Central suboptimalH∞controller design for linear time-varying systems with unknown parameters

2011 ◽  
Vol 42 (5) ◽  
pp. 709-716 ◽  
Author(s):  
Michael V. Basin ◽  
Pedro Soto ◽  
Dario Calderon-Alvarez
Keyword(s):  
Controller Design ◽  
Linear Time ◽  
Time Varying ◽  
Unknown Parameters ◽  
Time Varying Systems

Iterative learning identification and control for point-to-point tracking of linear time-varying systems with unknown parameters and stochastic noise

2017 ◽  
Vol 40 (13) ◽  
pp. 3834-3845 ◽  
Author(s):  
Yan Geng ◽  
Xiaoe Ruan
Keyword(s):  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Time Varying ◽  
Unknown Parameters ◽  
Point Tracking ◽  
Time Varying Systems ◽  
System Output ◽  
Point To Point ◽  
And Control

In this paper, an interactive iterative learning identification and control (ILIC) scheme is developed for a class of discrete-time linear time-varying systems with unknown parameters and stochastic noise to implement point-to-point tracking. The identification is to iteratively estimate the unknown system parameter matrix by adopting the gradient-type technique for minimizing the distance of the system output from the estimated system output, whilst the control law is to iteratively upgrade the current control input with the current point-to-point tracking error scaled by the estimated system parameter matrix. Thus, the iterative learning identification and the iterative learning control are scheduled in an interactive mode. By means of norm theory, the boundedness of the discrepancy between the system matrix estimation and the real one is derived, whilst, by the manner of the statistical technique, it is conducted that the mathematical expectation of the tracking error monotonically converges to nullity and the variance of the tracking error is bounded. Numerical simulations exhibit the validity and effectiveness of the proposed ILIC scheme.

scholarly journals Adaptive Data-Driven Control for Linear Time Varying Systems

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 167
Author(s):  
Talal Abdalla
Keyword(s):  
Design Problem ◽  
Controller Design ◽  
Reference Model ◽  
Linear Time ◽  
Convex Relaxation ◽  
Data Driven ◽  
Time Varying ◽  
Model Matching ◽  
Control Approach ◽  
Time Varying Systems

In this paper, we propose an adaptive data-driven control approach for linear time varying systems, affected by bounded measurement noise. The plant to be controlled is assumed to be unknown, and no information in regard to its time varying behaviour is exploited. First, using set-membership identification techniques, we formulate the controller design problem through a model-matching scheme, i.e., designing a controller such that the closed-loop behaviour matches that of a given reference model. The problem is then reformulated as to derive a controller that corresponds to the minimum variation bounding its parameters. Finally, a convex relaxation approach is proposed to solve the formulated controller design problem by means of linear programming. The effectiveness of the proposed scheme is demonstrated by means of two simulation examples.

Input-Constrained Controller Design of Linear Time-Varying Systems Based on Piecewise State Estimation

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Feng Tan ◽  
Mingzhe Hou ◽  
Haihong Zhao ◽  
Guangren Duan
Keyword(s):  
Finite Time ◽  
Controller Design ◽  
Optimization Problems ◽  
Linear Time ◽  
Time Interval ◽  
Time Varying ◽  
Input Constraints ◽  
Time Control ◽  
State Evolution ◽  
Time Varying Systems

Finite-time control problem of linear time-varying systems with input constraints is considered in this paper. Successive ellipsoidal approximations are used to estimate the state evolution of linear time-varying systems during a certain finite-time interval. An algorithm to design a controller based on approximations of state evolution is proposed. According to the proposed algorithm, the speed of state approaching equilibrium is optimized piecewisely using admissible control. The controller gain can be obtained by solving several quasi-convex optimization problems, which makes the design process computationally tractable. Simulation results show that the proposed controller can quickly reduce state deviation without violating input constraints.

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