scholarly journals Disturbance Observer-Based Offset-Free Global Tracking Control for Input-Constrained LTI Systems with DC/DC Buck Converter Applications

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4079
Author(s):  
Kyunghwan Choi ◽  
Dong Soo Kim ◽  
Seok-Kyoon Kim
Keyword(s):  
Tracking Control ◽  
Disturbance Observer ◽  
Linear Time ◽  
Estimation Error ◽  
Open Loop ◽  
Buck Converter ◽  
Invariance Property ◽  
Linear Time Invariant ◽  
Global Tracking ◽  
Time Invariant

This paper presents an offset-free global tracking control algorithm for the input-constrained plants modeled as controllable and open-loop strictly stable linear time invariant (LTI) systems. The contribution of this study is two-fold: First, a global tracking control law is devised in such a way that it not only leads to offset-free reference tracking but also handles the input constraints using the invariance property of a projection operator embedded in the proposed disturbance observer (DOB). Second, the offset-free tracking property is guaranteed against uncertainties caused by plant-model mismatch using the DOB’s integral action for the state estimation error. Simulation results are given in order to demonstrate the effectiveness of the proposed method by applying it to a DC/DC buck converter.

Novel Gaussian State Estimator based on H2 Norm and Steady-State Variance

2020 ◽  
Author(s):  
Alesi Augusto De Paula ◽  
Víctor Costa da Silva Campos ◽  
Guilherme Vianna Raffo ◽  
Bruno Otávio Soares Teixeira
Keyword(s):  
Steady State ◽  
Linear Time ◽  
Estimation Error ◽  
Performance Criterion ◽  
Gaussian State ◽  
State Estimator ◽  
Linear Time Invariant ◽  
Linear Time Invariant Systems ◽  
Linear Time Invariant System ◽  
Time Invariant

This paper proposes a novel state estimator for discrete-time linear systems with Gaussian noise. The proposed algorithm is a fixed-gain filter, whose observer structure is more general than Kalman one for linear time-invariant systems. Therefore, the steady-state variance of the estimation error is minimized. For white noise stochastic processes, this performance criterion is reduced to the square H2 norm of a given linear time-invariant system. Then, the proposed algorithm is called observer H2 filter (OH2F). This is the standard Wiener-Hopf or Kalman-Bucy filtering problem. As the Kalman predictor and Kalman filter are well-known solutions for such a problem, they are revisited.

A Linear Time Varying Model for On-Off Valve Controlled Pneumatic Actuators

1990 ◽  
Vol 112 (4) ◽  
pp. 740-747 ◽  
Author(s):  
C. Kunt ◽  
R. Singh
Keyword(s):  
Linear Time ◽  
Digital Simulation ◽  
Control Valve ◽  
Open Loop ◽  
Rotary Valve ◽  
Pneumatic Actuators ◽  
Linear Time Invariant ◽  
Directional Control ◽  
Actuation Systems ◽  
Time Invariant

A new linear time varing (LTV) model has been developed for open loop, on-off valve controlled pneumatic actuation systems. This formulation is based on a periodic profile description for variable operating points and directional control valve flow variations. The dynamic behavior of the example case, a single acting cylinder controlled by a two way-two port rotary valve, under the cyclic pressure loading is obtained using the proposed LTV model. Experimental evidence and digital simulation predictions based on the nonlinear mathematical equations validate the analytical formulation. The proposed LTV model is found to be better and more applicable than linear time invariant (LTI) models used previously by many investigators.

Optimal Deterministic State Estimation—A First Step

1984 ◽  
Vol 106 (2) ◽  
pp. 176-178 ◽  
Author(s):  
R. G. Jacquot
Keyword(s):  
Objective Function ◽  
Linear Time ◽  
Estimation Error ◽  
Stochastic Estimation ◽  
Initial Condition ◽  
State Estimator ◽  
First Order ◽  
Linear Time Invariant ◽  
Linear Time Invariant Systems ◽  
Time Invariant

Optimal deterministic observers are derived for all first order linear time invariant systems. The optimization process consists of minimizing an objective function which is quadratic in the observer gain and in the estimation error. The objective function was chosen such that the resulting observer gains would be independent of system initial-condition which would, in general, be unknown to the state estimator. The results of this optimization are sensible in the light of the stochastic estimation results of Kalman.

Modeling and path-tracking control of a mobile wheeled robot with a differential drive

Robotica ◽  
1995 ◽  
Vol 13 (4) ◽  
pp. 401-410 ◽  
Author(s):  
R. M. DeSantis
Keyword(s):  
Tracking Control ◽  
Linear Time ◽  
Path Tracking ◽  
Modeling And Control ◽  
Differential Drive ◽  
Linear Time Invariant ◽  
Wheeled Robot ◽  
And Control ◽  
Time Invariant ◽  
Kinematic Properties

SummaryTopics relevant to modeling and control of mobile wheeled robots with a differential drive are discussed by assuming a motion that is planar and free from lateral and longitudinal slippages, and by taking into account dynamic and kinematic properties of the vehicle. Based on the concept of geometric path-tracking, a controller is designed that is a memoryless function of the lateral, heading, and velocity path-tracking offsets. If these offsets are kept small and the assigned tracking velocity is constant, then this controller may be given a linear, time-invariant and decoupled PID (Proportional + integral + derivative) structure.

Tracking control of linear time-invariant descriptor systems

1992 ◽  
Vol 23 (11) ◽  
pp. 1953-1963 ◽  
Author(s):  
R. J. SCHILLING ◽  
S. L. HARRIS ◽  
VICTOR LOVASS-NAGY
Keyword(s):  
Tracking Control ◽  
Linear Time ◽  
Descriptor Systems ◽  
Linear Time Invariant ◽  
Time Invariant
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