Dynamic Systems & Controls

We consider the problem of optimal and constrained data-driven control for unknown systems. A novel data-enabled predictive control (DeePC) algorithm is presented that computes optimal and safe control policies driving the unknown system along a desired trajectory while satisfying system constraints. Using a finite number of data samples from the unknown system, our algorithm is grounded on insights from subspace identification and behavioral systems theory. In particular, we use raw unprocessed data assembled in a matrix time series for data-driven estimation and prediction.

Karl Åström once famously called automatic control the hidden technology In recognition of the fact that despite Its pervasiveness, It Is rarely mentioned. Control ls Indeed a critical component of so many technologies used in Industry and In our everyday life. In this talk I want to Illustrate the broad reach of control engineering through applications I performed over the last forty years.

Autonomous mobile robots are becoming pervasive in everyday life, and hybrid approaches that merge traditional control theory and modern data-driven methods are becoming increasingly important. In the first half of the seminar, we begin with a discussion of safety verification methods, and their computational and practical challenges. In the second half, we examine connections between optimal control and reinforcement learning, and between optimal control and visual navigation.

In control engineering practice, the users of system identification may have prior knowledge of control plants to be identified. In order to get better model estimates and further improve the modeling efficiency, the users have thus been suggested to make an intelligent choice of experiment design, model set, and identification criterion guided by prior knowledge as well as by observed data.

Recent research in safety control has leveraged the availability of accurate models to detect impending safety violations and to intervene accordingly. However, there is often a mismatch between the models that are used for algorithm design and the real systems. Moreover, control designs typically assume the availability of full state information that is error-free and trustworthy.

In this talk, we will present some of our recent results and ongoing work on safety-critical control synthesis under state and time (spatiotemporal) constraints and input constraints, with some applications in multi-robot systems. The proposed framework aims to eventually develop and integrate estimation, learning, and control methods towards provably-correct and computationally-efficient mission synthesis for multi-agent systems in the presence of spatiotemporal constraints and uncertainty.

The ability of machine learning techniques to process rich sensory inputs such as vision makes them highly appealing for use in robotic systems (e.g., micro aerial vehicles and robotic manipulators). However, the increasing adoption of learning-based components in the robotics perception and control pipeline poses an important challenge: how can we guarantee the safety and performance of such systems?

We consider the Bayesian inverse problem of inferring the initial condition of a linear dynamical system from noisy output measurements taken after the initial time. In practical applications, the large dimension of the dynamical system state poses a computational obstacle to computing the exact posterior distribution.

The introduction of machine learning (ML) and artificial intelligence (AI) creates unprecedented opportunities for achieving full autonomy. However, learning-based methods in building autonomous systems can be extremely brittle in practice and are not designed to be verifiable. In this talk, I will present several of our recent efforts that combine ML with formal methods and control theory to enable the design of provably dependable and safe autonomous systems.

In this talk, I will present our recent results on compositional verification and synthesis of interconnected control systems against high-level temporal logic requirements. I propose a divide and conquer strategy to scale our proposed techniques by leveraging the natural structure present in the system to break the verification and synthesis problem into semi-independent ones. I will leverage small-gain type reasoning and notions of barrier certificates as two key tools to tackle the verification and synthesis complexity.