7,216 research outputs found
Yet Another Tutorial of Disturbance Observer: Robust Stabilization and Recovery of Nominal Performance
This paper presents a tutorial-style review on the recent results about the
disturbance observer (DOB) in view of robust stabilization and recovery of the
nominal performance. The analysis is based on the case when the bandwidth of
Q-filter is large, and it is explained in a pedagogical manner that, even in
the presence of plant uncertainties and disturbances, the behavior of real
uncertain plant can be made almost similar to that of disturbance-free nominal
system both in the transient and in the steady-state. The conventional DOB is
interpreted in a new perspective, and its restrictions and extensions are
discussed
MIMO First and Second Order Discrete Sliding Mode Controls of Uncertain Linear Systems under Implementation Imprecisions
The performance of a conventional model-based controller significantly
depends on the accuracy of the modeled dynamics. The model of a plant's
dynamics is subjected to errors in estimating the numerical values of the
physical parameters, and variations over operating environment conditions and
time. These errors and variations in the parameters of a model are the major
sources of uncertainty within the controller structure. Digital implementation
of controller software on an actual electronic control unit (ECU) introduces
another layer of uncertainty at the controller inputs/outputs. The
implementation uncertainties are mostly due to data sampling and quantization
via the analog-to-digital conversion (ADC) unit. The failure to address the
model and ADC uncertainties during the early stages of a controller design
cycle results in a costly and time consuming verification and validation (V&V)
process. In this paper, new formulations of the first and second order discrete
sliding mode controllers (DSMC) are presented for a general class of uncertain
linear systems. The knowledge of the ADC imprecisions is incorporated into the
proposed DSMCs via an online ADC uncertainty prediction mechanism to improve
the controller robustness characteristics. Moreover, the DSMCs are equipped
with adaptation laws to remove two different types of modeling uncertainties
(multiplicative and additive) from the parameters of the linear system model.
The proposed adaptive DSMCs are evaluated on a DC motor speed control problem
in real-time using a processor-in-the-loop (PIL) setup with an actual ECU. The
results show that the proposed SISO and MIMO second order DSMCs improve the
conventional SISO first order DSMC tracking performance by 69% and 84%,
respectively. Moreover, the proposed adaptation mechanism is able to remove the
uncertainties in the model by up to 90%.Comment: 10 pages, 11 figures, ASME 2017 Dynamic Systems and Control
Conferenc
Disturbance Observer-based Robust Control and Its Applications: 35th Anniversary Overview
Disturbance Observer has been one of the most widely used robust control
tools since it was proposed in 1983. This paper introduces the origins of
Disturbance Observer and presents a survey of the major results on Disturbance
Observer-based robust control in the last thirty-five years. Furthermore, it
explains the analysis and synthesis techniques of Disturbance Observer-based
robust control for linear and nonlinear systems by using a unified framework.
In the last section, this paper presents concluding remarks on Disturbance
Observer-based robust control and its engineering applications.Comment: 12 pages, 4 figure
Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
In this work, a nonlinear model predictive controller is developed for a
batch polymerization process. The physical model of the process is
parameterized along a desired trajectory resulting in a trajectory linearized
piecewise model (a multiple linear model bank) and the parameters are
identified for an experimental polymerization reactor. Then, a multiple model
adaptive predictive controller is designed for thermal trajectory tracking of
the MMA polymerization. The input control signal to the process is constrained
by the maximum thermal power provided by the heaters. The constrained
optimization in the model predictive controller is solved via genetic
algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference
Vibration suppression in multi-body systems by means of disturbance filter design methods
This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort
Feedback control of unsupported standing in paraplegia. Part I: optimal control approach
This is the first of a pair of papers which describe an investigation into the feasibility of providing artificial balance to paraplegics using electrical stimulation of the paralyzed muscles. By bracing the body above the shanks, only stimulation of the plantarflexors is necessary. This arrangement prevents any influence from the intact neuromuscular system above the spinal cord lesion. Here, the authors extend the design of the controllers to a nested-loop LQG (linear quadratic Gaussian) stimulation controller which has ankle moment feedback (inner loops) and inverted pendulum angle feedback (outer loop). Each control loop is tuned by two parameters, the control weighting and an observer rise-time, which together determine the behavior. The nested structure was chosen because it is robust, despite changes in the muscle properties (fatigue) and interference from spasticity
Application of a Combined Active Control and Fault Detection Scheme to an Active Composite Flexible Structure.
In this paper, the problem of increasing reliability of active control procedure is considered. Indeed, a design method of rejection perturbation in presence of potentially faults, on a flexible structure with integrated piezo-ceramics, is presented. The piezo-ceramics are used as actuators and sensors. A single unit based solution, which handles both control action and fault diagnosis is proposed. The algorithm uses H∞ optimization techniques. A full order model of the structure is first obtained via both finite-element (FE) approach and identification procedure. This model is then reduced in order to be used in our robust approach. By a suitable choice of weightings functions, the provided method is able to reject disturbance robustly and to estimate occurred faults. The case of sensors and actuators faults is discussed. The choice of weightings for diagnosis and control systems is also tackled. Finally, the effectiveness of this integrated method is confirmed by both simulation and experimental results
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Multiobjective control of a four-link flexible manipulator: A robust H∞ approach
Copyright [2002] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper presents an approach to robust H∞ control of a real multilink flexible manipulator via regional pole assignment. We first show that the manipulator system can be approximated by a linear continuous uncertain model with exogenous disturbance input. The uncertainty occurring in an operating space is assumed to be norm-bounded and enter into both the system and control matrices. Then, a multiobjective simultaneous realization problem is studied. The purpose of this problem is to design a state feedback controller such that, for all admissible parameter uncertainties, the closed-loop system simultaneously satisfies both the prespecified H∞ norm constraint on the transfer function from the disturbance input to the system output and the prespecified circular pole constraint on the closed-loop system matrix. An algebraic parameterized approach is developed to characterize the existence conditions as well as the analytical expression of the desired controllers. Third, by comparing with the traditional linear quadratic regulator control method in the sense of robustness and tracking precision, we provide both the simulation and experimental results to demonstrate the effectiveness and advantages of the proposed approach
Neural Network Based Min-Max Predictive Control. Application to a Heat Exchanger
IFAC Adaptation and Learning in Control and Signal Processing. Cemobbio-Como. Italy. 2001Min-max model predictive controllers (MMMPC) have been proposed for the control of linear plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the numerical optimization problem that has to be solved at every sampling time. This fact severely limits the class of processes in which this control is suitable. In this paper the use of a Neural Network (NN) to approximate the solution of the min-max problem is proposed. The number of inputs of the NN is determined by the order and time delay of the model together with the control horizon. For large time delays the number of inputs can be prohibitive. A modification to the basic formulation is proposed in order to avoid this later problem. Simulation and experimental results are given using a heat exchanger
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