More Like this

1. A generalization of the achievable rate of a MISO system using Bode-Fano wideband matching theory

   https://doi.org/10.1109/TWC.2024.3400793  

   Deshpande, Nitish; Castellanos, Miguel R; Khosravirad, Saeed R; Du, Jinfeng; Viswanathan, Harish; Heath, Robert W (May 2024, IEEE Transactions on Wireless Communications)

   Impedance-matching networks affect power transfer from the radio frequency (RF) chains to the antennas. Their design impacts the signal to noise ratio (SNR) and the achievable rate. In this paper, we maximize the information-theoretic achievable rate of a multiple-input-single-output (MISO) system with wideband matching constraints. Using a multiport circuit theory approach with frequency-selective scattering parameters, we propose a general framework for optimizing the MISO achievable rate that incorporates Bode-Fano wideband matching theory. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We apply this framework to a single electric Chu’s antenna and an array of dipole antennas. We compare the optimized achievable rate obtained numerically with other benchmarks like the ideal achievable rate computed by disregarding matching constraints and the achievable rate obtained by using sub-optimal matching strategies like conjugate matching and frequency-flat transmission. We also propose a practical methodology to approximate the achievable rate bound by using the optimal transmission coefficient to derive a physically realizable matching network through the ADS software. 

   more » « less
2. Optogenetic stimulation probes with single-neuron resolution based on organic LEDs monolithically integrated on CMOS

   https://doi.org/10.1038/s41928-023-01013-y  

   Taal, Adriaan J.; Uguz, Ilke; Hillebrandt, Sabina; Moon, Chang-Ki; Andino-Pavlovsky, Victoria; Choi, Jaebin; Keum, Changmin; Deisseroth, Karl; Gather, Malte C.; Shepard, Kenneth L. (January 2023, Nature Electronics)

   Abstract The use of optogenetic stimulation to evoke neuronal activity in targeted neural populations—enabled by opsins with fast kinetics, high sensitivity and cell-type and subcellular specificity—is a powerful tool in neuroscience. However, to interface with the opsins, deep-brain light delivery systems are required that match the scale of the spatial and temporal control offered by the molecular actuators. Here we show that organic light-emitting diodes can be combined with complementary metal–oxide–semiconductor technology to create bright, actively multiplexed emissive elements. We create implantable shanks in which 1,024 individually addressable organic light-emitting diode pixels with a 24.5 µm pitch are integrated with active complementary metal–oxide–semiconductor drive and control circuitry. This integration is enabled by controlled electrode conditioning, monolithic deposition of the organic light-emitting diodes and optimized thin-film encapsulation. The resulting probes can be used to access brain regions as deep as 5 mm and selectively activate individual neurons with millisecond-level precision in mice. 

   more » « less
3. Backup Plan Constrained Model Predictive Control with Guaranteed Stability

   https://doi.org/10.2514/1.G007627  

   Tao, Ran; Kim, Hunmin; Yoon, Hyung-Jin; Wan, Wenbin; Hovakimyan, Naira; Sha, Lui; Voulgaris, Petros (October 2023, Journal of Guidance, Control, and Dynamics)

   Safe control designs for robotic systems remain challenging because of the difficulties of explicitly solving optimal control with nonlinear dynamics perturbed by stochastic noise. However, recent technological advances in computing devices enable online optimization or sampling-based methods to solve control problems. For example, Control Barrier Functions (CBFs) have been proposed to numerically solve convex optimization problems that ensure the control input to stay in the safe set. Model Predictive Path Integral (MPPI) control uses forward sampling of stochastic differential equations to solve optimal control problems online. Both control algorithms are widely used for nonlinear systems because they avoid calculating the derivatives of the nonlinear dynamic functions. In this paper, we use Stochastic Control Barrier Functions (SCBFs) constraints to limit sample regions in the samplingbased algorithm, ensuring safety in a probabilistic sense and improving sample efficiency with a stochastic differential equation. We also show that our algorithm needs fewer samples than the original MPPI algorithm does by providing a sampling complexity analysis. 

   more » « less
4. Adaptive MPPI Architecture for Robust and Agile Control of Multirotors

   https://doi.org/10.1109/IROS45743.2020.9341154  

   Pravitra, Jintasit; Ackerman, Kasey A.; Cao, Chengyu; Hovakimyan, Naira; Theodorou, Evangelos A. (October 2020, IEEE International Workshop on Intelligent Robots and Systems)

   null (Ed.)

   This paper presents a multirotor control architecture, where Model Predictive Path Integral Control (MPPI) and ℒ 1 adaptive control are combined to achieve both fast model predictive trajectory planning and robust trajectory tracking. MPPI provides a framework to solve nonlinear MPC with complex cost functions in real-time. However, it often lacks robustness, especially when the simulated dynamics are different from the true dynamics. We show that the ℒ 1 adaptive controller robustifies the architecture, allowing the overall system to behave similar to the nominal system simulated with MPPI. The architecture is validated in a simulated multirotor racing environment. 

   more » « less
5. Adaptive Risk Sensitive Path Integral for Model Predictive Control via Reinforcement Learning

   https://doi.org/10.1109/MED59994.2023.10185876  

   Yoon, Hyung-Jin; Tao, Chuyuan; Kim, Hunmin; Hovakimyan, Naira; Voulgaris, Petros (June 2023, editerranean Conference on Control and Automation (MED))

   We propose a reinforcement learning framework where an agent uses an internal nominal model for stochastic model predictive control (MPC) while compensating for a disturbance. Our work builds on the existing risk-aware optimal control with stochastic differential equations (SDEs) that aims to deal with such disturbance. However, the risk sensitivity and the noise strength of the nominal SDE in the riskaware optimal control are often heuristically chosen. In the proposed framework, the risk-taking policy determines the behavior of the MPC to be risk-seeking (exploration) or riskaverse (exploitation). Specifcally, we employ the risk-aware path integral control that can be implemented as a Monte-Carlo (MC) sampling with fast parallel simulations using a GPU. The MC sampling implementations of the MPC have been successful in robotic applications due to their real-time computation capability. The proposed framework that adapts the noise model and the risk sensitivity outperforms the standard model predictive path integ 

   more » « less