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Genetic circuits control every aspect of life and thus the ability to engineer them de-novo opens exciting possibilities, from revolutionary drugs and green energy to bugs that recognize and kill cancer cells. Just like in mechanical, electrical, and hydraulic systems, the problem of loading, or back-action, is encountered when engineering genetic circuits. These molecular loads can be severe to the point of completely destroying the intended function of a circuit. In this talk, I will review a systems theoretic modeling formalism, grounded on the concept of retroactivity, that captures molecular loads in a way that makes the loading problem amenable of a solution. I will, in particular, focus on two types of loading: inter-module loads and loads to cellular resources that feed the modules. I will show experimentally validated models of loading effects on the emergent dynamics of a system and nonlinear control techniques that we have developed and implemented to mitigate these effects.
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Logistics and transportation systems are man-made systems that are well suited for modeling in a discrete event system framework and particularly by Petri Nets (PNs), due to their different characteristics: distributed, parallel, deterministic, stochastic, discrete, and continuous. The paper presents a survey on the various Petri nets modeling frameworks proposed in the related literature for logistics and transportation systems, with applications to modeling, simulation, analysis, optimization and control. In particular, we focus on papers dealing with freight transportation and outline and classify the related works conducted using PNs as regards the proposed framework and addressed problems. We also debate the approach's viability, discussing contributions and limitations, and identify future research potentials.
Urban Air Mobility (UAM) is an emerging aviation sector and is playing an integral part in the on-demand mobility revolution. UAM is powered by the convergence of advances in distributed electrical propulsion (DEP) and vehicle autonomy. The complexity of operations in the urban environment and the unconventional vehicle configurations designed to take advantage of new propulsion technologies, result in numerous challenges that benefit from a control-centric approach. In this talk, we outline some of these challenges and present our current approach to addressing them. For example, in order to achieve full market potential and access to UAM, vehicle autonomous flight is required. A key barrier to autonomous flight in a large multi-agent system is dealing with off-nominal situations and contingencies in a safe and predictable manner. We present our approach to intelligent contingency management and share recent results and open problems. Additionally, we discuss another major barrier to ubiquitous UAM – the noise signature produced by vehicles with multiple rotors. We present our approach to minimizing such noise within the framework of the acoustically-aware vehicle.
In this talk, we will discuss how optimization and control theory play a fundamental, and often overlooked, role in multi-UAV coordination. We will see how the solutions of optimal control problems are essential in combinatorial assignment algorithms. Using intuition gained by solving these problems, one can intuit how results dealing with static task assignments extend to cases where the tasks are dynamic in nature. The concepts discussed in this talk will be highlighted with specific problems that are relevant to defense applications.
Genetic circuits control every aspect of life and thus the ability to engineer them de-novo opens exciting possibilities, from revolutionary drugs and green energy to bugs that recognize and kill cancer cells. The robustness of natural gene networks is the result of a million years of evolution and is in contrast with the fragility of today’s engineered circuits. A genetic module’s input/output behavior changes in unpredictable ways upon inclusion into a larger system. Therefore, each component of a system is usually redesigned every time a new piece is added. Rather than relying on such ad-hoc design procedures, control theoretic approaches may be used to engineer “insulation” of circuit components from context, thus enabling modular composition through specified input/output connections. In this talk, I will give an overview of modularity failures in genetic circuits, focusing on problems of loads, and introduce a control-theoretic framework, founded on the concept of retroactivity, to address the insulation question. Within this framework, insulation can be mathematically formulated as a disturbance rejection problem; however, classical solutions are not directly applicable due to biophysical constraints. I will thus introduce solutions relying on time-scale separation, a key feature of biomolecular systems, which were used to build two devices: the load driver and the resource decoupler. These devices aid modularity, facilitate predictable composition of genetic circuits and show that control-theoretic approaches may be suitable to address pressing challenges in engineering biology.
Recent results in deep learning have left no doubt that it is amongst the most powerful modeling tools that we possess. The real question is how can we utilize deep learning for control without losing stability and performance guarantees. Even though recent successes in deep reinforcement learning (DRL) have shown that deep learning can be a powerful value function approximator, several key questions must be answered before deep learning enables a new frontier in robotics. DRL methods have proven difficult to apply to real-world robotic systems where stability matters and safety is critical. In this talk, I will present our recent work in bringing deep learning-based methods to provably stable adaptive control and expand upon possibilities of using concepts from adaptive control to create safe and stable reinforcement learning algorithms. I will put our theoretical work in context by discussing several applications in flight control and agricultural robotics. I will also bring to light our recent work in understanding how the octopus brain works and how it can inspire future learning and distributed control tools.
Reachability analysis, which considers computing or approximating the set of future states attainable by a dynamical system over a time horizon, is receiving increased attention motivated by new challenges in, e.g., learning-enabled systems, assured and safe autonomy, and formal methods in control systems. Such challenges require new approaches that scale well with system size, accommodate uncertainties, and can be computed efficiently for in-the-loop or frequent computation. In this talk, we present and demonstrate a suite of tools for efficiently over-approximating reachable sets of nonlinear systems based on the theory of mixed monotone dynamical systems. A system is mixed monotone if its vector field or update map is decomposable into an increasing component and a decreasing component. This decomposition allows for constructing an embedding system with twice the states such that a single trajectory of the embedding system provides hyperrectangular over-approximations of reachable sets for the original dynamics. This efficiency can be harnessed, for example, to compute finite abstractions for tractable formal control verification and synthesis or to embed reachable set computations in the control loop for runtime safety assurance. We demonstrate these ideas on several examples, including an application to safe quadrotor flight that combines runtime reachable set computations with control barrier functions implemented on embedded hardware.
The fields of adaptive control and machine learning have evolved in parallel over the past few decades, with a significant overlap in goals, problem statements, and tools. Machine learning as a field has focused on computer-based systems that improve and learn through experience. Oftentimes the process of learning is encapsulated in the form of a parameterized model such as a neural network, whose weights are trained in order to approximate a function. The field of adaptive control, on the other hand, has focused on the process of controlling engineering systems in order to accomplish regulation and tracking of critical variables of interest. Learning is embedded in this process via online estimation of the underlying parameters. In comparison to machine learning, adaptive control often focuses on limited-data problems where fast, on-line performance is critical. Whether in machine learning or adaptive control, this learning occurs through the use of input-output data. In both cases, the approach used for updating the parameters is often based on gradient descent-like and other iterative algorithms. Related tools of analysis, convergence, and robustness in both fields have a tremendous amount of similarity. As the scope of problems in both topics increases, the associated complexity and challenges increase as well. In order to address learning and decision-making in real time, it is essential to understand these similarities and connections to develop new methods, tools, and algorithms.
This talk will examine the similarities and interconnections between adaptive control and optimization methods commonly employed in machine learning. Concepts in stability, performance, and learning, common to both fields will be discussed. Building on the similarities in update laws and common concepts, new intersections and opportunities for improved algorithm analysis will be explored. High-order tuners and time-varying learning rates have been employed in adaptive control leading to very interesting results in dynamic systems with delays. We will explore how these methods can be leveraged to lead to provably correct methods for learning in real-time with guaranteed fast convergence. Examples will be drawn from a range of engineering applications.
Hiring and Supporting a Diverse Faculty (Dr. Bonnie Ferri)
This talk will explore some of the issues, challenges, and opportunities for hiring and supporting a diverse faculty in STEM disciplines. What are some policies, practices, and programs that support a healthy and productive culture among a diverse population? Do our promotion and advancement practices need retuning? What contributions can a professional society have to support success? Finally, what can each of us do individually to support diversity, equity, and inclusion in the faculty ranks?
