Robust adaptive control using multiple models, switching and tuning

Abstract

The supervisory control problem is analysed as an online robust design problem using switching to select the relevant models for designing the control law. The proposed supervisory control algorithm is based on the integration of concepts used in supervisory adaptive control, robust control and receding horizon control. It involves a two-stage adaptive control algorithm: (i) the identification of a time-varying set of models P L(k) , from the set of admissible models P L , that explains the input -output behaviour of the system, followed by (ii) the design of the control law using a parametric linear optimisation problem. The authors show that under the proposed supervisory control algorithm, the system output remains bounded for any bounded disturbance. The use of superstability concepts, together with certain assumptions on P L , allows us to establish overall performance and robust stability guarantees for the supervisory scheme and to include constrains in the closed-loop variables as well as in the controller structure. The relevant features of the proposed control algorithm are demonstrated in a numerical simulation.

Dynamic Sliding Mode Control Based on Multi-Model Switching Laws for the Depth Control of an Autonomous Underwater Vehicle

“…Equation (27) portrays the relationship between these two aforementioned closedloop control laws, i.e., equation (24) and equation (26). The chattering phenomenon would not be neglected if the two control laws switched with each other directly.…”

“…In other words, switched systems of the diving plane are described as shown in equation (27), corresponding to the switching control laws for the transition between these two controllers. A state-dependent switching control law is suggested.…”

Synthesis of multiple model switching controllers using H∞ theory for systems with large uncertainties

“…(B2) The parameters change instantaneously, but the time between two changes is long enough. Assumption (B1) is commonly used in adaptive control [6]~ [11] [18]. Under the assumption (B2), the state error before next change can attenuate to small value.…”

“…The introduction of this switching based adaptation approach has been successfully adopted by the control society, and the performance of this approach has been substantially improved by employing modern control approaches to design the candidate controllers, e.g. robust adaptive controller [11] and dissipativity theory based controller [12]. For instance, Bonis Ioannis [13] and Soliman M [14] have used the model predictive controller to handle uncertain model set, which leads to increased robustness.…”

A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty

“…Since the birth of modern control theory in the 1950s, control theory develops rapidly and has been successfully adopted in the aerospace application in the 1960s [7,8]. In the recent few decades, robust control has gained remarkable attentions due to the well adaptation ability in dealing with objects in uncertain and noisy environment [9][10][11][12][13]. With the maturation of robust control theory, Kharitonov interval theory, ∞ control theory, and structural singular value theory ( theory) have been widely used in aircraft controller design and trajectory tracking.…”