Unscented transform-based dual adaptive control of nonlinear MIMO systems

The paper proposes a multilayer perceptron neural network controller for dual adaptive control of a class of stochastic MIMO nonlinear systems subject to functional uncertainty. The neural network parameters are adjusted in real-time using the Unscented Kalman filter algorithm and no pre-operational training phase is required. Dual adaptive control aims to strike a compromise between the two control characteristics of caution and probing, leading to an improved overall performance. The system is evaluated through numerical simulations and Monte Carlo analysis. The resulting performance of the dual adaptive controller is not only consistently superior to non-dual adaptive control schemes, but also surpasses the performance of similar controllers that are based on Extended Kalman filter estimators. This reflects the enhanced accuracy of the Unscented Kalman filter estimator, despite being a local estimation method. In addition, unlike use of other estimators, the proposed approach neither requires the computation of complex Jacobian matrices as part of the design, nor the evaluation of such matrices in real-time. This renders the proposed controller inherently amenable and practical for real-time implementation.peer-reviewe

Offset-free model predictive control using Koopman-Wiener models

Abstract. This master’s thesis was built on the previously developed Koopman-Wiener nonlinear model predictive controller, and the goal of this thesis was to find a suitable strategy for rejecting steady-state offset, caused by plant model mismatch. This thesis also aimed to enable the controller to perform in applications where the full state is not measured and the available measurements are corrupted with noise. The work in this thesis considered multiple strategies for handling plant model mismatch, but disturbance rejection was selected as the main approach. It is proposed in this thesis that the disturbance model for disturbance rejection can be chosen by calculating empirical observability Gramian at a single initial point for every considered augmented model option and then picking the model which is interpreted as the most observable. The proposed observability analysis provides information about weak observability of the disturbance augmented model only at the single initial point. Nevertheless, it was argued in this thesis that the results can be assumed to represent the relevant operation region, and thus the method is applicable for choosing a disturbance model. As an alternative to compare against disturbance rejection, this thesis also investigated recursive least squares method that adapts the Koopman-Wiener model within the controller online. For state estimation, this thesis utilized unscented Kalman filter. This thesis demonstrated performance of the chosen methods with two nonlinear system case studies commonly studied in the literature: a simulated continuous stirred tank reactor and a simulated distillation column. This paper provides three main results. Firstly, the controller with disturbance rejection is successful in eliminating steady-state offset in a closed-loop system. Secondly, the controller is unable to reach satisfactory performance while using the recursive least squares method. Thirdly, the results from case studies support the chosen disturbance modeling approach, since the disturbance models chosen with the approach lead to improved or equal controller performance compared to using other disturbance models. Furthermore, the results support presenting a useful heuristic about how to perform disturbance modeling with Koopman-Wiener models by having the disturbances affect the slow dynamics of the model.Säätöpoikkeamasta vapaa malliprediktiivinen säädin käyttäen Koopman-Wiener malleja. Tiivistelmä. Tämä diplomityö perustui aiemmin kehitettyyn epälineaariseen Koopman-Wiener malliprediktiiviseen säätimeen. Diplomityön tavoitteena oli löytää sopiva strategia eliminoimaan tasapainotilan säätöpoikkeama, joka on seurausta tilanteesta, jossa säätimen käyttämä malli ei vastaa ohjattavaa prosessia. Työssä tavoiteltiin myös säätimen toiminnan mahdollistamista sovelluksissa, joissa prosessin jokaista tilamuuttujaa ei mitata, ja saatavilla olevissa mittauksissa on kohinaa. Diplomityössä harkittiin useita eri strategioita vastaamaan säätimen ja prosessin mallien yhteensopimattomuuteen, mutta häiriön torjunta valikoitui pääasialliseksi lähestymistavaksi. Diplomityössä ehdotetaan, että häiriön torjuntaan käytettävä häiriömalli voidaan valita laskemalla empiirinen havaittavuus Gramin matriisi yhdessä alkupisteessä jokaiselle harkitulle häiriömallille ja sitten valitsemalla malli, joka tulkitaan eniten havaittavaksi. Ehdotettu havaittavuusanalyysi tuottaa tietoa heikosta havaittavuudesta häiriöaugmentoidulle mallille vain valitussa alkupisteessä. Siitä huolimatta, tässä työssä argumentoitiin, että tulosten voidaan olettaa kuvastavan olennaista prosessin toiminta-aluetta, ja menetelmä soveltuu täten häiriömallin valitsemiseen. Vaihtoehtona häiriön torjunnalle, tässä työssä tutkittiin myös rekursiivista pienimmän neliösumman menetelmää adaptoimaan säätimessä käytettävää Koopman-Wiener-mallia ajon aikana. Tilaestoimointiin tässä työssä käytettiin hajustamatonta Kalman suodinta. Diplomityö demonstroi valittujen menetelmien suorituskykyä kahdella epälineaarisella tapaustutkimuksella: simuloitu jatkuvatoiminen sekoitusreaktori ja simuloitu tislauskolonni. Tässä työssä esitetään kolme tärkeää tulosta. Ensimmäiseksi, säädin joka käyttää häiriön torjuntaa, onnistuu poistamaan tasapainotilan säätöpoikkeaman takaisinkytketyssä systeemissä. Toiseksi, säädin ei saavuta tyydyttävää suorituskykyä rekursiivista pienimmän neliösumman menetelmää käytettäessä. Kolmanneksi, tapaustutkimukset tukevat ehdotettua lähestymistapaa häiriömallinnukseen, koska valitut häiriömallit johtavat parempaan tai yhtä hyvään säätimen suorituskykyyn verrattuna muiden häiriömallien käyttämiseen. Lisäksi tulokset tukevat hyödyllisen heuristisen säännön esittämistä Koopman-Wiener-mallien häiriömallintamiselle siten, että häiriömuuttujat vaikuttavat mallin dynaamisesti hitaisiin tilamuuttujiin

Survey on Recent Advances in Integrated GNSSs Towards Seamless Navigation Using Multi-Sensor Fusion Technology

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.©2021 The Authors. Published by ION.fi=vertaisarvioimaton|en=nonPeerReviewed

Unscented Kalman Filter for Brain-Machine Interfaces

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation

Widely Linear State Space Filtering of Improper Complex Signals

Complex signals are the backbone of many modern applications, such as power systems, communication systems, biomedical sciences and military technologies. However, standard complex valued signal processing approaches are suited to only a subset of complex signals known as proper, and are inadequate of the generality of complex signals, as they do not fully exploit the available information. This is mainly due to the inherent blindness of the algorithms to the complete second order statistics of the signals, or due to under-modelling of the underlying system. The aim of this thesis is to provide enhanced complex valued, state space based, signal processing solutions for the generality of complex signals and systems. This is achieved based on the recent advances in the so called augmented complex statistics and widely linear modelling, which have brought to light the limitations of conventional statistical complex signal processing approaches. Exploiting these developments, we propose a class of widely linear adaptive state space estimation techniques, which provide a unified framework and enhanced performance for the generality of complex signals, compared with conventional approaches. These include the linear and nonlinear Kalman and particle filters, whereby it is shown that catering for the complete second order information and system models leads to significant performance gains. The proposed techniques are also extended to the case of cooperative distributed estimation, where nodes in a network collaborate locally to estimate signals, under a framework that caters for general complex signals, as well as the cross-correlations between observation noises, unlike earlier solutions. The analysis of the algorithms are supported by numerous case studies, including frequency estimation in three phase power systems, DIFAR sonobuoy underwater target tracking, and real-world wind modeling and prediction.Open Acces

A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives

Efficient localization plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would contribute to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities for enhancing localization of UAVs and UGVs. In this paper, we review the radio frequency (RF) based approaches for localization. We review the RF features that can be utilized for localization and investigate the current methods suitable for Unmanned vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localization for both UAVs and UGVs is examined, and the envisioned 5G NR for localization enhancement, and the future research direction are explored

Model Identification and Robust Nonlinear Model Predictive Control of a Twin Rotor MIMO System

PhDThis thesis presents an investigation into a number of model predictive control (MPC) paradigms for a nonlinear aerodynamics test rig, a twin rotor multi-input multi-output system (TRMS). To this end, the nonlinear dynamic model of the system is developed using various modelling techniques. A comprehensive study is made to compare these models and to select the best one to be used for control design purpose. On the basis of the selected model, a state-feedback multistep Newton-type MPC is developed and its stability is addressed using a terminal equality constraint approach. Moreover, the state-feedback control approach is combined with a nonlinear state observer to form an output-feedback MPC. Finally, a robust MPC technique is employed to address the uncertainties of the system. In the modelling stage, analytical models are developed by extracting the physical equations of the system using the Newtonian and Lagrangian approaches. In the case of the black-box modelling, artificial neural networks (ANNs) are utilised to model the TRMS. Finally, the grey-box model is used to enhance the performance of the white-box model developed earlier through the optimisation of parameters using a genetic algorithm (GA) based approach. Stability analysis of the autonomous TRMS is carried out before designing any control paradigms for the system. In the control design stage, an MPC method is proposed for constrained nonlinear systems, which is the improvement of the multistep Newton-type control strategy. The stability of the proposed state-feedback MPC is guaranteed using terminal equality constraints. Moreover, the formerly proposed MPC algorithm is combined with an unscented Kalman filter (UKF) to formulate an output-feedback MPC. An extended Kalman filter (EKF) based on a state-dependent model is also introduced, whose performance is found to be better compared to that of the UKF. Finally, a robust MPC is introduced and implemented on the TRMS based on a polytopic uncertainty that is cast into linear matrix inequalities (LMI)

Input and State Estimation for Discrete-Time Linear Systems with Application to Target Tracking and Fault Detection

This dissertation first presents a deterministic treatment of discrete-time input reconstruction and state estimation without assuming the existence of a full-rank Markov parameter. Algorithms based on the generalized inverse of a block-Toeplitz matrix are given for 1) input reconstruction in the case where the initial state is known; 2) state estimation in the case where the initial state is unknown, the system has no invariant zeros, and the input is unknown; and 3) input reconstruction and state estimation in the case where the initial state is unknown and the system has no invariant zeros. In all cases, the unknown input is an arbitrary deterministic or stochastic signal. In addition, the reconstruction/estimation algorithm is deadbeat, which means that, in the absence of sensor noise, exact input reconstruction and state estimation are achieved in a finite number of steps. Next, asymptotic input and state estimation for systems with invariant zeros is considered. Although this problem has been widely studied, existing techniques are confined to the case where the system is minimum phase. This dissertation presents retrospective cost input estimation (RCIE), which is based on retrospective cost optimization. It is shown that RCIE automatically develops an internal model of the unknown input. This internal model provides an asymptotic estimate of the unknown input regardless of the location of the zeros of the plant, including the case of nonminimum-phase dynamics. The input and state estimation method developed in this dissertation provides a novel approach to a longstanding problem in target tracking, namely, estimation of the inertial acceleration of a body using only position measurements. It turns out that, for this problem, the discretized kinematics have invariant zeros on the unit circle, and thus the dynamics is nonminimum-phase. Using optical position data for a UAV, RCIE estimates the inertial acceleration, which is modeled as an unknown input. The acceleration estimates are compared to IMU data from onboard sensors. Finally, based on exact kinematic models for input and state estimation, this dissertation presents a method for detecting sensor faults. A numerical investigation using the NASA Generic Transport Model shows that the method can detect stuck, bias, drift, and deadzone sensor faults. Furthermore, a laboratory experiment shows that RCIE can estimate the inertial acceleration (3-axis accelerometer measurements) and angular velocity (3-axis rate-gyro measurements) of a quadrotor using vision data; comparing these estimates to the actual accelerometer and rate-gyro measurements provide the means for assessing the health of the accelerometer and rate gyro.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145813/1/ansahmad_1.pd