Direct Inverse Model Research Articles

Pyrolysis Model for Predicting the Heat Release Rate of Birch Wood

ABSTRACTThe numerical modeling of solid pyrolysis for fire simulations consists of three stages: specifying the reaction scheme and physical models, estimating the kinetic and thermal parameters from experimental data, and then solving the system using a computer. The interpretation of the experimental input parameters must be verified by reproducing the experimental conditions with the same model for which the parameters are being sought. In this work, the performance of three previously proposed reaction schemes of wood pyrolysis in reproducing thermogravimetric experiments of birch wood was evaluated, and the remaining model parameters from small and bench scale calorimetric experiments were determined. The predictive capability was tested by cone calorimeter experiments at different heat fluxes. The results indicate that the first-order single-step reaction scheme can provide equally good predictions for the heat release rate as the more complex schemes. The source of the thermal parameters—direct measurement or inverse modeling—did not have a great influence on the predictive capability.

A unified dynamic algorithm for wheeled multibody systems with passive joints and nonholonomic constraints

This paper presents a systematic approach to develop a generalized symbolic/numerical dynamic algorithm for modeling and simulation of multibody systems with branches and wheels. The proposed dynamic algorithm includes the direct kinematic and inverse dynamic models of the wheeled systems with prismatic/revolute as well as actuated/passive degrees of freedom. Using the geometric configuration of the system through modified Denavit–Hartenberg convention, symbolic equations in general algorithmic form are developed for kinematic constraints associated with the wheel–ground contacts. The Newton–Euler equations are used to develop an algorithm for the inverse dynamic model of the multibody system. The complete algorithm is then used to solve the kinematics and dynamics of the system, and computes: (i) the kinematics of the external/internal passive degrees of freedom of the system, (ii) the Lagrange multipliers associated with the wheel–ground contacts, and (iii) the driving forces/torques of the actuated degrees of freedom. Some examples are solved with the help of the proposed algorithm, using MATLAB, to illustrate its implementation on different wheeled systems. These examples include a differential wheeled robot, a snake-like wheeled system, and a bicycle.

Full closed-loop controls of micro/nano positioning system with nonlinear hysteresis using micro-vision system

The design and full closed-loop controls of micro/nano positioning system are presented in this paper. The micro/nano positioning system mainly consists of a 3-DOF compliant mechanism, a micro-vision system and three piezoelectric actuators. In order to compensate for nonlinear hysteresis in the piezoelectric actuators, an adaptive controller using direct inverse modeling approach based on a modified Prandtl–Ishlinskii model is presented. Instead of using traditional displacement sensors, a micro-vision system (MVS) is used to obtain feedback signals online. Based on the micro-vision system, a full closed-loop tracking control is developed to enhance the positioning accuracy of micro/nano positioning system. Taking into account the coupling motions, another full closed-loop tracking control with a decoupling feedforward controller is also proposed. Experimental results demonstrate that the full closed-loop controls can improve the positioning accuracy when compared with an open-loop control. Furthermore, the decoupling feedforward controller can improve the tracking performance of full closed-loop tracking control as well.

An Inverse Kinematics Solution Based on CA-CMAC and ILC for Trajectory Tracking of Redundant DOF Manipulator

The composite control strategy of CA-CMAC and ILC is adopted to solve the inverse kinematic problem of the redundant DOF manipulator during its real-time and high-precision tracking on the three-dimensional space target trajectory. A direct inverse model control strategy is adopted, in which CA-CMAC takes the current joint angles and the desired position increment of the manipulator as the input, and estimates the expected joint angle increments of the manipulator using the system history control experience. Then the estimated joint angles are taken as the initial value of the ILC module by which the control effect is improved iteratively. Based on the MATLAB, the tracking controls of linear and circular space target trajectories were simulated respectively. The results show that CA-CAMC and ILC composite control has better tracking precision and stability than CMAC control, while keeping the joint angles of the manipulator continuous and smooth during trajectory tracking.

Position control of a bio-inspired semi-active joint with direct inverse hysteresis modeling and compensation

Accurate position control of pneumatic artificial muscle actuated systems has always been difficult due to their inherent nonlinear hysteresis characteristics. This article designs a bio-inspired semi-active robotic joint that is synergistically driven by a pneumatic artificial muscle and an extension spring by imitating the mechanism of energy storage and return in animals and then studies its position tracking control with hysteresis compensation. To simplify the derivation of the inverse hysteresis model which functions as hysteresis compensator, a novel approach termed as direct inverse hysteresis modeling is adopted. With this method, the inversion of the asymmetric angle–pressure hysteresis of the semi-active joint is modeled directly from experimental measurements by a modified Prandtl–Ishlinskii model. On the basis, a closed-loop position tracking controller composed of forward hysteresis compensation and conventional proportional–integral–derivative control is designed for the joint. Experimental results indicate that the proposed controller can track the reference position signals with high accuracy.

Force tracking control of an electro-hydraulic control loading system on a flight simulator using inverse model control and a damping compensator

The electro-hydraulic control loading system (EHCLS) of a flight simulator is mainly used to simulate the force feel of flying a real airplane. A double-loop model, including control and hydraulic mechanism of the EHCLS, is established and its force–displacement impedance is analysed to evaluate force tracking performances and its stability. An approximated feed-forward inverse model controller is designed using a zero phase error compensation method for expanding the frequency bandwidth of the inner loop because the identified force closed-loop model is a nonminimum phase system and its direct inverse model is unstable. Modelling error between the designed feed-forward inverse model and the actual plant is discussed and a damping compensator is presented to increase stability of the EHCLS. Theoretical analysis, simulation and experimental results show that the control methods presented are feasible and can effectively improve force feel fidelity and stability of the EHCLS.

Joint kinematic calculation based on clinical direct kinematic versus inverse kinematic gait models

Most clinical gait laboratories use the conventional gait analysis model. This model uses a computational method called Direct Kinematics (DK) to calculate joint kinematics. In contrast, musculoskeletal modelling approaches use Inverse Kinematics (IK) to obtain joint angles. IK allows additional analysis (e.g. muscle-tendon length estimates), which may provide valuable information for clinical decision-making in people with movement disorders.The twofold aims of the current study were: (1) to compare joint kinematics obtained by a clinical DK model (Vicon Plug-in-Gait) with those produced by a widely used IK model (available with the OpenSim distribution), and (2) to evaluate the difference in joint kinematics that can be solely attributed to the different computational methods (DK versus IK), anatomical models and marker sets by using MRI based models.Eight children with cerebral palsy were recruited and presented for gait and MRI data collection sessions. Differences in joint kinematics up to 13° were found between the Plug-in-Gait and the gait 2392 OpenSim model. The majority of these differences (94.4%) were attributed to differences in the anatomical models, which included different anatomical segment frames and joint constraints. Different computational methods (DK versus IK) were responsible for only 2.7% of the differences. We recommend using the same anatomical model for kinematic and musculoskeletal analysis to ensure consistency between the obtained joint angles and musculoskeletal estimates.

Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies

The debutanizer column is an important unit operation in petroleum refining industries as it is the main column to produce liquefied petroleum gas as its top product and light naphtha as its bottom product. This system is difficult to handle from a control standpoint due to its nonlinear behavior, multivariable interaction and existence of numerous constraints on both its manipulated and state variable. Neural network techniques have been increasingly used for a wide variety of applications where statistical methods have been traditionally employed. In this work we propose to use an equation based MIMO (Multi Input Multi Output) neural network based multivariable control strategy to control the top and bottom temperatures of the column simultaneously, while manipulating the reflux and reboiler flow rates respectively. This equation based neural network model represented by a multivariable equation, instead of the normal black box structure, has the advantage of being robust in nature while being easier to interpret in terms of its input output variables. It is implemented for set point changes and disturbance changes and the results show that the neural network based model method in the direct inverse and internal model approach performs better than the conventional PID method in both cases.

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

In a pharmaceutical industry, batch extractive distillation (BED), a combination process between extraction and distillation processes, has been widely implemented to separate waste solvent mixture of acetone-methanol because of minimum-boiling azeotrope properties. Normally, water is used as solvent and semi-continues mode is proposed to improve purity of acetone. The solvent is charged into the BED column until the purity of a desired product is achieved. After the total reflux start-up period is ended, a dynamic optimization strategy is applied to determine an acetone distillate composition profile maximizing the weight of the distillate product (acetone). The acetone distillate composition profile is used as the set point of neural network model-based controllers: the neural network direct inverse model control (NNDIC) and neural network based model predictive control (NNMPC) in order to provide the acetone composition with the purity of 94.0% by mole within 9.5 hours. It has been found that although both NNDIC and proportional integral derivative (PID) control can maintain the distillate purity on its specification for the set point tracking and in presence of plant uncertainties, the NNMPC provides much more satisfactory control performance and gives the smoothest controller action without any fluctuation when compared to the NNDIC and PID.

Direct Resistivity Measurements of Core Sample using a Portable In-situ DC Resistivity Meter in Comparison with HERT Data

Abstract An indigenous apparatus was devised for measuring the true resistivity of core samples in laboratory or at the drilling site and tested for its efficacy. The true resistivities of selected core samples from a shallow borehole of Brahmaputra alluvium were measured using an in-situ DC resistivity meter and compared with corresponding High-resolution Electrical Resistivity Tomography (HERT) cross section acquired across the borehole site. We find a very good match between the direct measurements and inverse model data of HERT within the limits of resolution. Thus, the present work proves the validity and utility of the resistivity survey in identifying the nature of the sub-surface material and its stratification by measuring the core sample resistivity with the help of in-situ DC resistivity meter.

Real-time enhancement of tracking performances for cable-conduit mechanisms-driven flexible robots

Natural Orifice Transluminal Endoscopic Surgery (NOTES) is a method that allows for performing complex operations via natural orifices without skin incisions. Its main tool is a flexible endoscope. Cable-conduit mechanism (CCM) or tendon-sheath Mechanism (TSM) is often used in NOTES because of its simplicity, safety in design, and easy transmission. Nonlinearities between the cable and the conduit pose challenges in the motion control of the NOTES system. It is very difficult to achieve the precise position of robotic arms when the system is inside a human's body. This paper presents new approaches to model and control a pair of CCMs (TSMs) used in NOTES system. To deal with the change of cable-conduit configurations during its operation, two control schemes are proposed: (i) an updated table with offline backlash hysteresis learning is constructed. In this case, a simple computation of the direct inverse backlash hysteresis model is introduced without using output feedback for compensation; and (ii) an online estimation of the backlash hysteresis profile under the assumption of availability of output feedback. In this case, adaptive control laws are used to deal with the change of the endoscope configuration. The proposed model and compensation control schemes are experimentally validated using a prototype of a single-DOF-Master-Slave system, which consists of a master console, a telesurgical workstation, and a slave manipulator. The results show that the proposed model and the control schemes improve the tracking performances of the system. Compensation control schemes for cable-conduit system are developed.Hysteresis model parameters are identified and optimized for feedforward.Compensation with feedforward scheme is used for control purpose.Compensation with nonlinear adaptive scheme is developed for control purpose.The proposed models and control structure are validated satisfactorily.

Direct and Inverse Neural Modelization of Mobile Robots

In this paper, direct and inverse models determination of a mobile robot using artificial neural networks are proposed. The effectiveness of the proposed algorithm applied to the modeling of behavior of CHAR and KHEPERA robots is verified by simulation experiments. The results of simulation show that the use of the neural networks in the determination of direct model and inverse model is very interesting since it enables to guarantee the time competition and the quality of the modeling.

Improving Atomic Force Microscopy Imaging by a Direct Inverse Asymmetric PI Hysteresis Model

A modified Prandtl–Ishlinskii (PI) model, referred to as a direct inverse asymmetric PI (DIAPI) model in this paper, was implemented to reduce the displacement error between a predicted model and the actual trajectory of a piezoelectric actuator which is commonly found in AFM systems. Due to the nonlinearity of the piezoelectric actuator, the standard symmetric PI model cannot precisely describe the asymmetric motion of the actuator. In order to improve the accuracy of AFM scans, two series of slope parameters were introduced in the PI model to describe both the voltage-increase-loop (trace) and voltage-decrease-loop (retrace). A feedforward controller based on the DIAPI model was implemented to compensate hysteresis. Performance of the DIAPI model and the feedforward controller were validated by scanning micro-lenses and standard silicon grating using a custom-built AFM.

Optimization of a neural network based direct inverse control for controlling a quadrotor unmanned aerial vehicle

UAVs are mostly used for surveillance, inspection and data acquisition. We have developed a Quadrotor UAV that is constructed based on a four motors with a lift-generating propeller at each motors. In this paper, we discuss the development of a quadrotor and its neural networks direct inverse control model using the actual flight data. To obtain a better performance of the control system of the UAV, we proposed an Optimized Direct Inverse controller based on re-training the neural networks with the new data generated from optimal maneuvers of the quadrotor. Through simulation of the quadrotor using the developed DIC and Optimized DIC model, results show that both models have the ability to stabilize the quadrotor with a good tracking performance. The optimized DIC model, however, has shown a better performance, especially in the settling time parameter.

Prediction of properties of polymer concrete composite with tire rubber using neural networks

The neural network method was used to investigate the influence of filler and resin content on the mechanical properties of polymer concrete with powdered tire waste. The mechanical strengths of 10 experimentally determined combinations using mixed epoxy resin, aggregates and tire powder as filler were optimized using direct neural modeling and inverse neural modeling, by imposing a minimum cost (content in resin). Direct neural modeling gave the optimum composition for obtaining maximum values for compressive strength, flexural strength and split tensile strength. Inverse neural modeling analyzed the possibility of obtaining maximum values of mechanical properties by variations in the dosages of the epoxy resin and tire powder. Neural network modeling generated the mixes with the lowest cost and maximum strength. The modeling method has shown that two mechanical properties can be simultaneously optimized in the investigation domain. From direct modeling, the maximum compressive strength was obtained for a composition with 0.215 (fraction weight) epoxy resin and 0.3 (fraction weight) tire powder. Maximum flexural strength was obtained for experimental values of 0.23 epoxy resin and 0.17 tire powder with a severe reduction noted for smaller resin dosages. The maximum split tensile strength was obtained for a resin dosage of 0.24 and tire powder dosage of 0.17.

Fundamentals of Direct Inverse CFD Modeling to Detect Air Pollution Sources in Urban Areas

This paper presents the fundamentals of direct inverse modeling using CFD simulations to detect air pollution sources in urban areas. Generally, there are four techniques used for detecting pollution sources: the analytical technique, the optimization technique, the probabilistic technique, and the direct technique. The study discusses the potentialities and limits of each technique, where the direct inverse technique is focused. Two examples of applying the direct inverse technique in detecting pollution source are introduced. The difficulties of applying the direct inverse technique are investigated. The study reveals that the direct technique is a promising tool for detecting air pollution source in urban environments. However, more efforts are still needed to overcome the difficulties explained in the study.

RBF Neural Network Approach for Identification and Control of DC Motors

In this paper, a neural network approach for the identification and control of a separately excited direct (DC) motor (SEDCM) driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN) are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN). The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

Modeling of double ridge waveguide using ANN

The ridge waveguide is useful in various microwave applications because it can be operated at a lower frequency and has lower impedance and a wider mode separation than a simple rectangular waveguide. An accurate model is essential for the analysis and design of ridge waveguide that can be obtained using electromagnetic simulations. However, the electromagnetic simulation is expensive for its high computational cost. Therefore, artificial neural networks (ANNs) become very useful especially when several model evaluations are required during design and optimization. Recently, ANNs have been used for solving a wide variety of radio frequency (RF) and microwave computer-aided design (CAD) problems. Analysis and design of a double ridge waveguide has been presented in this paper using ANN forward and inverse models. For the analysis, a simple ANN forward model is used where the inputs are geometrical parameters and the outputs are electrical parameters. For the design of RF and microwave components, an inverse model is used where the inputs are electrical parameters and the outputs are geometrical parameters. This paper also presents a comparison of the direct inverse model and the proposed inverse model.

AMPLITUDE VERSUS OFFSET (AVO) TECHNIQUE FOR LIGHT HYDROCARBON EXPLORATION: A CASE STUDY

AVO as a known methodology is used to identify fluid type and reservoir lithology in subsurface exploration. Method discussed in this paper, consists of three stages, including: Direct modeling, Inverse modeling and Cross plot interpretation. By direct modeling we can clarify lithology or fluid dependent attributes. Analysis performed using both P-P and P-Sv attributes. Inverse modeling deals with real data and is fed by the results of direct modeling to identify the light hydrocarbon (gas) zones. The main role of cross plot interpretation is to confirm the inverse modeling results and consequently increasing validity of performed analysis. Using Hodogram – cross plot, makes possible to identify hydrocarbon zone even in small scales. This methodology was applied in Gorgan Plain Southeast Caspian, northern Iran. It was concluded that: fluid factor, SIGN, and Poisson reflectivity are fluid dependent attributes. It was also defined that normal incidence reflectivity and P-wave impedance reflectivity are lithology dependent. Inverted sections of fluid-dependent attributes defined the existence two light hydrocarbon accumula- tion under the Tertiary-Cretaceous unconformity in the North Gorgan Plain. Two wet and gaseous zones are also confirmed by cross plot.

Hexapod Robot Leg Dynamic Simulation and Experimental Control using Matlab

In this paper the authors present a hexapod robot structure. For the leg has been developed a simple simulation interface to ease the analysis of the motion. The control part is an experimental one and uses Matlab and Arduino Duemilanove development board. The movement of the leg tip along a predefined trajectory was made using a walking algorithm. Moving the leg tip from one point to another is made in two steps: stance phase and swing phase. The communication protocol is also presented. In order to control the robotic leg the direct kinematical model and inverse kinematical model were used. Also the dynamic model using Lagrange method was developed and implemented using SimMechanics toolbox and ode45 function from Matlab. The paper includes some experimental results related to the leg control and some simulations regarding dynamic model.