Robust Preview Control for a Class of Uncertain Discrete-Time Lipschitz Nonlinear Systems

© 2018 Xiao Yu et al. This paper considers the design of the robust preview controller for a class of uncertain discrete-time Lipschitz nonlinear systems. According to the preview control theory, an augmented error system including the tracking error and the known future information on the reference signal is constructed. To avoid static error, a discrete integrator is introduced. Using the linear matrix inequality (LMI) approach, a state feedback controller is developed to guarantee that the closed-loop system of the augmented error system is asymptotically stable with H∞ performance. Based on this, the robust preview tracking controller of the original system is obtained. Finally, two numerical examples are included to show the effectiveness of the proposed controller

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity

Time-Delay Switch Attack on Networked Control Systems, Effects and Countermeasures

In recent years, the security of networked control systems (NCSs) has been an important challenge for many researchers. Although the security schemes for networked control systems have advanced in the past several years, there have been many acknowledged cyber attacks. As a result, this dissertation proposes the use of a novel time-delay switch (TDS) attack by introducing time delays into the dynamics of NCSs. Such an attack has devastating effects on NCSs if prevention techniques and countermeasures are not considered in the design of these systems. To overcome the stability issue caused by TDS attacks, this dissertation proposes a new detector to track TDS attacks in real time. This method relies on an estimator that will estimate and track time delays introduced by a hacker. Once a detector obtains the maximum tolerable time delay of a plant’s optimal controller (for which the plant remains secure and stable), it issues an alarm signal and directs the system to its alarm state. In the alarm state, the plant operates under the control of an emergency controller that can be local or networked to the plant and remains in this stable mode until the networked control system state is restored. In another effort, this dissertation evaluates different control methods to find out which one is more stable when under a TDS attack than others. Also, a novel, simple and effective controller is proposed to thwart TDS attacks on the sensing loop (SL). The modified controller controls the system under a TDS attack. Also, the time-delay estimator will track time delays introduced by a hacker using a modified model reference-based control with an indirect supervisor and a modified least mean square (LMS) minimization technique. Furthermore, here, the demonstration proves that the cryptographic solutions are ineffective in the recovery from TDS attacks. A cryptography-free TDS recovery (CF-TDSR) communication protocol enhancement is introduced to leverage the adaptive channel redundancy techniques, along with a novel state estimator to detect and assist in the recovery of the destabilizing effects of TDS attacks. The conclusion shows how the CF-TDSR ensures the control stability of linear time invariant systems

Active inference: building a new bridge between control theory and embodied cognitive science

The application of Bayesian techniques to the study and computational modelling of biological systems is one of the most remarkable advances in the natural and cognitive sciences over the last 50 years. More recently, it has been proposed that Bayesian frameworks are not only useful for building descriptive models of biological functions, but that living systems themselves can be seen as Bayesian (inference) machines. On this view, the statistical tools more traditionally used to account for data in biology, neuroscience and psychology, are now used to model the mechanisms underlying functions and properties of living systems as if the systems themselves were the ones“calculating”those probabilities following Bayesian inference schemes. The free energy principle (FEP) is a framework proposed in light of this paradigm shift, advocating the minimisation of variational free energy, a proxy for sensory surprisal, as a general computational principle for biological systems. More intuitively and under some simplifying assumptions,the minimisation of variational free energy reduces,for an agent,to the minimisation of prediction errors on sensory input. Initially proposed as a candidate unifying theory of brain functioning, the FEP was later extended to encompass hypotheses on the origins of life, and is nowadays discussed in the cognitive science community for its possible implications for theories of the mind. In particular,one of the most popular process theories derived from the FEP,active inference,describes a biologically plausible algorithmic implementation of this principle with several repercussions on our understanding of cognition. In this thesis, I will focus on the role of this process theory for action and perception. In active inference, the two of them are combined in a closed sensorimotor loopasco-dependent processes of minimisation of a single loss function,variational free energy, with respect to different sets of variables. Building on this, I will suggest that some of the core ideas of active inference are best seen in terms of enactive, embodied, extended and embedded (4E) theories, in contrast to the majority of the literature emphasising its apparent connections to more traditional, computational, accounts of the mind. In particular, I will develop this argument by focusing on some proposals central to 4E approaches: (a) the non-brain-centric nature of cognitive processes,(b)the lack of explicit representations of the world,(c)the coupling of agent-environment systems and (d) the necessity of real-time feedback signals from the environment. Under the FEP formulation, I will present a series of case studies with mainly two objectives in mind: 1) to conceptually analyse and reframe these 4E ideas in the context of active inference, arguing for the advantages of their formalisation in a more general probabilistic (Bayesian) framework and, 2) to present new mathematical models and agent-based implementations of some of the conceptual connections between Bayesian inference frameworks and 4E proposals, largely missing in the literature

AAS/GSFC 13th International Symposium on Space Flight Dynamics

This conference proceedings preprint includes papers and abstracts presented at the 13th International Symposium on Space Flight Dynamics. Cosponsored by American Astronautical Society and the Guidance, Navigation and Control Center of the Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to orbit-attitude prediction, determination, and control; attitude sensor calibration; attitude dynamics; and mission design