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This study introduces a design of robust finite-time controllers that aims to solve the trajectory tracking of robot manipulators with full-state constraints. The control design is based on the construction of a distributed state constraint non-singular terminal sliding mode (CNTSM). The CNTSM design includes the gain self-adapting tuning method, which can ensure finite-time convergence to the sliding surface aside from the states to its corresponding reference trajectories. The implementation of the time-varying gain ensures the fulfillment of the accurate tracking for the references while the position and velocity constraints are satisfied permanently. A barrier Lyapunov function is proposed to develop the finite-time stability analysis of the designed controllers. The CNTSM realization uses the tracking error as well as its estimated derivative, which is calculated using a variant of adaptive super-twisting algorithm operating as robust differentiator. The proposed CNTSM is numerically evaluated on a two-link RM with uncertain inertia and Coriolis matrices. Simulation and experimental results evidence the efficiency of the CNTSM controller demonstrating a better tracking performance while the full-state constraints are satisfied in counterpart with the classical non-singular terminal sliding mode which is not able to keep such restrictions.

The removal of phenol compounds (such as phenol (Ph), 4-chlorophenol (4-CPh), and 4-phenolsulfonic acid (4-APS)) was studied using catalytic ozonation and cerium oxide (CeO2) as the catalyst. Conve...

This study describes the design, instrumentation and control of an exoskeleton for lower limb children rehabilitation with nine degrees of freedom. Three degrees of freedom in each leg exert the movements of hip, knee and ankle in the sagittal plane, and three control the drive track system composed by a caterpillar-like robot. The control scheme presents a model free decentralized output feedback adaptive high-order sliding mode control to solve the trajectory tracking problem in each degree of freedom of the exoskeleton. A high order sliding mode differentiator estimates the unmeasured states and, by means of a dynamical state extension, it approximates the unknown dynamical model of the exoskeleton. A second-order adaptive sliding mode controller based on the super-twisting algorithm drives the exoskeleton articulations to track the proposed reference trajectories, inducing an ultimate boundedness for the tracking error. Numerical and experimental simulation results demonstrate the effect of the adaptive gain on the super-twisting control design. Such evaluations confirmed the superior tracking performance forced by the adaptive law for the controller with a smaller chattering amplitude and smaller mean tracking error.

A drum boiler is the most critical part of power generation plants. The control of a highly nonlinear, multiple input and multiple output drum boiler system (DBS) is a challenging task. In this wor...

The aim of this brief was to develop a class of state estimator modified with a projection function to reconstruct the non-measurable variables of a state-constraint system. The main characteristic of this observer was that the estimated states remain inside the compact set defined by the given variables constraints, independently of the output noise and internal modeling uncertainties. The application of the projection operator introduced a non-smooth form for the observer design. Hence, integral Lyapunov functions can be used to realize the stability study. The design of a Lyapunov-Krasovskii functional overcome the smoothness problem and justifies the ultimate boundedness of the estimation error. The upper bound for the estimation error is rationally related to the observer’s lag and it is nonlinear associated with the power of output noises and modeling errors. Two numerical examples (the Chua’s circuit and the ozonation of toxic compounds adsorbed in soil) illustrated the application of the suggested observer.

In this study, a local approximated solution for the Hamilton–Jacobi–Bellman equation based on differential neural networks is proposed. The approximated Value function is used to obtain a feedback control law for a class of uncertain dynamical systems. The approach to deal with the uncertainties of the dynamical system is a min–max method that yields obtaining a robust-like solution for an optimal control problem. The proposed cost functional is presented in the Bolza form and the necessary and sufficient conditions for getting the min–max optimal solution with the designed robust version of the dynamic programming approach are provided. Moreover, the effect of the neural network approximation is studied. All the analysis considers a smoothness assumption regarding the Value function. The practical stability of the system with the neural network feedback optimal control is formally demonstrated by means of the Lyapunov method. Finally, the performance of the control law is compared in simulation with a classical optimal controller. The proposed controller overcomes the results produced by the classical controller, which is confirmed by the functional evaluation.

The design of a state feedback and robust controller for regulating the tridimensional (3D) movement of autonomous underwater mobile crafts (AUMCs). The controller design is based on the applicatio...

The aim of this study was to design a first-order sliding mode controller for dual AC/DC/AC power converters to regulate the production of a corona discharge ozone generator. The design is presented in the two following parts: (a) The first part regulates a three-branch (phase) AC/DC converter to control the DC intermedium voltage that is required to produce the desired ozone concentration, while (b) the second part regulates a one-phase DC/AC power conversion circuit that regulates amplitude of the AC voltage that must be injected into the reaction chamber of the ozone generator. For the first section, the controller regulates the switching sequence yielding the control of the input currents, which defines the amplitude of the DC voltage. In the second, the switching control regulates the amplitude of the expected output AC signal, which depends on the obtained DC voltage from the AC/DC section. This structure represents a novel form to adjust the functioning of the AC/DC/AC power device, which has not been used before in the context of ozone production. Lyapunov’s second method was the main tool for confirming the existence of an asymptotical equilibrium state point for the trajectory tracking deviation for both sections of the power converter. The voltage amplitude was tested on a previously validated mathematical model of a corona-effect ozone generator performance. Several numerical evaluations illustrated the efficient performance of the suggested discontinuous controller and considering the proposed switching sequence that realizes the control action. The proposed controller was contrasted with a usual proportional/integral/derivative (extended state) design, which is a common technique to regulate the ozone generation system.

The aim of this study was to obtain a feasible mathematical model that may describe the lactic acid production by Lactobacillus casei using residual agro-industrial wastes. The model includes a delay term which characterizes the lagged biomass growth by the acclimation period induced by the considered complex substrate. A novel parametric identification algorithm leads to the characterization of the time delay as well as the kinetic parameters. The application of the estimated parameters in the proposed model defined the behavior of biomass growth, substrate consumption and lactic acid accumulation. A combination of robust exact differentiators (based on the sliding mode algorithm named super twisting) and a time-delay least mean square identification algorithm provides the on-line calculus of the required parameters. The identification algorithm was tested with the experimental data obtained from a bacterial culture system containing all the required nutritional sources to complete the target carbon to nitrogen ratio of 4.13. This value has been characterized as an adequate ratio to promote the efficient production of lactic acid. The proposed model reproduced the experimental data with correlation factor of 0.98 in average for the evaluated experimental conditions. The identified parameters agreed with the reported results in similar studies showing the effectiveness of the identification method as well as the possibility of using non-conventional substrates (milk whey and vegetable residues) to produce lactic acid.

The main goal of this study is developing an adaptive controller which can solve the trajectory tracking for a class of quadcopter unmanned aerial system (UAS), namely a quadrotor. The control design introduces a new paradigm for adaptive controllers based on the implementation of a set of differential neural networks (DNNs) in the consequence section of a Takagi–Sugeno (T–S) fuzzy inference system. This dynamic fuzzy inference structure was used to approximate the UAS description. The particular form of interaction between neural networks and fuzzy inference systems proposed in the present work received the name of dynamic neural fuzzy system (DNFS). An adaptive controller based on this DNFS form was the main solution attained in this study. This DNFS controller was focused on the estimation and compensation of the uncertain section of the Quadrotor dynamics and then, forced the UAS to perform a hover flight while the tracking of desired angular positions succeeded, which results in tracking a desired trajectory in the X-Y plane. The control design methodology supported on the Lyapunov stability theory guaranteed ultimate boundedness of the estimation and tracking errors simultaneously. Several experimental tests in an outdoor environment by using a real Quadrotor platform was performed by using an RTK-GPS (Real Time Kinematic) system to determine the position of the vehicle in the X-Y plane. The experimental results confirmed the superior performance of the proposed algorithm based on the combination of DNNs and T–S techniques with respect to classical robust controllers.

The aim of this work is to design a robust output-based controller for a robotic manipulator (RM) with a master–slave configuration (MSC). The trajectories of the slave robot manipulator (SRM) must fulfil two types of constraints. The first corresponds to the boundaries of the obstacles placed in the given workspace. The second is related to the boundaries of the workspace. The controller must ensure the collision avoidance of the obstacles and the end-effector of the SRM should remain inside of the workspace. The proposed controller satisfies a state feedback structure with time-varying gains. The time-variable gains include an integral compensation that guarantees the collision avoidance with the obstacle satisfying the boundary of the workspace, which is the main contribution of this study. The solution of a matrix inequality characterises the zone of convergence and the suboptimal control gains. A set of numerical simulations using a MSC based on a couple of two-link RMs illustrates the advantages obtained with the proposed method. Also, a set of experimental results demonstrate the effectiveness of the proposed controller.

This paper proves the ultimately boundedness analysis for the trajectory tracking problem between the states of an uncertain nonlinear system represented by a Takagi–Sugeno (T–S) system and a given...

This manuscript deals with the problem of controlling a virtualized humanoid robot with 16 degrees of freedom (DOF). The aim of this study was to design an output feedback discontinuous controller ...

Enhancing the productivity of biomass, carbohydrates, proteins, and lipids in microalgae cultures is an essential task to justify the feasibility of using biofuels as energetic alternatives to fossil fuels. Diverse strategies can be used to get a biologically plausible optimization of the microalgae culture such as using indigenous microalgae added with other microorganisms such as fungi or bacteria, analyzing the effect of culture broth composition and reaction experimental conditions. A wastewater isolated co-culture microalgae-yeast (CCMY) was evaluated as potential biomass and lipids-efficient producer. The Box-Behnken experimental design and the response surface methodology yield to identify the best attainable composition of inorganic nutrient variables in culture media: sodium nitrate (NaNO3), potassium phosphate (K2HPO4), and ferrous sulfate (FeSO4). The results of the experimental design revealed that the NaNO3, K2HPO4, and FeSO4 were three factors significantly influencing the biochemical content. The detected optimal conditions correspond to a highest biomass production of 1.68 g L−1; the carbohydrate content was 16.75%, for proteins 37.11%, and the maximum lipid content was 27.77%. These values were about two times higher than the results obtained with the original version of the culture broth (BBM). The complexity of inorganic salts effect on the biomass and lipid accumulation motivates the proposal of a quartic order mathematical model which characterizes the productivity of microalgae-yeast co-culture as function of the inorganic micronutrient contents.

Two-stage process of dark fermentation (DF) and photo fermentation (PF), using fruit and vegetable waste (FVW) and cheese whey powder (CWP), was used as an approach to enhance the hydrogen (H2) production. FVW and CWP at C/N ratios of 34, 39, 60, 71 and 82 were tested as substrates for DF. Dilution (1:2, 1:5, 1:10) of the DF effluents was used as a coupling strategy. DF effluents with low-butyrate and high lactate concentrations were obtained as a function of an increased C/N ratio, which results in high H2 production during the PF. Maximum overall H2 yields of 793.7 and 695.4 mLH2/gChemical Oxygen Demand (COD) were obtained using a 1:10 dilution, at a C/N ratio of 60 and 70, respectively. These H2 yields were higher than those obtained with the individual processes. The C/N ratio at the DF stage regulate not only H2 production but also the distribution and concentrations of by-products. These metabolites, in turn, control the H2 production during the PF. Predominant microbial population for both processes (DF: C/N = 34 Acetobacter lovaniensis, Clostridium butyricum; C/N = 39 C. butyricum, Enterobacter sp, Bifidobacterium; C/N = 82 Lactobacillus casei; PF: Rhodopseudomonas palustris) were in accordance with the final metabolic products.

The aim of this study is to provide a class of state observers, based on differential neural networks, to approximate a class of advanced oxidation systems, based on the application of ozone high oxidant power and catalyst (the named catalytic ozonation). The study considers the design of a state observer for uncertain systems with the restrictions of the ozonation system, including the positivity of the states, as well as the control action. The observer includes a projection operator which is motivated by the state constraints. The learning laws of the proposed differential neural networks are obtained using a class of controlled state restricted Lyapunov functions. The detailed stability analysis proves the input to state stability with respect to the modeling error, as well as the bounded uncertainties of the ozonation system. The experimental confirmation of the state estimation is also presented. The experimental case considers the ozonation of a toxic organic contaminant (therephtalic acid) which is a regular pollutant of the plastic industry wastewater.

Grid-connected converters are an important class of power electronic systems. Many power and energy applications require grid-connected converters. To control grid-connected converters, precise information of the grid voltage frequency and phase are required. Gradient estimators can be very useful in this regard. They are suitable to estimate the frequency and phase of the grid voltage signal. Various gradient estimators are already available in the literature e.g. regression-based techniques. However, most of them are designed by using the instantaneous estimation error as the cost-function. This amplifies the effect of noise in the estimated parameters. To overcome this issue, an integral cost-function is considered in this paper. The integral cost-function tries to minimize the estimation error over the integration window leading to reduce the effect of noise in the estimated frequency and phase. Moreover, the cost-function uses tunable forgetting factor to give more importance to recent data. The proposed gradient estimator assumes the grid frequency to be constant. However, in practice the frequency is variable with known nominal value. To overcome this problem, a frequency estimation block is coupled with the gradient estimator. The frequency estimation block uses the idea of phase-based frequency estimation. Comparative numerical simulation and experimental studies are performed to demonstrate the suitability of the proposed technique over three other advanced techniques from the literature.

International audience; This study is devoted to the design of an adaptive discontinuous control based on differential neural networks (DNNs) for a class of uncertain homogeneous systems. The control is based on the universal approximation properties of artificial neural networks (ANNs) applied on a certain class of homogeneous nonlinear functions. The adaptation laws for the DNNs parameters are obtained with the application of the Lyapunov stability theory and the homogeneity properties of the approximated nonlinear system. The stability analysis of the closed loop system with the proposed controller is presented. The estimation error in the approximation of the uncertain homogeneous functions is considered in the stability analysis. The performance of the controller is illustrated by means of a numerical simulation of a homogeneous model.

A DC voltage source and a DC/DC power converter can be used to control the position, speed, or torque of a DC motor. In such operational conditions, a rectifier is needed to use a DC voltage source if only a three-phase voltage source is available. The objective of this study is to replace a rectifier and DC/DC power converter by one AC/DC power converter, such that its output would be equal to the voltage needed to control a DC motor. It is assumed that the control algorithm of a DC motor is selected, which means that the desired output voltage of the AC/DC converter as a time function or function of the motor state is known. First, a sliding mode methodology is applied to control the converter’s three shoulders to make the three-phase input current track the source voltages multiplied by a time-varying gain. The gain is then selected such that the converter output voltage is equal to the desired input of the DC motor. It is shown that this condition holds if the time-varying gain satisfies a first-order differential equation, which can be implemented as part of the controller. The application of Lyapunov theory confirms that the speed regulation process has a stable equilibrium point at the origin and that the time gain variation is bounded. The power efficiency is equal to one if the gain is positive. A numerical simulation demonstrates application of the developed control methodology for both constant and time-varying angular speed reference inputs.

The current study presents the treatment of water contaminated with the dye Direct Red 28 in the presence of Na2SO4 at three different concentrations (10, 40 and 80 g/L) by simple ozonation. This w...

This article summarizes the comprehensive design of an adaptive control that solves the trajectory tracking of a four-rotor unmanned aerial vehicle supplied by an own-designed grasping device. The controller design uses the regular proportional-derivative (PD) structure which is common to solve regulation, as well as tracking problems for robotic systems. The PD structure needs the estimation of the time-derivative to be exerted on-line. The super-twisting algorithm served to operate as a decentralized estimator of the derivatives for the tracking errors. The controller gains were adjusted by the differential laws aimed to track attainable reference trajectories. The adaptive strategy adjusts the controller gains enforcing the convergence of both the estimation and the tracking errors. A numerical example showed the observer/controller performance. A comparison with a classic non-adaptive PD controller confirms the effectiveness on the tracking task by the proposed design. In addition, a set of virtual numerical evaluations using the parameters of a real quadcopter system confirmed the performance benefits of the adaptive controller based on the PD structure aided with the estimation of the velocity obtained by the observer.

The document search function in the quality assurance unit's information system uses the Knuth-Morris-Pratt algorithm. This KMP algorithm is a search algorithm that will match the pattern or arrangement of words to be searched from left to right at the beginning of the text and then shift the order of words until the word order is at the end of the text. The KMP algorithm has the advantage of matching matches on large files. The system development method used is the Rapid Application Development method. This RAD method is a life cycle strategy aimed at providing development that is much faster and gets results with better quality compared to results achieved through traditional cycles. Application of School Administration Information System is expected to be able to overcome problems arising from conventional document control processes to make it easier and more precise. Abstrak: Fungsi pencarian dokumen pada sistem informasi unit jaminan mutu menggunakan algoritma Knuth-Morris-Pratt. Algoritma KMP ini merupakan algoritma pencarian yang akan mencocokkan pattern atau susunan kata yang akan dicari dari kiri ke kanan pada awal teks dan kemudian menggeser susunan kata sampai susunan kata tersebut berada di ujung teks. Algoritma KMP memiliki keunggulan pencarian kecocokan pada file yang berukuran besar. Metode pengembangan sistem yang digunakan yaitu metode Rapid Application Development. Metode RAD ini adalah strategi siklus hidup yang ditujukan untuk menyediakan pengembangan yang jauh lebih cepat dan mendapatkan hasil dengan kualitas yang lebih baik dibandingkan dengan hasil yang dicapai melalui siklus tradisional. Aplikasi Sistem Informasi Administrasi Sekolah ini diharapkan dapat mengatasi permasalahan yang timbul dari proses pengendalian dokumen secara konvensional agar lebih mudah dan tepat.

This study aimed to develop a semi non-parametric model of the die casting process of aluminum alloys. This model uses a hierarchical artificial neural network (HANN), with a structure motivated by the relationships of the metals which define the characteristics of the aluminum alloy. These settings depend on the content of seven metals (Sn, Zn, Mn, Cu, Si, Ni, and Mg). The relation between these metals and the alloy characteristics oriented the HANN structure. A distributed back-propagation learning modified with the Levenberg-Marquardt method served to adjust the HANN weights. Two complementary validation methods justified the application of this novel hybrid non-parametric modelling structure. The training set came from standards composition proposed by different international organizations. A set of real aluminum alloys and the experimental results describing their characteristics formed the validation test. An average accuracy value of 3.65% confirmed the ability of the HANN to reproduce the relation between the metal content and the alloy characteristics. These values confirmed how the oriented HANN may predict the aluminum alloy characteristics as function of the metal distribution. This result offers a different alternative to the prediction of aluminum alloy properties using the metal composition as input information.

The aim of this study is to propose the output controller to solve the execution of the gait sequence for Gecko Bio-inspired Robotic Devices (GBRDs). The Twisting Controller (TC) serves as basis of the tracking trajectory designed algorithm. The TC uses both the tracking error and its estimated derivative, which is calculated by a set of distributed Super-Twisting Algorithms (STAs). Each STA is implemented as a robust and exact differentiator. The output-based controller structure corresponds to a sort of decentralized form for robotic devices. Consequently, each articulation is controlled by an independent TC. A set of proposed references trajectories reproduce the gait cycle of a Real Gecko-Lizard (RG-L). The reference trajectories are proposed as the superposition of sigmoid functions ful-filling the conditions of the Bezzier polynomials. Numerical simulations evaluate the GBRD movement enforced by the suggested controller in the horizontal as well as the vertical gaits. An own 3D printed GBRD is the experimental platform aimed to test the distributed controller. Two controlled vacuum pumps are used to adhere the GBRD to the wall surfaces. A set of experimental validations confirm the robustness and the reliability of the proposed controller when its performance is compared with classical output feedback controllers.

The non-parametric identification problem aims to estimate a suitable model based on the response produced by a given stimulus on an uncertain model. Complementary, input estimation considers a different problem where the model and the output are known. However, if neither model nor input is known, the identification problem seems to be more complicated. This is a major challenge in electrophysiological systems where the output signal can be measured, but the biological system is uncertain and the stimuli are unknown. The aim of this study was to introduce a novel methodology that may estimate the uncertain input in this more complex situation where the biological model of the system under analysis is uncertain and the input stimuli must be estimated accurately. A two step non-parametric identification scheme based on differential neural networks (DifNN) and the second order super-twisting sliding mode algorithm (STA) identified the input stimulus. The STA makes a robust exact estimation of the output signal. The DifNN produces an internal non-parametric mathematical modeling of the input-output relationship executed without the input information. Learning laws for both levels of DifNN modeling were presented as part of the uncertain input identification process. The estimation of uncertain visual stimulus applied over the retina in an ex-vivo avian model (EVAM) served to test the method proposed in this study. The output information corresponded to the electrophysiological voltage variation in the optical nerve.

In this paper, an adaptive disturbance estimation-based control of a class of uncertain feedback linearizable systems with the presence of, both, external perturbations as well as non-modeled dynamics is considered. The aim of the control design was to solve the tracking trajectory problem for a class of output-based linearizable uncertain systems. An adaptive scheme is proposed for developing a state estimator of the uncertain dynamics. The estimation of both, the states and the uncertain dynamics is attained despite the limited knowledge of the plant and the information contained in the output signal. The uncertain section in the linearized system was approximated by a class of time-dependent combination of the system states. The observer implemented a parametric identifier to obtain the time varying parameters associated to the estimation of the uncertain section. This method ensured the adequate estimation process of the uncertainties/perturbations, measured in terms of the mean square error. Simultaneously, an adaptive gain associated to the observer adjusts its trajectories to provide the ultimate boundedness of the estimation error. Once the states of the uncertain system are obtained, a feedback controller rejects actively the perturbations that affect the system by a compensation scheme. Two numerical examples were developed to show the observer-based control performance.

In this work, the degradation of terephthalic acid (TA) by vanadium oxide (VxOy) supported on zinc oxide (ZnO) was evaluated in a photocatalytic ozonation treatment based on two UV-A LEDs distribut...

The aim of this study was to develop an adaptive state estimator with discontinuous parameter adjustment law for the catalytic ozonation system. A nonlinear transformation defined an equivalent system presented in chain-of-integrators form with uncertain structure in the dynamics of the last state. A step-by-step state estimator using a sequence of super-twisting algorithms (STAs) estimated the unmeasured states of the uncertain system. A class of differential neural network (DNN) with discontinuous learning law served to estimate the uncertain section of the catalytic process. The learning method was developed by implementing a strong lower-semi-continuous Lyapunov function. The method used to generate the laws that adjusted the weights, also yields the estimation of the parameters included in the catalytic ozonation system. A set of numerical simulations demonstrated the application of the DNN-based state observer to solve the estimation of the non-measurable information in the catalytic ozonation system. The available output signal was the concentration of the ozone gas at the output of the reactor. This was the only information used by the observer. The state estimator with discontinuous learning laws was also evaluated with experimental information obtained by a catalytic ozonation system using NiO as catalyst and phtalic acid as model contaminant. The effect of aggregating the DNN in the observer structure was compared with the observer using only the sequence of STA. The superior performance of the observer developed in this study was confirmed by evaluating the mean square error of the identification error.

This manuscript introduces a novel methodology to solve the state estimation of discrete-time multi-input multi-output (MIMO) nonlinear systems with uncertain dynamics. The mathematical model of the nonlinear systems considered in this paper satisfies the usual Lagrangian structure that characterizes many mechanical, electrical or electromechanical systems. A recurrent neural network (RNN) estimates the uncertain dynamics of the MIMO system with an updating law based on a particular variant of the discrete-time version of the super-twisting algorithm (DSTA). A Lyapunov stability analysis defines the convergence zone for the state estimation error throughout the solution of a matrix inequality. The convergence zone for the estimation is smaller when the DSTA and the RNN work together in an observer. Numerical examples demonstrate how the adaptive observer reduces the zone of convergence and the oscillations in the steady state compared with a discrete version of the STA with additional linear correcting terms. An experimental implementation shows how the observer estimates the unknown states of a Van Der Pol Oscillator. A comparison against some variations of the DSTA justifies the advantages of the mixed DSTA-RNN observer.

Artificial neural networks (ANNs) are considered efficient tools for modeling complex, non-linear processes with uncertain dynamic models. ANNs were originally applied as effective predictors of diverse processes with static dependence on the input-output information. However, when the ANN must be applied to characterize an approximate model of time-dependent input-output relationships, then it is necessary to introduce the time effect as part of the ANN, yielding to the construction of dynamic ANN or DNN. This review establishes the variants of recurrent and differential forms of DNN, their mathematically formulation as well as the methods to adjust the network weights. The characteristics of DNNs motivate their use to represent the dynamics of decontamination processes. This review details recent findings on the DNN application for the modeling and control of treatment systems based on either biological and chemical processes. The modeling application of DNN for some common methods used in the treatment of wastewater, contaminated soil and atmosphere is described. The major benefits of using the approximate DNN-based model instead of designing the complex mathematical description for each treatment are analyzed in the context of enhancing the efficiency of the decontamination treatment. This review also highlights the remarkable efficiency of DNNs as a keystone tool for modeling and control sequence of treatments. The last section in the review introduces several open researching areas for the application of DNN for decontamination systems based on biochemical and chemical treatments.

In this paper, a non parametric identifier for homogeneous nonlinear systems affine in the input is proposed. The identification algorithm is based on the neural networks using sigmoidal activation functions. The learning algorithm is derived by means of Lyapunov function method and homogeneity theory. A numerical example demonstrates the performance of the proposed identifier.

The aim of this study was to design a step-by-step numerical differentiator for a class of discretized version of a n-th chain-of-integrator system based on the application of the implicit discretization of the super-twisting algorithm (STA). The high-order differentiator reconstructed the non-measurable states of an input-output linearizable systems. The gains of the observer were calculated based on the application of a class of generalized functions. The convergence analysis showed that all the states in the linearized system achieved an invariant set with a predefined size. The existence of this invariant set depends on the uniqueness of the solution for the generalized function. A simple interpretation of a possible exact reconstruction of the non-measurable states of the implicit discretized form. The application of the implicit discretized STA served to design a class of observer for the microalgae culture system.

International audience; The aim of this study is to design a non-parametric identifier for homogeneous systems based on a class of artificial neural networks with continuous dynamics. The identification algorithm is developed for input-affine systems with uncertain gains and diverse degrees of homogeneity. One of the main contributions of this study is the extension of the universal approximation property of neural networks for continuous homogeneous systems. Another contribution is the development of a differential non-parametric identifier based on the novel concept of homogeneous neural networks. The adjustment laws for the weights are obtained from a Lyapunov stability analysis taking homogeneity properties of the system into account. The ultimate boundedness of the origin for the identification error is demonstrated using the persistent excitation condition. The effectiveness of the proposed identifier is verified by the simulation of the three-tank homogeneous model. In this example, the proposed identification scheme is compared with a classical ANN identifier, and we present a statistical analysis of such comparison. It is shown in simulations that the identification error of the proposed homogeneous algorithm has faster convergence and less oscillations.

This study evaluates naproxen (NP) degradation efficiency by ozonation using nickel oxide films (NiO(F)NiO(F)) as a catalyst. The NiO films were synthesized by chemical vapor deposition and characterized by X-ray diffraction, scanning electron microscopy, atomic force microscopy and X-ray photoelectron spectroscopy. NP degradation was conducted for 5 min using 10 films of NiO(F) comparing against ozonation using 100 mg/L NiO powder in suspension (NiO(s)NiO(S)) and conventional ozonation (O3-conv). Total organic carbon analysis demonstrated a mineralization degree of 12% with O3O3-conv, 35% with NiONiO as powder and 22% with NiO(F)NiO(F) after 60 min of reaction. The films of NiO(F)NiO(F) were sequentially used 4 times in ozonation demonstrating the stability of the synthesized material, as well as its properties as a catalyst for ozonation. A proposed modeling strategy using robust parametric identification techniques allows the comparison of NP decomposition pseudo-monomolecular reaction rates.

Unsupervised learning is a target free methodology to classify unorganized information. This study proposes a new unsupervised learning method for classifying unlabeled targets based on convex ellipsoidal sets. The method described here uses ellipsoidal calculus tools to realize pattern classification via a clustering scheme. The algorithm consisted in adjusting the number of clusters defined by an ellipsoidal set as well as their centers, shape forms and orientation. All these processes were realized without preliminary information on the data distribution. The application of an inner gradient descent algorithm permitted the adjustment of these parameters, using the standard deviation of data in the cluster with respect to the semi-axis of ellipsoid that contains the corresponding data. A specific evaluation of the proposed algorithm used two-dimensional attributes databases. The outcomes of the classification results were compared with those produced by the K-means and DBSCAN algorithm. Comparable classification results were achieved overcoming some drawbacks of the methods.

The aim of this study is to design a robust controller for an autonomous underwater vehicle (AUV) based on the application of the extended Integral Sliding Mode (ISM) method. The control problem is reformulated here as an extremum seeking control problem realization for solving the tracking of attainable reference trajectories. The states of the uncertain dynamic corresponding to the AUV dynamics track the references restricted to the x-z plane. The dynamic nature of the AUV induces the application of the ISM extended version design based on the implementation of a sub-gradient strategy to perform the on-line optimization for the tracking error norm. The proposed control permits to minimize the tracking error without a complete knowledge of the AUV dynamics. The trajectory tracking results of the designed controller are compared with the corresponding outcomes enforced by a state feedback and twisting controllers . The proposed controller exhibits better tracking and smaller control magnitude than the state feedback and twisting form. These two results confirm the benefits of introducing the mixed extended strategy, including ISM and dual averaged extremum seeking control.

This manuscript presents an algorithm to classify continuously electroencephalographic (EEG) signals based on deep differential neural networks (DDNNs). The learning laws are obtained by the second stability method of Lyapunov that requires the solution of a set of matrix differential equations. The robustness of this technique allows the analysis and classification of bio-signals like EEG signals. The EEG signals have complex dynamics and they are strongly affected by noises in the measurements and a high degree of variability between different studies in patients. The main strength of DDNNs is their feedback property, which allows them to work with the time-dependent variation of the EEG signals. The DDNNs are tested in a database constituted of EEG signals acquired from a study made in ten volunteers. The study consisted of the acquisition of EEG measurements of the volunteers recognizing geometrical figures appearing in a graphic user interface. The DDNN obtained better performance than a single layer differential neural network and a convolutional neural network.

The aim of this article is to investigate the trajectory tracking problem of systems with uncertain models and state restrictions using differential neural networks (DNNs). The adaptive control design considers the design of a nonparametric identifier based on a class of continuous artificial neural networks (ANNs). The design of adaptive controllers used the estimated weights on the identifier structure yielding a compensating structure and a linear correction element on the tracking error. The stability of both the identification and tracking errors, considering the DNN, uses a barrier Lyapunov function (BLF) that grow to infinity whenever its arguments approach some finite limits for the state satisfying some predefined ellipsoid bounds. The analysis guarantees the semi-globally uniformly ultimately bounded (SGUUB) solution for the tracking error, which implies the achievement of an invariant set. The suggested controller produces closed-loop bounded signals. This article also presents the comparison between the tracking states forced by the adaptive controller estimated with the DNN based on BLF and quadratic Lyapunov functions as well. The effectiveness of the proposal is demonstrated with a numerical example and an implementation in a real plant (mass-spring system). This comparison confirmed the superiority of the suggested controller based on the BLF using the estimates of the upper bounds for the system states.

The aim of this study was to design and evaluate a prototype of a snake-like endoscopic manipulator robot (SLEMR) and its corresponding automatic controller based on the first order sliding mode theory. The SLEMR was controlled with a set of actuators made of shape memory alloys (SMA). The SLEMR device was constructed with a sequential arrangement of links interconnected by a two degree-of-freedom joint. A parallel agonist–antagonist configuration of actuators was implemented to move each joint. The physical relation between temperature and elongation in SMA forced the execution of the movement in the joint. Elongation–temperature model of the SMA actuator served to get a feasible bound of velocity for each joint. Each pair of SMA actuators was controlled by a first order sliding mode controller. This control design solved the tracking trajectory problem for each joint in the SLEMR because of its robustness against uncertainties and external perturbations. The control action was projected into a feasible implementable set of pulse-width modulated signals which was used to regulate the temperature of the corresponding SMA actuator. The controller designed in this study was experimentally validated in a SLEMR made up by a tridimensional printing technique. The control strategy induced the successful trajectory tracking for all the joints in the SLEMR simultaneously. This characteristic of the control design also enforces the tracking of a reference position by the tip of the final link of the SLEMR. An image acquisition system was used to determine the position of the final actuator in the SLEMR. The effectiveness of the controller proposed in this study was confirmed by the evaluation of the tracking error of the final actuator which approached to a bounded region (less than 1.0 mm) near the origin in a finite-time (0.5 s).

The aim of this study was to design an automatic classifier for electroencephalographic information (EEGI) registered in evoked potentials experiments. The classifier used a parallel associative memory based on recurrent neural networks (RNNs). Each RNN was trained to classify signals belonging to an individual class. A recurrent method based on the application of Lyapunov controlled functions served to design the training procedure of each RNN in the classifier. A parallel structure of RNN with fixed weights (obtained after training process) performed the validation stage. This structure formed a classifier assemble. The selected class assigned to a new segment of EEGI signal is estimated by the minimum value of the least mean square error among the RNNs forming the assemble. The generalization-regularization and a k-fold cross validation ( $$k=5$$ ) were the validation methods evaluating the classifier efficiency. The confusion matrix method justified the application of the classification method introduced in this study. The EEGI obtained from two different annotated databases served to test the classifier based on RNNs. The first database contained signals divided in five different classes and collected from patients suffering from epilepsy. The second database has 90 signals divided in three classes that corresponded to EEGI signals corresponding to 3 different visual evoked potentials. The pattern classifier achieved a maximum correct classification percentage of 97.2% using the information of both databases. This value prevailed over results reported in similar studies using the first database. In comparison with other pattern recognition algorithms, the proposed RNNs based classifier attained similar or even better correct classification results.

This article deals with the control of a class of robotic systems with constant input time delay through the Active Disturbance Rejection paradigm, by means of Generalized Proportional Integral observers. The system is represented as a linear perturbed system, whose lumped disturbance input is estimated and then compensated by an Extended State Observer-Based Control, which is implemented on a predictor scheme for the time delay compensation. A complete Lyapunov Krasovskii functional was used to perform the stability analysis, leading to an ultimate bound trajectory tracking error. The proposal is tested and validated on a two degrees of freedom robotic manipulator.

This study focuses in the output feedback stabilisation of constrained linear systems affected by uncertainties and noisy output measurements. The system states are restricted inside a given polyto...

In this paper, a differential neural network (DNN) implemented as a robust observer estimates the dynamics of perturbed uncertain nonlinear systems affected by exogenous unknown inputs. In the first stage, the identification error converges into a neighborhood around the origin. Then, the second-order sliding mode supertwisting algorithm implemented as a robust exact differentiator reconstructed the unknown inputs. The approach proposed in this paper can be applied in the case of full access to the state vector (identification problem) and in the case of partial access to the state vector (estimation problem). In the second case, the nonlinear system under study must have well-defined full relative degree with respect to the unknown input. Numerical examples showed the effectiveness of the proposed algorithm. The first example tested the DNN working as an identifier into a mathematical model describing the dynamics of a spatial minisatellite. The second example (with a DNN implemented as an observer) tested the methodology of this paper over a single link flexible robot manipulator represented in a canonical (Brunovsky) form. In both examples, the mathematical models served as data generators in the testing of the neural networks. Even when not exact mathematical description of both models was used in the input estimation, the accuracy obtained with the DNN is comparable with the case of applying a high-order differentiator with complete knowledge of the plant.

The recent use of dry baths allows the separation of urine from wastewaters with no dilution and without mixing with human faecal material nor detergents, etc. Human urine can be used as culture medium for microalgae because of its high nitrogen and phosphorus concentrations. Nevertheless, a culture medium to grow microalgae requires some other compounds with less concentration including some metals. We assessed the effect of adding Fe, Co, Mo and Zn at specific concentrations, defined by a Box–Behnken experiment design, as well as the effect of metal concentrations on biomass growth and lipid accumulation. The levels of each variable were selected considering the concentration of metals in the Bold Basal Medium (BBM), twice this value and half of that concentration. Human urine supplemented with two metals (Fe and Zn or Fe and Co) was a feasible alternative to the BBM medium. Higher biomass concentrations, lipid production and, hence, lipid quota were obtained with the selected alternative medium. Similar conditions were obtained for the three strains evaluated in this study. In Chlorella vulgaris, an increase of biomass yield (23%) was obtained with the medium designed in this study but lipid production never exceeded the one obtained with the reference medium. Biomass yields (66 and 59%) and lipids production (97 and 23%) were simultaneously increased in Scenedesmus sp. and Chlorococcum humicola. In C. vulgaris, Fe and Co positively affected growth. Scenedesmus, was affected negatively by Co, Mo, and Zn. C. humicola was affected positively by Zn and negatively by Fe. It was demonstrated that it is feasible to replace complex media like BBM by a formulation consisting of a urine dilution supplemented with only two metals.

This work reports the study of the decomposition of three PAHs (anthracene, phenanthrene, and fluorene) in aqueous solution at different pHs (2, 7, and 11) and in the presence of methanol as a co-s...

This study designed a suboptimal output control strategy to characterise an attractive and invariant set for the state trajectories of perturbed linear systems with noisy measurements and state constraints. The state constraints were defined by a given polytope formed of by n-dimensional vectors. An adaptive linear controller enforced the existence of an attractive and invariant set (centred at the origin) for the trajectories of the perturbed system. A barrier Lyapunov function (BLF) and the attractive ellipsoid method (AEM) derived the adjustment law of the adaptive gain. The controller design used the linear matrix inequality technique to solve two optimisation problems. The first solution provided a maximal set where the initial conditions must belong without violating the state constraints. The second optimisation solution characterised the invariant minimal attractive set for the system trajectories. An academic example verified how the proposed adaptive control generated the system trajectories that converged to the minimal attractive ellipsoid while keeping them inside the polytope defining the state constraints. The simulation script showed the advantages of the adaptive BLF controller (ABLC) against classical AEM controller. A second numerical example considered a direct current motor showing the advantages of the ABLC against the sliding mode technique.

The aim of this study was to develop a prototype of an orthotic system that can be used as a support tool in the rehabilitation of the upper limb. The construction of this device was motivated by the increasing number of subjects suffering from full or partial loss of the upper limb function as a consequence of spinal cord injuries, strokes, occupational syndromes and sports injuries. The majority of procedures used in upper limb rehabilitation consist of repetitive movements enforced by physiotherapists; a robotic device executing the same tasks seems to be a plausible solution if the orthosis can be programmed and controlled automatically. This study reports the mechanical design, electronic instrumentation and automatic control of an upper limb orthosis made of plastic polymer that makes the orthosis a wearable and self-carrying device. The orthosis consisted of a mechatronic device with five joints. The pieces made by a three-dimensional plastic printer were used to construct the device leading to a total weight of 2.6 kg. The application of a robust automatic controller based on the sliding-mode theory forces the movement of the arm, while taking into account the constraints in each angular displacement of the orthosis. A set of reference trajectories designed to represent the usual movements of a healthy upper limb served for evaluating the controller execution. The orthosis was tested on 15 volunteers with a maximum experimental steady-state error of 2% in the angular deviation of all articulations with respect to their reference trajectories.

This study deals with the design of an automatic controller to solve the problem of trajectory tracking for collaborative robot manipulators. The control algorithm corresponds to an adaptive proportional derivative controller (APDC) aimed to reduce the needed energy to execute the tracking of some predefined reference trajectories. The controller was designed with the aim of keeping a constant distance between the final effectors of both manipulators. The derivative estimation used in the APDC was obtained by a set of robust exact differentiators based on the application of the super-twisting algorithm (STA). The controller was tested on two real cooperative multi-articulated robotic manipulators system. The experimental setup consisted of two robot manipulators designed with four degrees of freedom each one constructed by the 3D printing technology. The experimental results included comparisons with respect to the classical non-adaptive proportional-derivative controllers. The superior performance of the adaptive controller proposed in this study was confirmed by the estimation of the least mean square analysis of the tracking error.

The aim of this study was to design a discontinuous controller with adaptive gains, based on the sliding-mode theory. The adaptive controller solved the trajectory tracking problem for a class of systems with state constraints, specifically for a class of multi-articulated robot manipulator with a constraint work-space. The controller implemented a state dependent adaptive gain that grows in value when the trajectories approach the boundary of the set that characterized the states constraints. The design of the adaptive gain guaranteed the existence of a finite-time stable equilibrium point for a special sliding surface depending on the tracking trajectory error. The specific law used to adjust the gain was obtained by a Lyapunov-based stability analysis. Some numerical simulations proved that the sliding-mode controller showed a superior performance, measured by the mean square value of the tracking result, compared to the non-adaptive gain first order sliding-mode controller.

This paper presents the development of a hybrid model that describes the temperature, elongation and inner force relationships in a spring Shape Memory Alloy (SMA) actuator. The temperature-inner force relationship obeys a hybrid structure in which a sigmoid function correlates the variation of temperature for the SMA and the force executed by the spring actuator. The hybrid nature of the model describes the regular hysteresis behavior of the SMA. The switching law of the hybrid model depends on the time derivative of the temperature. A multivariable model depending on temperature, and inner and external forces was developed in order to characterize the Shape Memory Effect (SME). A set of experiments was carried out to obtain the parameters used to characterize the model. The application of the Levenberg-Marquardt method resulted in the parametric estimation procedure. An averaged correlation factor of 0.95 between the model response and experimental results justifies the proposed modeling approach.