This talk takes into account the current status of automation science and technology development as well as the existing automation undergraduate programs in many Chinese universities at the moment; while lending from the successes in automation science and technology developmental history combined with future demands for automation systems to aid in the economic development and national security of China with emerging technologies including mobile internet, cloud computing and big data driven artificial intelligence; taking manufacturing systems, important vehicles and cyber-physical and human system as research objects, this talk proposes that the future of automation systems be directed towards transforming into intelligent autonomous control system, intelligent optimal decision-making system and integrated system of intelligent optimal decision-making and control. With a research focus geared towards practical application, new algorithms of modeling, control and optimization for the prospective functions of the developing automation systems with subsequent design of methods and implementation techniques for new automation systems are taken as development directions of automation science and technology. Finally, this talk proposes that the future direction for development in the field of automation science and technology should be based on the current challenges and requirements that have emerged in new areas of application.

Hybrid systems, i.e., heterogeneous systems that include discrete and continuous-time subsystems, have been used to model control applications e.g. in automotive control, air traffic management systems, smart grids and intelligent manufacturing. Failure in this kind of applications can cause irreparable damage to the physical controlled systems and to the people who depend on it, or may cause large direct and indirect economic losses. Therefore, security for hybrid systems represent a significant concern.

In this respect, observability and diagnosability play an important role since they are essential in characterizing the possibility of identifying the system’s hybrid state, and in particular, the occurrence of specific states that may correspond to malfunctioning due to a fault or an adversarial attack.

In this talk, I review and place in context how the continuous and the discrete dynamics, as well as their interactions, intervene in the observability and diagnosability properties of a general class of hybrid systems. I also illustrate under which conditions the hybrid system’s state can be correctly estimated even when the system is under attack. An example related to network topology changes due to faults or attacks will illustrate the results.

In this talk by "using delays" I understand either Time-Delay Approaches to control problems (that originally may be free of delays) or intentional inserting delays to the feedback. I will start with an old Time-Delay approach - to sampled-data control. In application to network-based control with communication constraints, this is the only approach that allows treating transmission delays larger than the sampling intervals. I will continue with "using artificial delays" via simple Lyapunov functionals that lead to feasible LMIs for small delays and to simple sampled-data implementation.

Finally I will present a New Time-Delay approach - this time to Averaging. The existing results on averaging (that have been developed for about 60 years starting from the works of Bogoliubov and Mitropolsky) are qualitative: the original system is stable for small enough values of the parameter if the averaged system is stable. Our approach provides the first quantitative bounds on the small parameter making averaging-based control (including vibrational and extremum seeking control) reliable.

Many important plants (e.g. flexible manipulators or heat transfer processes) are governed by partial differential equations (PDEs) and are often described by models with a significant degree of uncertainty. Some PDEs may not be robust with respect to arbitrary small time-delays in the feedback. Robust finite-dimensional controller design for PDEs is a challenging problem.

In this talk two constructive methods for finite-dimensional control will be presented:

1. Spatial decomposition (or sampling in space) method, where the spatial domain is divided into N subdomains with N sensors and actuators located in each subdomain;
2. Modal decomposition method, where the controller is designed on the basis of a finite-dimensional system that captures the dominant dynamics of the infinite-dimensional one. Sufficient conditions ensuring the stability and performance of the closed-loop system are established in terms of simple linear matrix inequalities that are always feasible for appropriate choice of controllers.

We will discuss delayed and sampled-data implementations as well as application to network-based deployment of multi-agents.

One of the central societal challenges is to prolong independent living for elderly and promote well. Personalized robotic rehabilitation and assistance is considered one of the enabling technologies with control design playing a significant role. Focusing on sensorimotor rehabilitation and assistance, personalized control should be able to adapt to the high inter-personal variability in human motor behavior but also to intra-personal changes over time. Control adaptation is further challenged by the sparsity of person-specific data because calibration routines need to be brief for user acceptance. Above all, guaranteed safety is one of the key requirements.

In this talk we will present recent results on learning-based control with performance and safety guarantees for highly uncertain systems with particular focus on challenges arising from personalized rehabilitation and assistance. In order to achieve high sample efficiency as well as transparency of the system, available knowledge of dynamic models will be exploited and and augmented by Bayesian non-parametric model components. Epistemic uncertainty due to limited training data will explicitly be taken into account in the control design in order to achieve uncertainty-aware behavior of the closed loop system.

Online learning as well as realtime capabilities are further important aspects discussed in this talk. The results will be demonstrated in user intention-driven shared control designs for upper limb rehabilitation and assistance with exoskeletons.

Achieving a high level of autonomy of robots operating in extreme environments is particularly desirable but also particularly challenging due to uncertain and potentially varying operating conditions. By extreme environments we mean remote or hardly accessible environments where robots need to rely largely on local limited resources for their control implementation as for example underwater robots for collecting litter in marine environments. In this scenario the strong influence of nonlinear hydrodynamics on the motion of underwater robots and (often unpredictable) influences like currents as well as the distorted perception of the environment pose significant challenges for precise control and safe operation.

Recent progress in machine learning for control promises high performance in such uncertain conditions, yet many of the available approaches cannot directly be applied due to the limited available resources in terms of local computational power and communication. Hence apart from the challenge of providing safety and performance guarantees for learning control, also the efficient implementation plays an important role.

In this talk we will present results on learning-based control with performance guarantees for nonlinear systems in uncertain environment and under resource constraints on the example of an underwater robotic system with manipulation capabilities. We will introduce approaches to evaluate data-efficiency in non-parametric modeling techniques and show that the control task matters in this respect. The promises of physics-informed learning techniques to improve learning performance in terms of data efficiency and under noisy training conditions will be discussed.

Furthermore, different approaches to achieve real-time performance of non-parametric machine learning techniques given limited resources will be presented. While the proposed approaches promise to bring us a step further towards implementable high performance control for robots in extreme environments we will also discuss the remaining challenges as well as their limits.

Online learning in closed loop control systems is very attractive because it allows the automated identification of highly nonlinear dynamical systems as well as a fast adaptation to dynamically changing environments. Yet, depending on the application the data collection and the training of models is costly if not even prohibitive. On the one hand, the training is computationally expensive and might compromise real-time performance.

In particular in non-parametric learning approaches as e.g. in Gaussian Processes, the computational tractability is tied to the number of training data. As such it is important to understand how informative training samples are and further how to improve algorithmic efficiency of training and prediction.

In this talk we will demonstrate that the control task in addition to the underlying system dynamics has a strong influence on the required sample complexity. Employing Bayesian principles, we explore methods to quantify epistemic uncertainty with respect to control objectives and how they can be exploited to achieve a high sample efficiency for learning in the closed loop system. Additionally, approaches for efficient non-parametric online learning algorithms are proposed to allow the application of the presented methods under real-time constraints.

Stochastic approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. Reinforcement learning algorithms such as TD- and Q-learning are two of its most famous applications.

This talk provides an overview of stochastic approximation, with focus on optimizing the rate of convergence. Based on this general theory, the well known slow convergence of Q-learning is explained: the variance of the algorithm is typically infinite.   New algorithms with provably fast (even optimal) convergence rate have been developed in recent years:   stochastic Newton-Raphson,  Zap SNR,  and acceleration techniques inspired by Polyak and Nesterov will be discussed (as time permits, and depending on the interests of the audience).

This work concerns design of control systems for "Demand Dispatch" to obtain ancillary services to the power grid by harnessing inherent flexibility in many loads.  With careful design, the grid operator can harness this flexibility to regulate supply-demand balance.  The deviation in aggregate power consumption can be controlled just as generators provide ancillary service today.  

Distributed control techniques are called for, much like those used today to provide congestion control in communication networks.  The main message is that intelligence should be concentrated as much as possible at the load.  In this way it is possible to design local control loops so that the aggregate of loads appears as a passive input-output system, while strict QoS constraints are maintained for each load.

While autonomous agents that perform solo missions can yield significant benefits, greater efficiency and operational capability will be realized from teams of autonomous agents operating in a coordinated fashion. Potential applications for networked multiple autonomous agents include environmental monitoring, search and rescue, space-based interferometers and hazardous material handling. Networked multi-agent systems place high demands on features such as low cost, high adaptivity and scalability, increased flexibility, great robustness, and easy maintenance. To meet these demands, the current trend is to design distributed control algorithms that rely on only local interaction to achieve global group behavior.

The purpose of this talk is to overview our research in distributed control of multi-agent systems. Theoretical results on distributed leaderless consensus with agent dynamics including first- and second-order linear dynamics, rigid body attitude dynamics, and Euler-Lagrange dynamics, distributed single-leader collective tracking with reduced interaction and partial measurements, distributed multi-leader containment control with local interaction, distributed average tracking with multiple time-varying reference signals, and distributed optimization with non-identical constraints will be introduced. Application examples in multi-vehicle cooperative control will also be introduced.

The problem of distributed dynamic state estimation using networked local agents with sensing and communication abilities, has become a popular research area in recent years due to its wide range of applications such as target tracking, region monitoring and area surveillance. Specifically, we consider the scenario where the local agents take local measurements and communicate with only their nearby neighbors to estimate the state of interest in a cooperative and fully distributed manner. A distributed hybrid information fusion algorithm is proposed in the scenario where the process model of the target and the sensing models of the local agents are linear and time varying. The proposed distributed hybrid information fusion algorithm is shown to be fully distributed and hence scalable, to be run in an automated manner and hence adaptive to locally unknown changes in the network, to have agents communicate for only once during each sampling time interval and hence inexpensive in communication, and to be able to track the interested state with uniformly upper bounded estimate error covariance. It is also explored very mild conditions on general directed time-varying graphs and joint network observability/detectability to guarantee the stochastic stability of the proposed algorithm.

Integrated power systems (IPS) incorporate heterogeneous power sources, including energy storage systems, to achieve improved energy efficiency and reliability. They have been a critical enabling technology for vehicle electrification. One distinctive characteristic of IPS is the highly interactive and dynamic nature, due to tight physical couplings of the multiple components involved. To achieve high efficiency, one often exploits their operating profiles and pushes these systems to operate on or close to their admissible boundary, thereby calling for predictive control. In this lecture, we will explore the unique characteristics of the IPS and discuss the challenges and solutions of real-time optimization and predictive control applied to this particular class of systems. Several examples, including the IPS for all-electric ships and the integrated solid oxide fuel cell and gas turbine (SOFC/GT) system, will be used to provide motivations and illustrate the impact of solutions.

Thermal and power systems are tightly coupled and dynamically integrated. The different time scales in thermal and power responses make the integrated thermal and power management problems intriguing and challenging. For connected and automated vehicles (CAVs), the availability of predictive traffic information and the ability to coordinate multiple control subsystems allow us to explore the thermal-power interactions in new dimensions to enhance safety and improve fuel economy. It presents a perfect example where prediction, estimation, control, and optimization serve as the cornerstones for technology breakthroughs in the interconnected and dynamic environment. The talk will discuss the problems, explore the effective tools, and showcase some illustrative solutions.

Nonlinear dynamical systems cover an immensely wide range of real-life situations. However, it is often the case that a priori structure information of the unknown system is not available. Thus, nonparametric identification is necessary for data-driven identification of nonlinear systems. In the first part of this talk, we present a recursive local linear estimator for nonparametric identification of nonlinear autoregressive systems with exogenous inputs. The strong consistency and the asymptotical mean square error properties of the recursive local linear estimator are established, and its application to an additive nonlinear system is discussed. The recursive local linear estimator provides recursive estimates not only for the function values but also their gradients at fixed points. In the second part of this talk, we present a data-driven method for identification of high-dimensional additive nonlinear dynamical systems with little a priori information. In particular, we develop a two-step method for variable selection to determine contributing additive functions and to remove non-contributing ones from the underlying nonlinear system. At the first step, we estimate each additive function by kernel-based nonparametric identification approaches without suffering from the curse of dimensionality. At the second step, we utilize a nonnegative garrote estimator to identify which additive functions are nonzero by use of the obtained nonparametric estimates of each function. We show that the proposed variable selection method can find the correct variables with probability one under weak conditions.

Due to the openness of operation systems and communication interfaces, an increasing number of cyber attacks can easily sneak into Cyber-Physical Systems (CPSs) and cause very serious consequences. Denial-of-service (DoS) attack is one particularly common type of cyber attacks in CPSs. A great deal of efforts have been expended in investigating the effect of power-constrained DoS attacks on the performances of CPSs. Almost all of them assume that communication channel states are unaltered. However, more practically, the variation of communication channel states will impact the consequence of DoS attacks, and thus this factor should not be ignored when investigating the security issues of CPSs. In this talk, we consider the scenario that the sensor data are transmitted through a standard block fading communication channel. From the viewpoint of the DoS attacker, we construct optimization problems considering jointly the system performance indexes and the attack power consumption. Then we transform the original problem into a Markov decision problem and show the existence of optimal solution. As it is difficult to provide an analytical expression of optimal attack strategy, the objective function is approximated to derive an analytical expression of the suboptimal attack strategy.