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This paper considers the optimization landscape of linear dynamic output feedback control with $\mathcal{H}_\infty$ robustness constraints.
J. C. Doyle, “Guaranteed margins for LQG regulators,” IEEE Transactions on Automatic Control , vol. 23, no. 4, pp. 756–757, 1978
1978
Earlier work this paper cites.
D. H. Martin, “Connected level sets, minimizing sets, and uniqueness in optimization,” Journal of Optimization Theory and Applications , vol. 36, no. 1, pp. 71–91, 1982
1982
Earlier work this paper cites.
P. Whittle, Risk-sensitive Optimal Control . Wiley Chichester, 1990
1990
Earlier work this paper cites.
P. Gahinet and P. Apkarian, “A linear matrix inequality approach to ℋ ∞ \mathcal{H}_{\infty} control,” International journal of robust and nonlinear control , vol. 4, no. 4, pp. 421–448, 1994
1994
Earlier work this paper cites.
K. Zhou, J. C. Doyle, and K. Glover, Robust and optimal control . Prentice Hall, 1996
1996
Earlier work this paper cites.
C. Scherer, P. Gahinet, and M. Chilali, “Multiobjective output-feedback control via LMI optimization,” IEEE Transactions on Automatic Control , vol. 42, no. 7, pp. 896–911, 1997
1997
Earlier work this paper cites.
G. Dullerud and F. Paganini, A Course in Robust Control Theory: A Convex Approach . Springer, 1999
1999
Earlier work this paper cites.
J. M. Ortega and W. C. Rheinboldt, Iterative solution of nonlinear equations in several variables . SIAM, 2000
2000
Earlier work this paper cites.
J. M. Lee, Introduction to Smooth Manifolds , 2nd ed. Springer Science & Business Media, 2013
2013
Earlier work this paper cites.
M. Fazel, R. Ge, S. Kakade, and M. Mesbahi, “Global convergence of policy gradient methods for the linear quadratic regulator,” in International Conference on Machine Learning , vol. 80, 2018, pp. 1467–1476
2018
Cited alongside, same era.
D. Malik, A. Pananjady, K. Bhatia, K. Khamaru, P. Bartlett, and M. Wainwright, “Derivative-free methods for policy optimization: Guarantees for linear quadratic systems,” in International Conference on Artificial Intelligence and Statistics , 2019, pp. 2916–2925
2019
Cited alongside, same era.
H. Feng and J. Lavaei, “On the exponential number of connected components for the feasible set of optimal decentralized control problems,” in 2019 American Control Conference (ACC) , 2019, pp. 1430–1437
2019
Cited alongside, same era.
2019
Cited alongside, same era.
H. Mohammadi, A. Zare, M. Soltanolkotabi, and M. R. Jovanovic, “Convergence and sample complexity of gradient methods for the model-free linear quadratic regulator problem,” IEEE Transactions on Automatic Control , 2021
2021
Later among the works it cites.
K. Zhang, B. Hu, and T. Basar, “Policy optimization for ℋ 2 \mathcal{H}_{2} linear control with ℋ ∞ \mathcal{H}_{\infty} robustness guarantee: Implicit regularization and global convergence,” SIAM Journal on Control and Optimization , vol. 59, no. 6, pp. 4081–4109, 2021
2021
Later among the works it cites.
K. Zhang, X. Zhang, B. Hu, and T. Başar, “Derivative-free policy optimization for linear risk-sensitive and robust control design: Implicit regularization and sample complexity,” in Thirty-Fifth Conference on Neural Information Processing Systems , 2021
2021
Later among the works it cites.
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L. Furieri, Y. Zheng, and M. Kamgarpour, “Learning the globally optimal distributed LQ regulator,” in Learning for Dynamics and Control , 2020, pp. 287–297
2020
Cited alongside, same era.
K. Zhang, B. Hu, and T. Başar, “On the stability and convergence of robust adversarial reinforcement learning: A case study on linear quadratic systems,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Cited alongside, same era.
B. Gravell, P. M. Esfahani, and T. Summers, “Learning optimal controllers for linear systems with multiplicative noise via policy gradient,” IEEE Transactions on Automatic Control , vol. 66, no. 11, pp. 5283–5298, 2020
2020
Cited alongside, same era.
J. P. Jansch-Porto, B. Hu, and G. E. Dullerud, “Convergence guarantees of policy optimization methods for Markovian jump linear systems,” in American Control Conference , 2020, pp. 2882–2887
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Later among the works it cites.
Y. Sun and M. Fazel, “Learning optimal controllers by policy gradient: Global optimality via convex parameterization,” in 2021 60th IEEE Conference on Decision and Control (CDC) , 2021, pp. 4576–4581
2021
Later among the works it cites.
I. Fatkhullin and B. Polyak, “Optimizing static linear feedback: Gradient method,” SIAM Journal on Control and Optimization , vol. 59, no. 5, pp. 3887–3911, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2022
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