system are established by using a fully...
The paper explores the advantage of Subspace Predictive Control for Linear Parameter Varying systems (SPC LPV) in supervisory and in robust control scheme. Closed-loop subspace identification technique is used to identify the I/O LPV predictor for optimal control application. This type of the SPC LPV is a data driven approach.
A method is described in order to address control problems in the presence of state and input constraints for noisy linear plants under partial state information. The approach consists in reducing the problem to a full-state feedback strategy for an auxiliary system subject to suitably modified constraints and disturbances. This is particularly useful in the context of model predictive control since...
An encoderless predictive control method for doubly fed induction generator is proposed in this paper. Doubly fed induction generators are commonly used in wind turbine applications because of fast dynamic response and grid synchronizing capability. Dependency on speed measurement is an important drawback in wind turbine applications. A reduced order model based observer is used to estimate the torque,...
In the last years many research studies have presented simulation or experimental results using Nonlinear Model Predictive Control (NMPC). The computation times needed for the solution of the resulting nonlinear optimization problems are in many cases no longer an obstacle due to the advances in algorithms and computational power. However, NMPC is not yet an industrial reality as its linear counterpart...
Enumerative nonlinear model predictive control for speed tracking problem of linear induction motors has been presented in [1], where the authors show that this control scheme has better performance as compared to direct torque control. In this paper, the authors show that using a load observer for integral action, the performance can be further improved. Specifically simulation results show that...
This paper presents a continuous model predictive control (MPC) for a class of linear disturbed system under mismatching condition by using a disturbance observer. The disturbance estimates are introduced in the output prediction to correct the errors raised by disturbances and uncertainties, which eventually leads to the desired offset-free tracking performance. The feasibility of the proposed method...
A nonlinear optimal predictive controller for current control of paralleled three-phase three-leg active power filter is presented. The objective is to track distorted currents with sudden changes. In order to achieve robustness to parameter uncertainties, a disturbance observer is introduced. The deduced control law has closed analytical form and on-line dynamic optimization is omitted and easy to...
This paper describes a new model predictive control method for buck DC-DC converters. The target is to achieve a good trade-off between fast response and low overshoot in the transient response of output voltage. In the well-known PWM-PI method, such trade-off is difficult to attain. The proposed model predictive control is based on an analytic algorithm which can be easily implemented on-line. Further,...
In this paper the problem of estimation and control of a bioprocess is approached. These tasks were performed using the interval observers and multiple model predictive control approaches. For estimation of nonlinear and uncertain biotechnological process a robust interval observer was used. The model predictive control, in particular nonlinear model predictive control, that is an advanced control...
In this paper, the application of offset-free model predictive control to a quadruple tanks system is addressed. The main goal is to eliminate steady state tracking error which is produced by model mismatch. The model mismatch between process and model used by model predictive controller is due to parameter uncertainty. It is shown how this uncertainty can be modeled by state disturbances. In this...
An efficient output feedback predictive control algorithm based on the state observer for constrained parameter uncertain discrete system is presented. The proposed method can give a solution to the problems that the error of the state estimation was not considered and the calculation was too large. The system was described by the polytopic description. A state observer was designed to realize the...
A robust generalized predictive controller (GPC) with a disturbance observer for a permanent magnet synchronous motor (PMSM) is presented. The proposed GPC controller combines the linear predictive control techniques together with the disturbance observer, which is designed to take into account the load torque variations and uncertain electrical and mechanical parameters. Stability of the closed-loop...
This paper studies the robustness of networked predictive control systems (NPCS) with uncertainties. A networked predictive control strategy that compensates for delay actively rather than passively is introduced to cope with - varying network delay and data dropout. The closed-loop networked predictive control system is described as a normal robust control system, which makes the control design and...
The Smith predictor is a well-known method for designing controllers for plants with I/O delays. Assuming the time delay known, this control configuration compensates the delay within the loop making it possible to design the controller just considering the (rational) delay-free part of the plant. Since the original Smith predictor does not allow to deal with unstable plants, intensive research has...
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