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In this seminar, Dr. L’Afflitto will present two recent advances in the state-of-the-art in model reference control systems design. The first of these results will concern the design of an adaptive control system that allows the user to impose both the rate of convergence on the closed-loop system during its transient stage and constraints on both the trajectory tracking error and the control input at all times, despite parametric and modeling uncertainties. Successively, our speaker will present the first extension of the model reference adaptive control architecture to switched dynamical systems within the Carathéodory and the Filippov framework. The applicability of these theoretical formulations will be shown by the results of numerical simulations and flight tests involving multi-rotor unmanned aerial systems such as tilt-rotor quadcopters and tailsitter UAVs.
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.
Time-critical applications are often performed over a time interval [0, τ), where the utilized finite-time control algorithms are expected to assure a task completion at a user-defined convergence time τ. In this talk, we will explore how to address these applications using the time transformation approach, which allows us to transform a resulting algorithm over the prescribed time interval [0, τ) to an equivalent algorithm over the stretched infinite-time interval [0,∞) for stability analysis. In addition, a procedure for designing such finite-time control algorithms is presented. We further demonstrate the approach’s efficacy with numerical examples and experimental results involving networked multiagent systems.
There are two main approaches to control gain synthesis an internal model-based distributed dynamic state feedback control law for the linear cooperative output regulation problem: (i) agent-wise local design methods, (ii) global design methods. Agent-wise local design methods to synthesize distributed control gains focus on the individual dynamics of each agent to guarantee the overall stability of the system. They are powerful tools due to their scalability. However, the agent-wise local design methods are incapable of maximizing the overall system performance through, for example, decay rate assignment. On the other hand, design methods, which are predicated on a global condition, lead to nonconvex optimization problems. We present a convex formulation of this global design problem based on a structured Lyapunov inequality. Then, the existence of solutions to the structured Lyapunov inequality is investigated. Specifically, we analytically show that the solutions exist for the systems satisfying the agent-wise local sufficient condition. Finally, we compare the proposed method with the agent-wise local design method through numerical examples in terms of conservatism, performance maximization, graph dependency, and scalability.
Systems with dynamics evolving in distinct slow and fast timescales include aircraft (Khalil & Chen, 1990), robotic manipulators, (Tavasoli, Eghtesad, & Jafarian, 2009), electrical power systems (Sauer, 2011), chemical reactions (Mélykúti, Hespanha, & Khammash, 2014), production planning in manufacturing (Soner, 1993), and so on. The Geometric Singular Perturbation theory (Fenichel, 1979) is a powerful control law development tool for multiple-timescale systems because it provides physical insight into the evolution of the states in more than one timescale. The behaviour of the full-order system can be approximated by the slow subsystem, provided that the fast states can be stabilised on an equilibrium manifold. The fast subsystem describes how the fast states evolve from their initial conditions to their equilibrium trajectory or the manifold. This presentation develops two nonlinear, multiple-time-scale, output feedback tracking controllers for a class of nonlinear, nonstandard systems with slow and fast states, slow and fast actuators, and model uncertainties. The class of systems is motivated by aircraft with uncertain inertias, control derivatives, engine time-constant, and without direct measurement of angle-of-attack and sideslip angle. One controller achieves the control objective of slow state tracking, while the other does simultaneous slow and fast state tracking. Each controller is synthesized using time-scale separation, lower-order reduced subsystems, and estimates of unknown parameters and unmeasured states. The estimates are updated dynamically, using an online parameter estimator and a nonlinear observer. The update laws are so chosen that errors remain ultimately bounded for the full-order system. The controllers are simulated on a six-degree-of-freedom, high-performance aircraft model commanded to perform a demanding, combined longitudinal and lateral/directional maneuver. Even though two important aerodynamic angles are not measured, tracking is adequate and as good as a previously developed full-state feedback controller handling similar parametric uncertainties. Additionally, even though the two controllers in theory achieve two different control objectives, it is possible to choose either one of them for the same maneuver. Of the two new output feedback controllers, the slow state tracker accomplishes the maneuver with less control effort, while the simultaneous slow and fast state tracker does so with a smaller number of gains to tune.
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.
Swarm robotics, a subfield of both robotics and artificial swarm intelligence, focuses on the development of teams composed of large numbers of autonomous robotic agents. Like swarm intelligence, swarm robotics arises from the study of the phenomenology of biological systems in which large numbers of individuals collaborate in joint collective actions for the benefit of the community as a whole. However, whereas swarm intelligence often utilizes the means and mechanisms of bio-inspired swarms for numerical optimization, the goals of bio-inspired robot swarms are generally concerned with the use of large numbers of low-cost physically embodied agents, acting together in a real-world environment, to achieve a common purpose. This talk will discuss key methods and bio-inspired algorithms for use in programming and controlling robotic swarms, and potential applications of these swarms.
We will illustrate the essential intuition behind the so-called "Model Recovery Anti-windup" scheme for handling input saturation in control systems design. The talk will mostly focus on the qualitative aspects of the core feature of the scheme: storage and recovery of the unconstrained response that would have occurred without saturation. This goal and the ensuing (model recovery) anti-windup solutions will be discussed and clarified by way of a number of simulated and experimental application studies, ranging from vibration isolation, open water channels, flight control systems, robotic arms, and brake-by-wire systems for motorcycles.
Model reference adaptive control is a powerful tool that has a capability to suppress the effect of system uncertainties for achieving a desired level of closed-loop system performance. Yet, for a wide array of applications including unmodeled dynamics such as coupled rigid body systems with flexible interconnection links, airplanes with high aspect ratio wings, and high speed vehicles with strong rigid body and flexible dynamics coupling, the closed-loop system stability with model reference adaptive control laws can be challenged. In this seminar, we will focus on the stability interplay between a class of unmodeled dynamics and system uncertainties for model reference adaptive control laws, and proposed a robustifying term to relax the resulting interplay. The presented system-theoretical findings will be also supported by experimental results in order to bridge the theory-practice gap, where we use a benchmark mechanical system setup involving an inverted pendulum on a cart coupled with another cart through a spring in the presence of unknown frictions.
Reachability analysis is the problem of evaluating the set of all states that can be reached by a system starting from a given set of initial states. Since the reachable set can rarely be computed exactly, a standard approach is to over-approximate this set as tightly as possible. Various set representations and methods have been proposed for finding over-approximations; however, they are computationally expensive and do not scale well to high dimensional systems. This is a particularly important shortcoming for “symbolic control,” where the designer must first generate a finite state transition system from a continuous state model with repeated reachability computations. In this talk we present a suite of methods that offer computational efficiency using a simpler set representation in the form of multi-dimensional intervals. These methods leverage nonlinear systems concepts, such as monotonicity and its variants, sensitivity of trajectories to initial conditions and parameters, and contraction properties. We further introduce data-driven approaches for problems where probabilistic guarantees are appropriate. As we demonstrate with examples interval representation and the associated methods are particularly well suited to symbolic control, but of independent interest as well.
Urban mobility in Transportation is witnessing a transformation due to the emergence of new concepts in Mobility on Demand, where new modes of transportation other than private individual cars and public mass transit are being investigated. With a projection of a total number of 2 billion vehicles on roads by the year 2050, such innovations in transportation are urgently needed. One such paradigm is the notion of shared mobility on demand, which consists of customized dynamic routing for multi-passenger transport. A solution to this problem consists of a host of challenges that ranges from distributed optimization, behavioral modeling of passengers, traffic flow modeling, and distributed control. Recent efforts in our group have made some inroads into this problem and form the focus of this talk. A socio-technical model that combines behavioral models of passengers based on Cumulative Prospect Theory and traffic models will be discussed. The solution to dynamic routing is presented in the form of an optimization problem solved via an Alternating Minimization based approach. The model together with the optimization framework is then used to propose a dynamic tariff that can be viewed as a model-based control strategy based on Transactive Control, a methodology that is being explored in power grids for incentivizing flexible consumption.
The well-functioning of our modern society rests on the reliable and uninterrupted operation of large scale complex infrastructures, which are more and more exhibiting a network structure with a high number of interacting components/agents. Energy and transportation systems, communication and social networks are a few, yet prominent, examples of such large scale multi-agent networked systems. Depending on the specific case, agents may act cooperatively to optimize the overall system performance or compete for shared resources. Based on the underlying communication architecture, and the presence or not of a central regulation authority, either decentralized or distributed decision making paradigms are adopted. In this seminar, we address the interacting and distributed nature of cooperative multi-agent systems arising in the energy application domain. More specifically, we present our recent results on the development of a unifying distributed optimization framework to cope with the main complexity features that are prominent in such systems, i.e.: heterogeneity, as we allow the agents to have different objectives and physical/technological constraints; privacy, as we do not require agents to disclose their local information; uncertainty, as we take into account uncertainty affecting the agents locally and/or globally; and combinatorial complexity, as we address the case of discrete decision variables. (This is a joint work with Alessandro Falsone, Simone Garatti, and Kostas Margellos.)
Automated and connected road vehicles enable large-scale control and optimization of the transport system with the potential to radically improve energy efficiency, decrease the environmental footprint, and enhance safety. Freight transportation accounts for a significant amount of all energy consumption and greenhouse gas emissions. In this talk, we will discuss the potential future of road goods transportation and how it can be made more robust and efficient, from the automation of individual long-haulage trucks to the optimization of fleet management and logistics. Such an integrated cyber-physical transportation system benefits from having trucks traveling together in vehicle platoons. From the reduced air drag, platooning trucks traveling close together can save more than 10% of their fuel consumption. In addition, by automating the driving, it is possible to change driver regulations and thereby increase the efficiency even more. Control and optimization problems on various level of this transportation system will be presented. It will be argued that a system architecture utilizing vehicle-to- vehicle and vehicle-to-infrastructure communication enable robust and safe control of individual trucks as well as optimized vehicle fleet collaborations and new market opportunities. Furthermore, feedback control of individual platoons utilizing the cellular communication infrastructure can be used to improve the overall traffic conditions by reducing the variation of traffic density. Extensive experiments done on European highways will illustrate system performance and safety requirements. The presentation will be based on joint work over the last ten years with collaborators at KTH and at the truck manufacturers Scania and Volvo.
Autonomous systems use closed-loop feedback of sensed or communicated information to meet desired objectives. Meeting such objectives is more challenging when autonomous systems are tasked with operating in uncertain complex environments with intermittent feedback. This presentation explores different analysis methods that quantify the effects of intermittent feedback with respect to stability and performance of the autonomous agent. Various scenarios are considered where the intermittency results from natural phenomena or adversarial actors, including purposeful intermittency to enable new capabilities. Specific examples include intermittency due to occlusions in image-based feedback and intermittency resulting from various network control problems.
In cooperative multi-robot systems, there is a group of robots that seek to achieve a collective task as a team. Each individual robot makes decisions based on available local information as well as limited communications with neighboring robots. The challenge is to design local protocols that result in desired global outcomes. In contrast to a traditional centralized control paradigm, both measurements and decisions are distributed among multiple actors. This talk surveys various results for cooperative robotics based on methods drawn from game theory and distributed optimization, with applications to area coverage, cooperative pursuit, and self-assembly.
This seminar presents a survey of some of the main results in the theory of negative imaginary systems. The seminar also presents some applications of negative imaginary systems theory in the design of robust controllers. In particular, the seminar concentrates on the application of negative imaginary systems theory in the area of control of atomic force microscopes.
