6,624 research outputs found
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
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
Linear Control Theory with an ââ Optimality Criterion
This expository paper sets out the principal results in ââ control theory in the context of continuous-time linear systems. The focus is on the mathematical theory rather than computational methods
A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization
The frequency-domain data of a multivariable system in different operating
points is used to design a robust controller with respect to the measurement
noise and multimodel uncertainty. The controller is fully parametrized in terms
of matrix polynomial functions and can be formulated as a centralized,
decentralized or distributed controller. All standard performance
specifications like , and loop shaping are considered in a
unified framework for continuous- and discrete-time systems. The control
problem is formulated as a convex-concave optimization problem and then
convexified by linearization of the concave part around an initial controller.
The performance criterion converges monotonically to a local optimal solution
in an iterative algorithm. The effectiveness of the method is compared with
fixed-structure controllers using non-smooth optimization and with full-order
optimal controllers via simulation examples. Finally, the experimental data of
a gyroscope is used to design a data-driven controller that is successfully
applied on the real system
Modeling and supervisory control design for a combined cycle power plant
The traditional control strategy based on PID controllers may be unsatisfactory when dealing with processes with large time delay and constraints. This paper presents a supervisory model based constrained predictive controller (MPC) for a combined cycle power plant (CCPP). First, a non-linear dynamic model of CCPP using the laws of physics was proposed. Then, the supervisory control using the linear constrained MPC method was designed to tune the performance of the PID controllers by including output constraints and manipulating the set points. This scheme showed excellent tracking and disturbance rejection results and improved performance compared with a stand-alone PID controllerâs scheme
Minimax frequency domain performance and robustness optimization of linear feedback systems
It is shown that feedback system design objectives, such as disturbance attenuation and rejection, power and bandwidth limitation, and robustness, may be expressed in terms of required bounds of the sensitivity function and its complement on the imaginary axis. This leads to a minimax frequency domain optimization problem, whose solution is reduced to the solution of a polynomial equation
Design of generalized minimum variance controllers for nonlinear multivariable systems
The design and implementation of Generalized Minimum Variance control laws for nonlinear multivariable systems that can include severe nonlinearities is considered. The quadratic cost index minimised involves dynamically weighted error and nonlinear control signal costing terms. The aim here is to show the controller obtained is simple to design and implement. The features of the control law are explored. The controller obtained includes an internal model of the process and in one form is a nonlinear version of the Smith Predictor
- âŠ