Model-based State Observer Research Articles

Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation.

The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the most common procedures is the palpation of soft tissues to identify their mechanical characteristics. In particular, it is very useful to identify the tissue's stiffness or equivalently its elasticity coefficient. However, this identification relies on the existence of a force sensor or a tactile sensor mounted at the tip of the robot, as well as on measuring the robot velocity. For some applications it would be desirable to identify the biomechanical characteristics of soft tissues without the need for a force/tactile nor velocity sensors. An estimation of such quantities can be obtained by a model-based state observer for which the inputs are only the robot joint positions and its commanded joint torques. The estimated velocities and forces can then be employed for closed-loop force control, force reflection, and mechanical parameters estimation. In this work, a closed-loop force control is proposed based on the estimated contact forces to avoid any tissue damage. Then, the information from the estimated forces and velocities is used in a least squares estimator of the mechanical parameters. Moreover, the estimated biomechanical parameters are employed in a Bayesian classifier to provide further help for the physician to make a diagnosis. We have found that a combination of the parameters of both linear and nonlinear viscoelastic models provide better classification results: 0% misclassifications against 50% when using a linear model, and 3.12% when using only a nonlinear model, for the case in which the samples have very similar mechanical properties.

Uncalibrated image-based visual servoing approach for translational trajectory tracking with an uncertain robot manipulator

This work presents an adaptive image-based visual servoing approach for uncertain robot manipulators using an eye-to-hand camera configuration, which does not require any calibration procedure and image velocity measurement. A monocular camera provides visual feedback for the robot’s joint control, allowing successful tracking of the translational trajectory, prescribed in image space, for a given target object attached to the robot end-effector. As our contribution, an indirect adaptive control approach is proposed to deal with the uncertain parameters of the camera–robot system and any camera misalignment concerning the robot frame. The indirect adaptive visual servoing approach is then combined with a direct adaptive motion controller using a cascade control framework to consider the uncertain robot dynamics in our stand-alone robot vision system. Lastly, an adaptive model-based state observer is designed to avoid the need for measuring the image feature velocity in the interconnected control loops. The Lyapunov stability theory and the passivity paradigm are synergized to demonstrate the stability properties of the overall closed-loop system. Experimental results, obtained with a 4-DoF robot manipulator carrying out translational trajectory tracking tasks using a commercial webcam, illustrate the performance and effectiveness of the proposed adaptive control methodology.

Deep Deterministic Policy Gradient-DRL Enabled Multiphysics-Constrained Fast Charging of Lithium-Ion Battery

Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This article proposes a knowledge-based, multiphysics-constrained fast charging strategy for lithium-ion battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic framework combining model-based state observer and a deep reinforcement learning (DRL)-based optimizer is proposed, for the first time, to provide a LIB fast charging solution. Within the DRL framework, a multiobjective optimization problem is formulated by penalizing the over-temperature and degradation. An improved environmental perceptive deep deterministic policy gradient (DDPG) algorithm with priority experience replay is exploited to tradeoff smartly the charging rapidity and the compliance of physical constraints. The proposed DDPG-DRL strategy is compared experimentally with the rule-based strategies and the state-of-the-art model predictive controller to validate its superiority in terms of charging rapidity, enforcement of LIB thermal safety and life extension, as well as the computational tractability.

A framework for battery internal temperature and state-of-charge estimation based on fractional-order thermoelectric model

In recent years, the rapid development of electric vehicles has raised a wave of innovation in lithium-ion batteries. The safety operation of lithium-ion batteries is one of the major bottlenecks restraining the development of the energy storage market. The temperature especially the internal temperature can significantly affect the performance and safety of the battery; therefore, this paper presented a novel framework for joint estimation of the internal temperature and state-of-charge of the battery based on a fractional-order thermoelectric model. Due to the nonlinearity, coupling, and time-varying parameters of lithium-ion batteries, a fractional-order thermoelectric model which is suitable for a wide temperature range is first established to simulate the battery’s thermodynamic and electrical properties. The parameters of the model are identified by the electrochemical impedance spectroscopy experiments and particle swarm optimization method at six different temperatures, and then the relationship between parameters and temperature is obtained. Finally, the framework for joint estimation of both the cell internal temperature and the state-of-charge is presented based on the model-based state observer. The experimental results under different operation conditions indicated that, compared with the traditional off-line prediction method, the model-based online estimation method not only shows stronger robustness under different initial conditions but also has better accuracy. Specifically, the absolute mean error of the estimation of state-of-charge and internal temperature based on the proposed method is about 0.5% and 0.3°C respectively, which is about half of that based on the off-line prediction method.

Composite Learning Control of Overactuated Manned Submersible Vehicle With Disturbance/Uncertainty and Measurement Noise.

In this article, a novel composite learning control scheme based on nonlinear disturbance observer (NDOB), neural network (NN), and model-based state observer (MSOB) is investigated for the manned submersible vehicle. First, an MSOB is employed to reconstruct the real output signals from noise-contained measurements. Second, a composite estimation is developed where an NDOB is designed to estimate external disturbance and an NN is employed for model uncertainty. Furthermore, a control allocation technique is used to address the overactuated problem of the manned submersible vehicle. The rigorous stability analysis of the closed-loop manned submersible system is given via the Lyapunov theorem. Finally, several representative simulation results illustrate the superior control performance of the composite learning control scheme for the manned submersible vehicle.

Model-Based Temperature Feedback Control of Laser Cladding Using High-Resolution Hyperspectral Imaging

Laser cladding is a technique that is frequently used for the coating and repair of metallic components. More recently, the technology is used in the additive manufacturing domain for building freeform three-dimensional parts. A lot of attention is dedicated to the optimization of process parameters and to real-time feedback control strategies. This paper presents a feedback control scheme in which a hyperspectral camera is used to provide absolute temperature measurements of the melt pool surface with a high spatial resolution of 12 μm/pixel. A combined linear state feedback and proportional-integral controller actuates the laser based on the measured temperature profile in order to maintain a constant melt pool width. The controller includes a model-based state observer for suppressing the noise introduced by powder particles that are present in the laser cladding process with coaxial powder feeding. The performance of the controller is evaluated by creating beads with varying thicknesses on a base plate of AISI 316L stainless steel. The experimental results demonstrate that the controller is able to successfully regulate the melt pool size with a standard deviation that is smaller than 10 μm during laser melting (without powder) and 50 μm during laser cladding (with powder).

A model-based observer for state and stress estimation in structural and mechanical systems: Experimental validation

In this paper we present the results from a validation study of a recently proposed model-based state observer for structural and mechanical systems. The observer uses a finite element model of the structure and noise contaminated measurements to estimate the state and stress time histories at arbitrary locations in the structure of interest. The initial conditions and unknown excitations are described by random vectors and random processes with known covariance and power spectral density. A laboratory model consisting of an aluminum cantilever beam was used to perform the experiment. Two types of loading conditions were tested: an impact hammer test and a band limited excitation delivered through a shaker. The results obtained with the proposed observer are compared to the measured stress at the locations of interest, and to estimates obtained using well-established estimation methods such as Luenberger observers and the Kalman filter. The main finding is that for all experiments conducted the proposed model-based observer yielded estimates with higher or comparable accuracy to all other methods considered, with the advantage of requiring significantly less computational effort and with a more direct and transparent implementation.

A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles

One of the most important aspects in battery management systems (BMS) in electric vehicles is the state of charge (SOC) estimation. SOC needs to be accurately determined for safety and performance reasons but cannot be measured directly due to the flatness and hysteresis of the open circuit voltage (OCV) curve of Lithium-ion chemistries as LiFePO4. The classical approach of current integration (Coulomb counting) cannot solve the problems of accumulative error and inaccurate initial values, thus advanced estimation algorithms are applied to determine the sate of charge. In this work, three model-based state observer designs including Luenberger observer, Extended Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF) are carried out and studied. These estimation approaches are verified using measurement data acquired from commercial LiFePO4 cells. In addition, computational tests analyze the systems performances in terms of tracking accuracy, estimation robustness against temperature uncertainty, sensor drift, and convergence behavior with an initial SOC offset.

Observer‐based online compensation of inner filter effect in monitoring fluorescence of GFP‐expressing plant cell cultures

The green fluorescent protein (GFP) isolated from the jellyfish Aequorea victoria is a very useful reporter for real-time bioprocess sensing. GFP culture fluorescence is a composite signal that can be influenced by factors such as culture autofluorescence, inner filter effect (IFE), and photobleaching. These factors complicate accurate estimation of GFP concentrations from the culture fluorescence. IFE is especially problematic when using GFP in monitoring transgenic plant cell suspension cultures, due to the aggregated nature of the cells and the high biomass concentration in these culture systems. Reported approaches for online compensation of IFE in monitoring culture NADH fluorescence or bioluminescence require online measurement of biomass density or culture turbidity/optical density, in addition to fluorescence/bioluminescence measurement. In this study, culture GFP fluorescence was used successfully to estimate GFP concentration and other important states in bioreactor culture of transgenic tobacco cells, while the influences of IFE and culture autofluorescence were rectified without the need for an additional biomass sensor. This was achieved by setting up a novel model-based state observer. First, we developed an improved model for a backscatter fluorescence probe that takes into account the influence of IFE and autofluorescence on reporting culture GFP concentration from online fluorescence. The state observer was then established using the extended Kalman filter (EKF), based on the fluorescence probe model, a dynamic state model of the plant cell bioreactor, and online GFP fluorescence measurement. Several versions of the observer were introduced to address practical requirements associated with monitoring GFP fluorescence of plant cell cultures. The proposed approach offers an effective means for online compensation of IFE to enable quantitative interpretation of the culture fluorescence signals for accurate reporting of GFP or GFP-fusion protein expression.

State Observer Design Based on a Model of Chemostat Microbial Cultivation Accounting for the Memory Effects

The problem of model-based state observer design for the chemostat microbial cultivation is considered. General chemostat model accounting for the memory (delay) effects is used for the state observer design. The memory effects influencing the process dynamics are described by zero-order exponential memory functions in the expressions of the specific growth rate and specific consumption rate. Two modifications of the general model (µ-type and S-type models) are used for the state observer design. In the first modification, the memory effects are accounted for by weighted averages of the specific growth and consumption rates' past values. The second modification assumes that the memory effects are accounted for by weighted averages of the past values of the substrate concentration only. Particular cases, when the memory effects are taken into account in the specific growth and consumption rates by equal memory functions and when the memory effects are taken into account in the expression of the specific growth rate only, are considered. The proposed procedure is based on the Kalman filtering method under the assumption for full knowledge of the process kinetics and on-line measurement of the limiting substrate concentration. As a case study, a model of the continuous growth of a strain Saccharomyces cerevisiae on a glucose-limited medium is considered. The robustness of the observer with respect to the adaptability parameter variations is studied.