Search Results for author:
Found 8 papers, 0 papers with code
A Convex Optimization Approach for Control of Linear Quadratic Systems with Multiplicative Noise via System Level Synthesis
To deal with this issue, the presented optimization problem is next reformulated as a chance-constrained program (CCP) in which the guarantees are not intended in a deterministic sense of satisfaction against all possible closed-loop system responses, but are instead intended in a probabilistic sense of satisfaction against all but a small fraction of the system responses.
Performance Analysis of Event-Triggered Consensus Control for Multi-agent Systems under Cyber-Physical Attacks
This work presents a rigorous analysis of the adverse effects of cyber-physical attacks on the performance of multi-agent consensus with event-triggered control protocols.
A Risk-Averse Preview-based $Q$-Learning Algorithm: Application to Highway Driving of Autonomous Vehicles
A risk assessment unit module is then presented that leverages the preview information provided by sensors along with a stochastic reachability module to assign reward values to the MDP states and update them as scenarios develop.
Finite-time Koopman Identifier: A Unified Batch-online Learning Framework for Joint Learning of Koopman Structure and Parameters
In this paper, a unified batch-online learning approach is introduced to learn a linear representation of nonlinear system dynamics using the Koopman operator.
A Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning
This paper presents a model-free reinforcement learning (RL) algorithm to solve the risk-averse optimal control (RAOC) problem for discrete-time nonlinear systems.
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework
To guarantee performance while assuring satisfaction of safety constraints across variety of circumstances, an assured autonomous control framework is presented in this paper by empowering RL algorithms with metacognitive learning capabilities.
Data-driven Dynamic Multi-objective Optimal Control: An Aspiration-satisfying Reinforcement Learning Approach
A Sum-of-Square (SOS) based iterative algorithm is developed to solve the formulated aspiration-satisfying MO optimization.
Observer-based Adaptive Optimal Output Containment Control problem of Linear Heterogeneous Multi-agent Systems with Relative Output Measurements
That is, since the followers cannot directly sense their absolute states, a distributed observer is designed that uses only relative output measurements with respect to their neighbors (measured for example by using range sensors in robotic) and the information which is broadcasted by their neighbors to estimate their states.
