Proposed Hybrid Observer Research Articles

Hybrid State Observer Design for Estimating the Hitch Angles of Tractor-Multi Unit Trailer

In this paper, a new approach of state observer is proposed for the tractor with multi unit trailers. In the case of tractor with two articulated trailers, the dynamic characteristics of the trailers are dominantly determined by the two hitch angles connecting each trailer. A novel estimation system for the hitch angle is introduced by combining the Kalman filter with the deep learning network and transfer function techniques. The Gated Recurrent Unit (GRU) network is constructed to calculate hitch angles and these values are used as the virtual measurement in the Kalman filter. The dynamic characteristics of two trailers with respect to the hitch angles are expressed as the transfer function models and these models are used to calculate the hitch angles as the virtual measurement. The virtual measurements from the two methods are integrated separately into the Kalman filter design. The estimation performance of the hitch angle with the proposed hybrid observer is validated in simulations with various curvature scenarios.

Continuous-Discrete Time Observer Design for Multi-Output Linear Continuous Systems With Aperiodic Asynchronous Measurements

In this letter, a new observer is proposed for the state estimation of time-invariant systems with multiple outputs providing non periodic and asynchronous measurements. The state of the proposed hybrid observer is made up of two parts, the first one corresponding to the estimate which is continuous and the second one corresponding to a kind of output error predictor which is updated when new measurements are received. The convergence of the observer is analyzed and ensured with a condition on the maximum value of the outputs sample rates, with the help of a Lyapunov approach and is further recast using a Sum of Squares decomposition approach. Results of various simulations illustrate the efficiency of the observer.

Global Asymptotic Convergent Observer for SLAM

This paper examines the global convergence problem of SLAM algorithms, an issue that faces topological obstructions. This is because the state-space of attitude dynamics is defined on a non-contractible manifold: the special orthogonal group of order three SO(3). Therefore, this paper presents a novel, gradient-based hybrid observer to overcome these topological obstacles. The Lyapunov stability theorem is used to prove the globally asymptotic convergence of the proposed algorithm. Finally, comparative analyses of two simulations were conducted to evaluate the performance of the proposed scheme and to demonstrate the superiority of the proposed hybrid observer to a smooth observer.

A robust hybrid observer for monitoring high-cell density cultures exhibiting overflow metabolism

This work proposes a hybrid observer based on an asymptotic observer combined with an extended Kalman filter applied to high-density cultures of S. cerivisiae. The observer estimates the concentrations of biomass and ethanol, and the specific growth rate based on measurements of dissolved oxygen, carbon dioxide, and substrate (glucose). Computational simulations showed that the proposed hybrid observer could accurately detect the switch between oxidative and respiro-fermentative regimes under aerobic conditions. This switch is the metabolic signature of overflow metabolism – or Crabtree effect – in yeast. The simulations were performed under the assumption that there is no oxygen limitation, although it is controlled. Monte Carlo simulations enabled evaluation of the observer’s performance, showing stability under different metabolic regimes and a robust estimation under high measurement noise and parametric uncertainty. Furthermore, the performance of the hybrid observer was superior to classic observers like the asymptotic observer and extended Kalman filter in the simulated conditions. The proposed observer can be readily extended and applied to different cell cultures exhibiting overflow metabolism by modifying the particular process and culture parameters.

An Air-to-Fuel ratio estimation strategy for turbocharged spark-ignition engines based on sparse binary HEGO sensor measures and hybrid linear observers

An effective Air-to-Fuel Ratio (AFR) control is paramount to ensure a good combustion and high catalyst efficiency. This work addresses the problem of determining continuous-time estimates of AFR in turbocharged Spark Ignition (SI) engines on the basis of binary sparse measurements of the exhaust gas Oxygen. The latter are provided by a HEGO (Heated Exhaust Gas Oxygen) sensor installed at the catalytic converter input in place of a more expensive linear UEGO (Universal Exhaust Gas Oxygen) sensor, as nowadays common in commercial cars. The HEGO sensor outputs two voltage values only, corresponding respectively to low or high concentration of the residual Oxygen in the exhaust gas (on/off behavior). In view of this, it can be classified as a binary sensor generating irregular and sparse measurements in that the useful information is only present at the instants of the on/off and off/on transitions. An estimation scheme based on the use of a recursive least-squares algorithm has been designed by resorting to the theory of linear hybrid observers with quantized inputs. A detailed convergence analysis of the state reconstruction error is also provided. The proposed hybrid observer scheme is employed in a PI control-loop designed to maintain the AFR close to a desired value. The effectiveness of the proposed method is demonstrated by several numerical simulations based on both synthetic and real data.

Road traffic density estimation and congestion detection with a hybrid observer-based strategy

Reliable detection of traffic congestion provides pertinent information for improving safety and comfort by alerting the driver to crowded roads or providing useful information for rapid decision-making. This paper addresses the problem of road traffic congestion estimation and detection from a statistical approach. First, a piecewise switched linear traffic model (PWSL)-based observer is introduced. The proposed hybrid observer (HO) estimates the unmeasured traffic density, thus reducing the cost of implementing and maintenance sensors and measurements devices. Here, the observer gains of each mode are obtained by solving a set of linear matrix inequalities. Second, a novel method for efficiently monitoring traffic congestion is proposed by combining the proposed HO with a generalized likelihood ratio (GLR) test. Also, an exponentially-weighted moving average (EWMA) filter is applied to the residual data to reduce high-frequency noise. Thus, as the EWMA filter, aggregates all of the information from past and actual samples in the decision rule, it extends the congestion detection abilities of the GLR test to the detection of incipient changes. This study shows that a better performance is achieved when GLR is applied to filtered data than to unfiltered data. The effectiveness of the proposed approach is verified on traffic data from the four-lane State Route 60 (SR-60) and the three lanes Interstate 210 (I-210) in California freeways. Results show the efficacy of the proposed HO-based EWMA-GLR method to monitor traffic congestions. Also, the proposed approach is compared to that of the conventional Shewhart and EWMA approaches and found better performance.

A hybrid fixed-time observer for state estimation of linear systems

This paper deals with the problem of state estimation for single-output linear systems. Under some structural properties of the system matrices it is possible to design a hybrid observer capable of estimating the state of the system exactly and in a finite time. Moreover, the proposed hybrid observer provides fixed-time convergence of the state estimation error, i.e. there exists a convergence time that is bounded and such a bound is independent of the initial estimation error. The robustness properties of the hybrid observer are analyzed for the linear system with unknown inputs, and the linear system with unknown inputs and parametric uncertainty. Some simulation results illustrate the effectiveness of the proposed hybrid observer.

Design of a dynamical hybrid observer for pressure swing adsorption processes

As an integral part of a hybrid control system for Pressure Swing Adsorption (PSA) processes, a dynamical hybrid observer is proposed for online reconstruction of the active mode and continuous states of these processes. Hybrid systems feature both continuous and discrete-event dynamics and hence are very suited to describe PSA processes. For estimation of the active mode, a mode observer is designed, and the continuous spatial profiles in each mode are estimated by distributed and decentralized Kalman filters. The proposed hybrid observer has been applied, in silico, for a two-bed, six-step PSA process used for Hydrogen purification. The active mode of the process along with the continuous spatial profiles of its adsorption beds have been estimated quite accurately based on the measured temperatures and pressures at a few points in the process, these measured signals are all noise-corrupted and measured discretely.

Dynamical Hybrid Observer for Pressure Swing Adsorption Processes

A dynamical hybrid observer is proposed for online reconstruction of the active mode and continuous states of Pressure Swing Adsorption (PSA) processes as an integral part of a hybrid control system. A mode observer is designed for estimation of the active mode, and the continuous spatial profiles are estimated by a Distributed and Decentralized Switching Kalman Filter. The proposed hybrid observer has been applied, in silico, for a two-bed, six-step PSA process. The active mode of the process along with the continuous spatial profiles of its adsorption beds have been estimated quite accurately based on very limited number of noise corrupted temperature and pressure measurements.

Hybrid observer for parameters estimation in ethylene polymerization reactor: A simulation study

In this work, we proposed a novel hybrid fuzzy-sliding mode observer designed in such a manner that it can be utilized to estimate various parameters by simply using the related process model, without redesigning the structure of the whole observer. The performances and effectiveness of this hybrid observer are shown through numerical simulation based on a case study involving an ethylene polymerization process to estimate the ethylene and butene concentrations in the reactor as well as the melt flow index. It can be concluded that the proposed hybrid observer provides fast estimation with a high rate of accuracy even in the presence of disturbances and noise in the model. This hybrid observer is also compared with the sliding mode, extended Luenberger and proportional sliding mode observers to highlight its effectiveness and advantages over these observers.

Continuous and Discrete State Estimation for a Class of Nonlinear Switched Systems

This brief investigates the hybrid observer design for a class of nonlinear switched systems in which the discrete part is governed by a Petri net. The proposed hybrid observer represents a combination of a continuous observer based on the second-order sliding mode approach and a discrete one based on an unknown input observer. An illustrative example ends this brief and shows the efficiency of the proposed approach.

Observer design for DC/DC power converters with bilinear averaged model

Increased demand for high bandwidth and high efficiency made full state-feedback control solutions very attractive to power-electronics community. However, full state measurement is economically prohibitive for a large range of applications. Moreover, state measurements in switching power converters are very noisy. These facts make the development of observer design targeted specifically to power electronics highly important. This paper analyses suitability of two previously proposed hybrid observer design techniques for continuous-time linear switched and discrete-time piecewise affine systems in context of power converters. Furthermore, a novel design procedure of Luenberger type observers is proposed for discrete-time systems with input-induced bilinearity. The performance of developed observers is demonstrated through simulation and experimental results obtained for a buck-boost converter.