Passification-based adaptive control: Uncertain input and output delays

Abstract

a b s t r a c tFor a class of uncertain systems we analyze passification-based adaptive controller in the presence of small, unavoidable input and output time-varying delays as may be present in controller implementation. We derive upper bounds for time delays such that in some domain of initial conditions the states of the closed-loop system tend to zero, whereas an adaptive controller gain tends to a constant value. The results are semi-global, that is the domain of initial conditions is bounded but can be made arbitrary large by tuning an appropriate controller parameter. For the first time, we apply an adaptive controller to linear uncertain networked control systems, where sensors, controllers, and actuators exchange their information through communication networks. The efficiency of the results is demonstrated by the example of adaptive network-based control of an aircraft.

Time-varying group formation analysis and design for second-order multi-agent systems with directed topologies

“…By solving the differential equation (6) and taking advantage of (23)- (25), the conclusion of Theorem 2 can be derived.…”

“…Since (B 1 B T 2 + B 2 α, B 2 ) is stabilizable, the existence of the solution to (26) can be guaranteed, which means that one can always obtain the desired gain matrix K. It should be pointed that the design approach in Theorem 3 requires the global information of the interaction topology to calculate the minimum nonzero eigenvalue of the Laplacian matrix. To reduce this constraint, one can consider introducing some adaptive approaches (see, e.g., [25]- [28]) into the protocol (3) to estimate the required global information on the interaction topology (see, e.g., [29] and [30]).…”

Model‐based vs data‐driven adaptive control: An overview

“…To conclude this section about fully model‐based adaptation, we can cite other recent works, ie, post the latest general survey paper, which can be classified under the model‐based paradigm: for nonlinear models,) for models with time delay,) with parameter‐independent realization controllers, with input/output quantization,) under state constraints,) under inputs and actuator‐bandwidth constraints,) for Markovian jump systems,) for switched systems,) for partial differential equation (PDE)–based models,) for nonminimum/minimum‐phase systems,) to achieve adaptive regulation and disturbance rejection,) multiple‐model and switching adaptive control,) linear quadratic regulator (LQR)–based adaptive control, model predictive control–based adaptive control,) applications of model‐based adaptive control,) for sensor/actuator fault mitigation,) for rapidly time‐varying uncertainties, nonquadratic Lyapunov function–based MRAC, for stochastic systems,) retrospective cost adaptive control, persistent excitation–free/data accumulation–based control or concurrent adaptive control, sliding mode–based adaptive control,) set‐theoretic–based adaptive controller with performance guarantees, sampled data systems, and robust adaptive control …”

Prediction-based control for LTI systems with uncertain time-varying delays and partial state knowledge

“…The drawback of these two works is that the delay has to be known in advance to compute the controllers. In Selivanov, Fridman, and Fradkov (2015), an adaptive memory free controller is proposed and it does not need the delay value. However, it cannot compensate for arbitrarily large delays.…”