Adaptive Control: Stability, Convergence and Robustness

By S. Sastry, Marc Bodson. 1989

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Citations in the corpus

DART: Noise Injection for Robust Imitation Learning (2017, background): “A condition was proposed to correct for this known as persistance excitation, which requires the training data to be informative enough to learn a robust model [15].”
Learning from neural control (2006, background): “Thus, effective learning capabilities are incorporated into the controller design to make learning control possible.”
A Survey of Collectives (2004, background): “Adaptive control [7, 196], and in particular adaptive control involving locally weighted RL algorithms [9, 165], constitute a broadly applicable framework for controlling small, potentially inexactly modeled systems.”
A novel actor-critic-identifier architecture for approximate optimal control of uncertain nonlinear systems (2013, methodology): “Using Theorem 2 .5.1 in Sastry and Bodson (1989), the nominal system ˙ ˜ W c = −η c Γ ψψ T ˜ W c (44) is globally exponentially stable, if the bounded signal ψ(t) is PE, i.e. µ 2 I ≥  t 0 +δ t 0 ψ(τ )ψ(τ ) T dτ ≥ µ 1 I ∀t 0 ≥ 0, for some positive constants µ 1 , µ 2 , δ ∈ R.”
A feedback-based implementation scheme for batch process optimization (2000, background): “Some of the drawbacks of this approach include: (i) high computational burden, especially in the presence of state and input constraints; (ii) necessity of full-state information; (iii) possible infeasibility of the solution obtained [3]; (iv) con ̄ict between parameter estimation which requires per”
Robust Adaptive Control of Coupled Parallel Piezo-Flexural Nanopositioning Stages (2009, methodology): “It is well known that asymptotic robust schemes such as sliding-mode control are not practical due to the chatter phenomenon [28], but adaptive methods are able to effectively eliminate the effects of uncertainties in the constant parameters [32].”
Adaptive robust precision motion control of linear motors with negligible electrical dynamics: theory and experiments (2001, unknown): “) = [Proj6 t ('t),"', Projt, (.p)]r is defined in [7] [12] as 0 if0t = 0/max and • i > 0 Proj~(.i) = 0 ifti = 0imin and • i < 0 (10) • ~ otherwise It can be shown [7] [12] that for any adaptation function x, the projection mapping used in (10) guarantees P1 6 ~ ~ & {6 : 0~ _< 6 _< 0rex}”
Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints (2014, background): “Useful connections are made between robustness analysis of adaptive controllers and Lyapunov theory in [37].”
Robust adaptive finite‐time parameter estimation and control for robotic systems (2015, background): “ksC 1/ in (2) is stable, minimum phase, and strictly proper [2], thenˆf defined in (2) is PE onceˆ defined in (1) is PE becauseˆf is the filtered version ofˆ.”
Structure learning in action (2010, methodology): “The theoretical literature has primarily investigated parametric adaptive control as this allows, by design, the adaptive control problem to be turned into a parametric optimization problem for which established system identification techniques can be employed to determine the parameters of the fixe”
Control: A perspective (2014, background): “The increased knowledge in adaptive control that came from all this work is well documented in books [187,259,11,400,37,494,580,334].”
Provably safe and robust learning-based model predictive control (2011, unknown): “This class of learning is often used in adaptive control [64,6].”