Direct and Indirect Model Reference Adaptive Control for Multivariable Piecewise Affine Systems

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 …”

Robust integral feedback control based on interval observer for stabilising parameter‐varying systems

“…It should be mentioned that generated switching signal, which involves all 6 subsystems, has frequent switching in some interval times. Sometimes, as completely described in [30, 49], despite the continuous Lyapunov function decreases with time, if the vector fields of two neighbouring regions both points towards the switching hyperplane, the system trajectories are not able to simply cross between regions. Instead, the state of the system is unable to leave the hyperplane and an additional mode (sliding mode) is created.…”

Direct data‐driven design of switching controllers

“…Analogously, the hypothesis of exact state knowledge is shared by the approach proposed in 43 for asynchronous adaptive tracking control for switched system, where the authors further assume prior knowledge on the lower and upper bounds on the controller parameters. More recently, two MRAC approaches for PWA systems have been proposed in, 44 which rely on different strategies to adaptively tune the controller's parameters. Indeed, in the direct method presented therein, parameter tuning is based on the tracking error, whereas the indirect approach relies on a time-varying estimate of the model of the system.…”