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System identification is the field of systems mathematical modeling from experimental data. In the modeling chain, experiments realization, model structure selection and parameter estimation are essential steps. However, performing tests for system identification is time-consuming and not always possible. Also, while in most modeling workflows model parameters are optimized, model structure is generally selected manually, and if optimized, data informativity is not considered. For these reasons, an approach using historical data with optimized selection of model’s structure and parameters, considering data informativity, is proposed. Therefore, historical data is collected and data around the process operating point is selected. Then, data is prefiltered, resampled, cleaned, and normalized. Then, for modeling, a vector autoregressive with exogenous variables model structure is considered. To estimate the model’s structural parameters and coefficients, a bi-level optimization algorithm is proposed. For the outer optimization, genetic algorithm, fireworks algorithm, particle swarm optimization, and simulated annealing algorithm are simulated and compared to conventional methods. Also, as normal operation data is used, a data informativity step is integrated to discard non-informative model structures. To estimate the model coefficients, ridge regression is used. In a case study, the approach is applied to model an industrial thickener. The obtained results show that the proposed approach outperforms conventional ones.

ABSTRACTIn this paper, a data mining based methodology for process identification from historical data was proposed. Thereon, it considers the phases of process understanding, data collection, data preparation, data modeling, and model evaluation. As some parts of historical data are irrelevant, a data selection step, based on the Gaussian Mixture Model (GMM) clustering algorithm, was considered. Additionally, the methodology includes a data informativity step to study the richness of data. In this regard, the condition number (CN) and the extended CN for ridge regression (RR CN) were used. To evaluate the approach, 2 years of industrial thickener historical data were used. Thereafter, data were prepared and an ARX (Auto-Regressive with eXogenous inputs) model structure was adopted to identify the model. To estimate input delays, Granger causality was used. As for fit criteria, least square regression was tested and compared to ridge regression as a less sensitive method to multicollinearity. The results were then evaluated based on the 20-step ahead prediction and compared to existing methods. In this context, the proposed approach gave the best results with an R2of 98.11% and 62.70% for 1 and 20-step ahead predictions, respectively.

In this manuscript, a new robust nonsingular fast terminal second-order sliding mode based on input/output feedback linearization is developed for high precision joint position tracking of robotic manipulators subject to perturbations and unknown dynamics. The proposed approach reduces chattering, eliminates the problem of singularity in classical terminal sliding mode and allows fast convergence during the so-called sliding phase, which is suitable for real robot applications. Moreover, since a simple linear equivalent system is derived using feedback linearization, the design procedure doesn’t require anymore the computation of the time derivative of the dynamics. Thus, the real-time implementation is simplified, and the control torque inputs that are fed into the system will not be risky for the controlled system. Simulation and experimental results are respectively conducted on a two rigid arm robot and on an industrial 7-DOF robot arm to demonstrate the effectiveness of the developed control scheme, its simplicity and superiority over other nonlinear controller based on second-order sliding mode.

This paper tackles the problem of high-precision joint position trajectory tracking in the presence of matched and unmatched perturbations of the humanoid NAO robot that has not direct drive joints. The proposed solution consists of combining a backstepping sliding mode with time delay estimation. Since the processor on-board has low computational power, the time delay estimation will alleviate the heavy computations while ensuring an effective approximation of the global robot dynamics. On the other hand, the backstepping method will allow the rejection of the mismatched perturbations, while the sliding mode will guarantee fast convergence and will deal with the estimation error. Moreover, the sliding mode will be based on the exponential reaching law in order to reduce the problem of chattering. The controller is designed step-by-step using recursive Lyapunov functions to ensure the stability. The last step in the design procedure consists of a modification based on the NAO’s proportional-derivative regulator to convert the computed torque inputs into a position command for the robot’s servo actuators. Finally, experimental work was carried out to validate the proposed control architecture and to verify its performance.

This paper proposes the design of a new sliding mode controller of the attitude fast inner-loop of a drone quadrotor type system. The controller uses the novel model-based switching functions approach, which leads to important simplifications of the pitch, roll and yaw torques control inputs fed to the quadrotor. The model-based switching functions approach forces as well a complete chattering decoupling of these three torque inputs and enhances the robustness of the closed-loop system. The proposed approach is combined with the fixed-time sliding mode approach, and is experimentally implemented and successfully validated on a quadrotor system.

This paper proposes an improved non-singular terminal super-twisting control for the problem of position and attitude tracking of quadrotor systems suffering from uncertainties and disturbances. The super-twisting algorithm is a second-order sliding mode known to be a very effective control used to provide high precision and less chattering for uncertain nonlinear electromechanical systems. The proposed method is based on a non-singular terminal sliding surface with new exponent that solves the problem of singularity. The design procedure and the stability analysis of the closed-loop system using Lyapunov theory are detailed for the considered system. Finally, the proposed control scheme is tested in simulations and by experiments on the parrot-rolling spider quadrotor. Moreover, a comparison is made with the standard super-twisting algorithm in the simulation part. The results obtained show adequate performance in trajectory tracking and chattering reduction.