Passivity‐based adaptive control of a 2‐DOF serial robot manipulator with temperature dependent joint frictions

Mechanical systems are always suffering from the effects of temperature dependent friction forces where the system is operated in a wide range of temperature. Temperature and its variation play an important role in friction force in mechanical systems. If it is not compensated, it will tend to unwanted consequences, including steady‐state errors, limit cycling, and hunting. Therefore, it is necessary to take the temperature effects into account. This has been a strong motivation for the researchers to work on temperature effects on joint friction. In this paper, an adaptive compensation (control) scheme is proposed and applied to a 2‐degree‐of‐freedom serial robot manipulator by taking the temperature effects into account on the joints friction. In the proposed control scheme, the temperature is not required to be sensed. In this paper, joint friction is described by LuGre dynamic model with temperature dependent parameters. These parameters are described by some functions with unknown temperature dependent terms. According to the mathematical and practical concepts, the temperature dependent friction is decomposed into a viscous term and a disturbance term. An adaptive controller is designed to compensate the friction effect and it is shown that the proposed controller relaxes the condition for a priori knowledge about the environment characteristics, including the upper and lower bounds of the environment temperature and the parameters of the functions, describing the temperature dependent joint frictions. The stability and convergence of the joint position and velocity are proved in the sense of Lyapunov and then the proposed method is confirmed by the simulations.     

Fault detection and identification using combination of EKF and neuro-fuzzy network applied to a chemical process (CSTR)

Pattern Analysis and Applications - In this paper, a new algorithm is proposed for fault detection and identification (FDI) in a class of nonlinear systems by combining the extended Kalman filter...     

A new methodology for the production of furfural as a renewable energy source from bagasse in acidic aqueous media

Sugarcane is one of the most promising agricultural sources of biomass energy. Sugarcane produces mainly two types of biomass, cane trash and bagasse. Furfural is synthesized from bagasse hydrolysis in an acidic environment and as a result of pentose dehydration. This study has been focused on the production of furfural by using sulfuric acid plus an inorganic salt (NaCl + H₂SO₄) as catalyst in a pilot plant. The obtained experimental data show that sulfuric acid plus NaCl can be more effective in the production of furfural. Furthermore, in order to predict the outlet furfural percentage from reactors, a three-layer Feed-Forward Neural Network using temperature and pressure of reactor, time of reaction, sulfuric acid percentage, and bagasse humidity has been considered. In fact, using H₂SO₄ + NaCl as the catalyst, significant improvement is observed in the furfural production process and energy consumption.     

Identification of a two-link flexible manipulator using adaptivetime delay neural networks

This paper deals with identification of a two-link flexible manipulator belonging to a class of multi-input, multi-output (MIMO) nonlinear systems, by using adaptive time delay neural networks (ATDNNs). Two neuro-dynamic identifiers are proposed. The capabilities of the proposed structures for representing the nonlinear input-output map of the flexible manipulator are shown analytically. Selection criteria for specifying the fixed structural parameters as well as the adaptation laws for updating the adjustable parameters of the networks are provided. During identification, the two-link flexible manipulator is under nonlinear control and the input-output data sets are generated for different desired trajectories. Simulation results reveal that the proposed neuro-dynamic structures are capable of successfully identifying a highly nonlinear system without any a priori information about the nonlinearities of the system and without any off-line training.     

Thermodynamic modeling of CO2 solubility in ionic liquid ([C n -mim] [Tf2N]; n=2, 4, 6, 8) with using Wong-Sandler mixing rule, Peng-Rabinson equation of state (EOS) and differential evolution (DE) method

Environmental and safety regulations are creating increasing interest in ionic liquids which have been used as alternative solvents for a wide range of industrial applications. Knowing the phase equilibrium of these materials is very important. In this study, the solubility of CO₂ in ionic liquid 1-alkyl-3 methylimidazolium bis (trifluoromethylsulfonyl) imide ([C _n -mim][Tf₂N]; n=2, 4, 6, 8) was probed with the Peng-Robinson (PR) equation of state (EOS) and Wong-Sandler mixing rule and van Laar model for excess Gibbs free energy. The differential evolution (DE) optimization method was applied to optimize the binary interaction parameter and activity coefficients. Moreover, binary interaction parameters and activity coefficients were presented as mathematical correlations that for various materials have depended on temperature. Our results showed that average absolute derivations of our proposed model were less than other existing models, and by using the aforesaid method better prediction could be achieved.     

Thermodynamic modeling of solubilities of various solid compounds in supercritical carbon dioxide: Effects of equations of state and mixing rules

No one can ever deny the significance of calculations of solubilities of industrial solid compounds in supercritical CO₂ in separation processes. In this work, the Peng-Robinson (PR) and the Esmaeilzadeh-Roshanfekr (ER) equations of state (EoS) along with several mixing rules including the Wong-Sandler (WS), the covolume dependent (CVD) and the van der Waals one (VDW1) and two (VDW2) fluid mixing rules are applied to evaluate the solubilities of 52 mostly used solid compounds in supercritical carbon dioxide. Besides, the Van-Laar excess Gibbs energy (G^ex) model is applied in phase behavior calculations by the WS mixing rule. The optimal values of the proposed thermodynamic model parameters are evaluated using the DE (differential evolution) optimization strategy. The absolute average deviations of the model results from 1776 experimental data points and the optimal values of the adjustable parameters of the model are reported to investigate the capabilities of combinations of each equation of state with different mixing rules in calculations of the solubilities. The results indicate that the combination of the ER EoS with the WS mixing rule leads to more accurate results (AAD = 9.0%) compared with other ones.     

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.     

Prediction of asphaltene precipitation by the extended Flory–Huggins model using the modified Esmaeilzadeh–Roshanfekr equation of state

Asphaltene precipitation is caused by a number of factors, such as the variation of pressure and temperature, the change in composition and the mixing of oil with diluting solvents. The deposition of asphaltene precipitation is one of the main problems with the oil industries, appearing in the well bore, the well tubing and the refining processes. This causes an increase in the operating costs and imposes the costs of cleaning and washing well tubing as well. Therefore, it would be economically beneficial to know under what conditions and to what amount the asphaltene precipitates.In this paper, a model is presented based on the Flory–Huggins theory of polymeric solutions. Because the interaction parameter term plays a key role in the asphaltene precipitation, a correlation is proposed to account for the effect of the solvent ratio in addition to molecular weight. Several adjustable parameters in terms of the interaction parameter are determined in this work using a series of experimental precipitation data from a crude oil sample of a field located in the southwest of Iran (oil sample 1), and applying a robust optimization method (the differential evolution). Regarding the influence of the solubility parameter on the accuracy of the final results, a comparison is made between the m-ER, PR and the SRK EOSs. Finally, the obtained results from the comparison between the asphaltene precipitation amounts of various solvents and the existing experimental values for another group of data from oil sample 1, and two other oil samples verify the accuracy of the presented model.