A Model Reference Adaptive Control Framework for Uncertain Dynamical Systems With High-Order Actuator Dynamics and Unknown Actuator Outputs

Author(s):  
Benjamin C. Gruenwald ◽  
K. Merve Dogan ◽  
Tansel Yucelen ◽  
Jonathan A. Muse

As it is well-known, the stability properties of model reference adaptive controllers can be seriously affected by the presence of actuator dynamics. To this end, the authors recently proposed linear matrix inequalities-based hedging approaches to compute the stability limits of model reference adaptive controllers in the presence of a) scalar actuator dynamics with known outputs, b) scalar actuator dynamics with unknown outputs, and c) high-order (linear time-invariant) actuator dynamics with known outputs. The common denominator of these approaches is that they have the capability to rigorously characterize the fundamental stability interplay between the system uncertainties and the necessary bandwidth of the actuator dynamics. Building on these results, the purpose of this paper is to extend the recent work by the authors to the general case, where there exist high-order actuator dynamics with unknown outputs in the closed-loop model reference adaptive control systems. For this purpose, we propose an observer architecture to estimate the unknown output of the actuator dynamics and use the estimated actuator output to design the linear matrix inequalities-based hedging framework. Remarkably, with the proposed observer, the sufficient stability condition in this case of unknown actuator outputs is identical to the case with known actuator outputs that was established in the prior work by the authors. Therefore, a control designer can utilize the proposed framework for practical applications when the output of the actuator dynamics is not measurable, and hence, unknown (e.g., in hypersonic vehicle applications). An illustrative numerical example complements the proposed theoretical contribution.

Author(s):  
Benjamin C. Gruenwald ◽  
Daniel Wagner ◽  
Tansel Yucelen ◽  
Jonathan A. Muse

Although model reference adaptive control has been used in numerous applications to achieve system performance without excessive reliance on dynamical system models, the presence of actuator dynamics can seriously limit the stability and the achievable performance of adaptive controllers. In this paper, an linear matrix inequalities-based hedging approach is developed and evaluated for model reference adaptive control of uncertain dynamical systems in the presence of actuator dynamics. The hedging method modifies the ideal reference model dynamics in order to allow correct adaptation that does not get affected due to the presence of actuator dynamics. Specifically, we first generalize the hedging approach to cover cases in which actuator output and is known and unknown. We next show the stability of the closed-loop dynamical system using tools from Lyapunov stability and linear matrix inequalities. Finally, an illustrative numerical example is provided to demonstrate the efficacy of the proposed linear matrix-inequalities-based hedging approach to model reference adaptive control.


Author(s):  
Abbas Zabihi Zonouz ◽  
Mohammad Ali Badamchizadeh ◽  
Amir Rikhtehgar Ghiasi

In this paper, a new method for designing controller for linear switching systems with varying delay is presented concerning the Hurwitz-Convex combination. For stability analysis the Lyapunov-Krasovskii function is used. The stability analysis results are given based on the linear matrix inequalities (LMIs), and it is possible to obtain upper delay bound that guarantees the stability of system by solving the linear matrix inequalities. Compared with the other methods, the proposed controller can be used to get a less conservative criterion and ensures the stability of linear switching systems with time-varying delay in which delay has way larger upper bound in comparison with the delay bounds that are considered in other methods. Numerical examples are given to demonstrate the effectiveness of proposed method.


Author(s):  
Pascal Nespeca ◽  
Nesrin Sarigul-Klijn

Any classical control design starts by first satisfying stability and then looking towards satisfying transient requirements. Similarly, a Model Reference Adaptive Control (MRAC) Method should start with a stability analysis. Lyapunov function analysis is first used to justify the stability of the adaptive scheme. Next, a numerical study is conducted to predict the stability behavior of three different MRAC methods in the presence of large unanticipated changes in the dynamics of an aircraft. The Model reference adaptive control methods studied are: Method:1, an adaptive gain method; Method:2, a Neural Network (NN) approximation technique; and, Method:3, a linear approximation technique. For comparison purposes, the aircraft is assumed to have Linear Time Invariant, LTI dynamics. Each algorithm is given full state feedback, an inaccurate reference model and a poor Linear Quadratic Regulator, LQR design for the true plant. It is seen that when the LQR stabilizes the true plant, the three algorithms all achieve the same steady state error to a step command. Numerical results predict the different types of stability behavior that the algorithms provide. It is seen that the Methods: 2 and 3 can only provide a bounded stability, whereas Method: 1 can provide an asymptotic stability. A robust static controller can satisfy stability, but a robust static controller that accommodates variations in plant dynamics might not always be able to match transient requirements as expected. Although there may be no analytical guarantee from adaptive controllers of transient performance, one might look at anecdotal performances.


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