Nonlinear Dynamical Systems and Control: A Lyapunov-Based Approach

By Wassim M. Haddad, V. Chellaboina. 2008

1,357 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/ce6406918b9e029a89eb093a50281888a996047b

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In Control and Systems Engineering

Citations in the corpus

Toward a Science of Cyber–Physical System Integration (2012, background): “Since different suppliers implement their products with different strategies, engineering processes, and tools, OEMs always face the integration challenge, regardless of whether the components are designed and developed for the same vehicle products.”
Cooperative Optimal Control for Multi-Agent Systems on Directed Graph Topologies (2014, methodology): “Then classical results on optimality, [20], [21] state that the solution of optimal problem can be obtained by forming the Hamiltonian”
Neural Lyapunov Control (2020, methodology): “To control nonlinear systems outside their linearizable regions, we need to rely on Lyapunov methods [13].”
Theory and Flight-Test Validation of a Concurrent-Learning Adaptive Controller (2011, methodology): “Combined Instantaneous and Concurrent-Learning NN, Proof of Boundedness In this sectionLyapunov-like analysis is used to guarantee stability of a particular concurrent-learning adaptive law (Theorem 2) in the sense of UUB [26,27].”
Adaptive Critic Nonlinear Robust Control: A Survey (2017, methodology): “Before proceeding, we give an assumption to the system uncertainty as used in [137], [138], [150], and [151].”
Concurrent learning for convergence in adaptive control without persistency of excitation (2010, background): “6 from [6] exponential stability of the zero solution W̃ ≡ 0 of the parameter error dynamics of equation 8 is established.”
Multi-agent consensus algorithm with obstacle avoidance via optimal control approach (2010, background): “A novel avoidance penalty function is constructed based on an inverse optimal control strategy [16, 17] such that an analytical optimal control law can be obtained.”
Integrated Optimal Formation Control of Multiple Unmanned Aerial Vehicles (2012, methodology): “A novel inverse optimal control strategy [26], [27] is adopted to incorporate a nonquadratic avoidance penalty function into the cost function such that an analytical optimal control law can be obtained.”
Convex Computation of the Region of Attraction of Polynomial Control Systems (2012, methodology): “The beneﬁts of our occupation measure approach are overall the convexity of the problem of ﬁnding the ROA, and the availability of publicly available software to implement and solve the hierarchy of LMI relaxations.”
An Adaptive Control Architecture for Mitigating Sensor and Actuator Attacks in Cyber-Physical Systems (2017, methodology): “In this case, it follows from converse Lyapunov theory [22] that for every positive definite matrix R ∈ Rn×n , there exists a unique positive-definite P ∈ Rn×n satisfying”
Reinforcement learning-based control of drug dosing for cancer chemotherapy treatment. (2017, background): “In general, the problem of designing optimal controllers for onlinear systems is challenging [25] .”