Search · four archives
Search · four archives
23 papers · ranked by Valyu relevance
Aditya Gahlawat, Sambhu H. Karumanchi, Naira Hovakimyan
Data-driven machine learning methodologies have attracted considerable attention for the control and estimation of dynamical systems. However, such implementations suffer from a lack of predictability and robustness. Thus, adoption of data-driven tools has been minimal for safety-aware applications despite their…
Insoon Yang
Standard stochastic control methods assume that the probability distribution of uncertain variables is available. Unfortunately, in practice, obtaining accurate distribution information is a challenging task. To resolve this issue, we investigate the problem of designing a control policy that is robust against errors…
Hany Abdulsamad, Tim Dorau, Boris Belousov, Jia-Jie Zhu + 1 more
—Trajectory optimization and model predictive control are essential techniques underpinning advanced robotic applications, ranging from autonomous driving to full-body humanoid control. State-of-the-art algorithms have focused on data-driven approaches that infer the system dynamics online and incorporate posterior…
Rui Liu, Guangyao Shi, Pratap Tokekar
— Distributionally Robust Optimal Control (DROC) is a technique that enables robust control in a stochastic setting when the true distribution is not known. Traditional DROC approaches require given ambiguity sets or a KL divergence bound to represent the distributional uncertainty. These may not be known a priori and…
Francesco Micheli, Anastasios Tsiamis, John Lygeros
— We present a novel approach for the control of uncertain, linear time-invariant systems, which are perturbed by potentially unbounded, additive disturbances. We propose a doubly robust data-driven state-feedback controller to ensure reliable performance against both model mismatch and disturbance distribution…
Guangyi Liu, Arash Amini, Vivek Pandey, Nader Motee
— We introduce a novel data-driven method to mitigate the risk of cascading failures in delayed discrete-time Linear Time-Invariant (LTI) systems. Our approach involves formulating a distributionally robust finite-horizon optimal control problem, where the objective is to minimize a given performance function while…
Hameed Ali, Oumaima Saidani, Marouan Kouki, Bilal Himmat + 1 more
Reliability surveillance of safety-critical systems often involves monitoring positively skewed characteristics, such as failure rates, repair times, or material degradation paths, which are robustly modeled by the lognormal distribution. We propose a Distributionally-Robust Bayesian Adaptive EWMA (DR-BAEWMA) framework…
Jana Dienstbier, Frauke Liers, Jan Rolfes
Single-level reformulations of (nonconvex) distributionally robust optimization (DRO) problems are often intractable, as they contain semi-infinite dual constraints. Based on such a semi-infinite reformulation, we present a safe approximation that allows for the computation of feasible solutions for DROs that depend on…
Jianhui Liu, Bin Zhang, Xiaosong Hu
Consensus tracking problems for multiple mechanical systems are considered in this paper, where information communications are limited between individuals and the desired trajectory is available to only a subset of the mechanical systems. A distributed tracking algorithm based on computed torque approach is proposed in…
Guangyu Wu, Panagiotis Tsiotras, Anders Lindquist
—Ensemble systems appear frequently in many engineering applications and, as a result, they have become an important research topic in control theory. These systems are best characterized by the evolution of their underlying state distribution. Despite the work to date, few results exist dealing with the problem of…
Armin M. Zand, Ankit Gupta, Mustafa Khammash
Precise intracellular regulation and robust perfect adaptation can be achieved using biomolecular integral controllers and it holds enormous potential for synthetic biology applications. In this letter, we consider the cascaded implementation of a class of such integrator motifs. Our cascaded integrators underpin…
Jun Zhao, Qingliang Zeng, Bin Guo
Model uncertainties are usually unavoidable in the control systems, which are caused by imperfect system modeling, disturbances, and nonsmooth dynamics. This paper presents a novel method to address the robust control problem for uncertain systems. The original robust control problem of the uncertain system is first…
Ruobing Li, Quanmin Zhu, Jun Yang, Pritesh Narayan + 3 more
'Ravi P. Agarwal' 'Maria Alessandra Ragusa'] U-model, which is a control-oriented model set with the property of generally facilitate nonlinearity dynamic inversion/cancellation, has been introduced to the Disturbance Observer-Based control (DOBC) methods to improve the performance of the nonlinear systems in this…
Pan Zhao, Ziyao Guo, Naira Hovakimyan, Baochang Zhang
This paper presents a tracking controller for nonlinear systems with matched uncertainties based on contraction metrics and disturbance estimation that provides exponential convergence guarantees. Within the proposed approach, a disturbance estimator is proposed to estimate the pointwise value of the uncertainties…
Corentin Briat, Ankit Gupta, Mustafa Khammash
Homeostasis is a running theme in biology. Often achieved through feedback regulation strategies, homeostasis allows living cells to control their internal environment as a means for surviving changing and unfavourable environments. While many endogenous homeostatic motifs have been studied in living cells, some other…
S. Puga-Guzmán, J. Moreno-Valenzuela, V. Santibáñez
A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors…
Corentin Briat, Ankit Gupta, Mustafa Khammash
The antithetic integral feedback motif recently introduced in [6] is known to ensure robust perfect adaptation for the mean dynamics of a given molecular species involved in a complex stochastic biomolecular reaction network. However, it was observed that it also leads to a higher variance in the controlled network…
Maurice Filo, Mustafa Khammash
Proportional-Integral-Derivative (PID) feedback controllers have been the most widely used controllers in the industry for almost a century. This is mainly due to their simplicity and intuitive operation. Recently, motivated by their success in various engineering disciplines, PID controllers found their way into…
Steven A. Frank
As systems become more robust against perturbations, they can compensate for greater sloppiness in the performance of their components. That robust compensation reduces the force of natural selection on the system’s components, leading to component decay. The paradoxical coupling of robustness and decay predicts that…
Corentin Briat, Mustafa Khammash
Controlling stochastic reactions networks is a challenging problem with important implications in various fields such as systems and synthetic biology. Various regulation motifs have been discovered or posited over the recent years, the most recent one being the so-called Antithetic Integral Control (AIC) motif [1].…
Authors not listed
This paper presents an empirical comparison of process control algorithms, with particular emphasis on classical Proportional–Integral–Derivative (PID) control, Model Predictive Control (MPC), and neural network-based methods in the context of complex industrial plants. Since industrial sectors frequently demand…
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This paper formally defines an operational isomorphism between spectral damping in molecular vibronic systems and neuromodulatory control in biological sensory systems. Without asserting causal continuity or physical identity across scales, we show that both domains instantiate the same class of output-selective…
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We develop a control-oriented model for manipulating calcium carbonate (CaCO3) precipitation through pH and/or dissolved CO2(aq) adjustments in a semi-batch process. We present two open-loop control strategies: first, a closed-form optimal control solution derived via Pontryagin’s Minimum Principle; and second, an…