Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms

Temperature and concentration control of exothermic chemical processes in continuous stirred tank reactors

Exothermic chemical reaction taking place in continuous stirred tank reactor is considered. Heat release from the chemical reaction, non-linear dynamic behavior of the process and uncertainty in parameters are the main factors motivating the use of robust control design. Viewing temperature and molar concentration as variables both accessible in real time, PI and optimal state-feedback controllers driven by temperature and concentration error signals are proposed to regulate the system over reactor’s steady-state working points by counteracting undesired disturbances. Since access to concentration value has proved beneficial for the reactor’s performance, estimation techniques are examined to compensate for the problematic nature of the concentration’s measurement. A linear reduced-order observer is first proposed to estimate the concentration value using temperature measurements. In addition, assuming concentration measurement is available with a relatively short delay via sample analysis, a linear and non-linear discrete-time predictor is constructed to estimate the concentration’s real-time value. A linear combination of the two estimation schemes (observer, predictor) is proposed resulting in a combined estimator, in which the emphasis between the two individual schemes can be controlled via a scalar parameter. The work presented in this paper was supported by the GLOW project – New weather-stable low gloss powder coatings based on bifunctional acrylic solid resins and nanoadditives – as part of the development of novel and efficient processing technologies regarding the production of new families of powder coatings, responding to industrial requirements for quality improvement at lower cost and shorter development cycles

Measurements, Models, Systems and Design

531 s.

Outlier-Resistant Remote State Estimation for Recurrent Neural Networks with Mixed Time-Delays

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61933007, 61873148 and 61873058); 10.13039/501100005046-Natural Science Foundation of Heilongjiang Province of China (Grant Number: ZD2019F001); Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment of Ministry of Education in Anhui Polytechnic University of China (Grant Number: GDSC202016); 10.13039/100005156-Alexander von Humboldt Foundation of Germany

New control structure for high voltage fields

In electrostatic painting, a high voltage is applied to the paint, attracting the paint particles toward a grounded work object, drastically increasing the efficiency of the painting process. However, combining high voltage and highly flammable paint is a potential hazard that is reduced by an automatic fire extinguishing system and strict controller limits defined by safety standards. The thesis investigates alternative controller strategies attempting to improve the performance of ABB's high-voltage control system. The dynamics of electrostatic gas discharge, or corona discharge, is studied to estimate the relation between the applied voltage and the corona current passing through the electrostatic field. However, given the data available for the real-time system, the estimation problem is concluded to be structurally unidentifiable, resulting in the estimators not converging to the actual state of the system. Despite that, a simple estimator is utilized in a current limiting controller. This controller is activated when the system leaves its normal working area. Simulation results indicate that this controller can decrease the amount of unnecessary safety-related stops and reduce the reaction time for actual safety-hazard incidents. Furthermore, a data-driven approach is selected to model and create a controller for the system generating the high-voltage output. The model of the dynamics of the high-voltage system is created using neural networks and open-loop high-resolution data collected with a self-developed data acquisition program. Then, the estimated model is used in a reinforcement learning environment to create a theoretically optimal controller valid for the entire nonlinear workspace. Due to limited computational resources, and errors in the data, the thesis presents a lower-resolution proof of concept for both the neural network model and controller. Additionally, the thesis presents a basis of knowledge on ABB's electrostatic painting system, featuring recommendations and suggestions for future work.In electrostatic painting, a high voltage is applied to the paint, attracting the paint particles toward a grounded work object, drastically increasing the efficiency of the painting process. However, combining high voltage and highly flammable paint is a potential hazard that is reduced by an automatic fire extinguishing system and strict controller limits defined by safety standards. The thesis investigates alternative controller strategies attempting to improve the performance of ABB's high-voltage control system. The dynamics of electrostatic gas discharge, or corona discharge, is studied to estimate the relation between the applied voltage and the corona current passing through the electrostatic field. However, given the data available for the real-time system, the estimation problem is concluded to be structurally unidentifiable, resulting in the estimators not converging to the actual state of the system. Despite that, a simple estimator is utilized in a current limiting controller. This controller is activated when the system leaves its normal working area. Simulation results indicate that this controller can decrease the amount of unnecessary safety-related stops and reduce the reaction time for actual safety-hazard incidents. Furthermore, a data-driven approach is selected to model and create a controller for the system generating the high-voltage output. The model of the dynamics of the high-voltage system is created using neural networks and open-loop high-resolution data collected with a self-developed data acquisition program. Then, the estimated model is used in a reinforcement learning environment to create a theoretically optimal controller valid for the entire nonlinear workspace. Due to limited computational resources, and errors in the data, the thesis presents a lower-resolution proof of concept for both the neural network model and controller. Additionally, the thesis presents a basis of knowledge on ABB's electrostatic painting system, featuring recommendations and suggestions for future work

Robust Controller for Delays and Packet Dropout Avoidance in Solar-Power Wireless Network

Solar Wireless Networked Control Systems (SWNCS) are a style of distributed control systems where sensors, actuators, and controllers are interconnected via a wireless communication network. This system setup has the benefit of low cost, flexibility, low weight, no wiring and simplicity of system diagnoses and maintenance. However, it also unavoidably calls some wireless network time delays and packet dropout into the design procedure. Solar lighting system offers a clean environment, therefore able to continue for a long period. SWNCS also offers multi Service infrastructure solution for both developed and undeveloped countries. The system provides wireless controller lighting, wireless communications network (WI-FI/WIMAX), CCTV surveillance, and wireless sensor for weather measurement which are all powered by solar energy

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

Advanced Three-Phase Grid Synchronization Using Synchronous Reference Frame Phase-Locked Loops

Modern power electronics devices require grid synchronization to accurately time the switching of their semiconductor devices. This project steps through the development of such an algorithm for three-phase grids. The classical synchronous reference frame phase-locked loop is studied in depth, including a detailed analysis of the transforms that give rise to its name. A few improvements are added in order to mitigate some of the well-known pitfalls of this method. Using the theory of symmetrical sequence components, new equations that describe the behaviour of said components in relation to unbalanced three-phase voltages are derived. These equations are then used to better understand the behaviour of the classical algorithm under unbalanced conditions. From this, an advanced grid synchronization algorithm based on multiple phase-locked loops is developed. This algorithm is then discretized and implemented in a typical microcontroller. Finally, a custom genetic algorithm is used to fine-tune the parameters of the algorithm to a specific simulated scenario meant to represent harsh grid conditions

Bayesian System ID: Optimal management of parameter, model, and measurement uncertainty

We evaluate the robustness of a probabilistic formulation of system identification (ID) to sparse, noisy, and indirect data. Specifically, we compare estimators of future system behavior derived from the Bayesian posterior of a learning problem to several commonly used least squares-based optimization objectives used in system ID. Our comparisons indicate that the log posterior has improved geometric properties compared with the objective function surfaces of traditional methods that include differentially constrained least squares and least squares reconstructions of discrete time steppers like dynamic mode decomposition (DMD). These properties allow it to be both more sensitive to new data and less affected by multiple minima --- overall yielding a more robust approach. Our theoretical results indicate that least squares and regularized least squares methods like dynamic mode decomposition and sparse identification of nonlinear dynamics (SINDy) can be derived from the probabilistic formulation by assuming noiseless measurements. We also analyze the computational complexity of a Gaussian filter-based approximate marginal Markov Chain Monte Carlo scheme that we use to obtain the Bayesian posterior for both linear and nonlinear problems. We then empirically demonstrate that obtaining the marginal posterior of the parameter dynamics and making predictions by extracting optimal estimators (e.g., mean, median, mode) yields orders of magnitude improvement over the aforementioned approaches. We attribute this performance to the fact that the Bayesian approach captures parameter, model, and measurement uncertainties, whereas the other methods typically neglect at least one type of uncertainty