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In 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 prepara...

The bulk port operations are given a growing attention for their important role in the global supply chain in different industries (mining, energy ...). To guarantee their competitiveness, the efficient management of port logistics, including yard side management is crucial. In this paper, we consider a real-word storage space allocation problem at an export bulk terminal. We formulate the problem as a mixed integer linear program and we propose a heuristic method to solve large scale data sets. Both the model and the heuristic can help the yard planner to test different scenarios and provide better stock yard plan, which is a first step toward improving the management of port operation in the bulk terminal under study.

In the mining industry, thickeners are used to increase density of slurries by removing water. When a thickener is in operation, the underflow slurry density should follow a given setpoint and stay inside a boundary layer. In general, thickeners are manually controlled. However, industrial experience has shown that manual control is not sufficient. For this reason, an advanced process control strategy is proposed in this paper. On the one hand, using historical data, a linear dynamic model of the studied thickener is developed. In this regard, data is collected, prepared, and data informativity is studied to ensure model identifiability and interpretability. On the other hand, a model predictive control is designed to control the process by manipulating the feed and the underflow slurry flowrates. The simulation results show that the method can be successfully used to control the underflow slurry density in the presence of feed variations and unmeasured disturbances.

This work investigates the problem of controlling robot manipulators with matched and mismatched uncertainties. To this end, a robust backstepping combined with super-twisting controller is developed to guarantee the convergence of the joint position states to their desired references. The proposed control technique allows the rejection of the uncertainties while eliminating the sliding mode's famous drawback that is the chattering. Based on recursive Lyapunov function, sufficient condition is set to ensure the closed-loop system stability. Finally, simulation and experimental works were carried out respectively on a two degrees-of-freedom robot model and on the seven degrees-of-freedom ANAT modular robot to verify the performance and the robustness of the developed control strategy.

In this paper, an Optimal Super-Twisting Algorithm (OSTA) with time delay estimation is designed based on Input/Output feedback linearization for uncertain robot manipulators. The design procedure consists on three steps. Firstly, an Input/Output feedback linearization is applied to transform the nonlinear model into a linear equivalent one. Secondly, by defining a quadratic performance, an optimal sliding surface will be designed. Finally, a super-twisting algorithm with time delay estimation is proposed for high accuracy tracking trajectory. Lyapunov theory is used to prove the finite-time convergence of the sliding surface and its derivative. This structure is used to estimate unknown dynamics and to reduce the control effort and the chattering phenomenon.

This paper proposes a super-twisting algorithm (STA) with time delay estimation (TDE) for the problem of high-accuracy tracking trajectory of robotic manipulators in the presence of uncertainties and unexpected disturbances. The TDE method is known for it capability to estimate uncertainties simply without an exact knowledge of the system dynamics. Using the estimated uncertainties, the control law is then designed based on STA to ensure robustness, finite-time convergence and chattering reduction. The stability analysis of the closed-loop system and the finite-time convergence are proved using Lyapunov theory. In order to show the effectiveness of the proposed method, simulations and experimental results were carried out on a 2-DOF rigid robot manipulator and on the 7-DOF ANAT robot arm, respectively.

Control of uncertain nonlinear systems is one of the main topics in automation, control problem. However, uncertainties caused by uncertain parameters, load variations, unmodeled dynamics and external disturbances affect the control performance. This paper investigates the problem of high accuracy joint space trajectory tracking. A discrete-time second order sliding mode combined with time delay estimation is designed to make the joint positions track a desired trajectory with high accuracy. In addition, sufficient condition assuring convergence of the error to zero in finite time based on Lyapunov theory is presented. Experiments on a 7-DOF ANAT robot arm are presented to verify the performance of the proposed controller.

The inverse kinematic analysis is required in many technical applications. Due to the non linearity of the system, resolving the Inverse Kinematics (IK) for robotic system becomes more challenging problem. In this chapter, the problem of IK for medical application is faced, based on human lower limb as being the three arm robotic system depending on the physiological constaints. A system analysis is carried out specially in three dimensionnel space. The developed forward kinematic model leads to define the feasible workspace of the human leg in the considered configuration. A constructive algorithm to compute the optimal IK of robot system is outlined, based on human performance measure that incorporates a new objective function of musculoskeletal discomfort. The effectiveness of the proposed approach is tested, and the algorithmic complexity is finally discussed.

The object of this paper is to study the problem of robust stabilizability for a class of continuous-time Linear Systems with Markovian Jumps (LSMJ) and saturated input. Sufficient conditions for stochastic stabilizability with constrained control input are given in terms of Linear Matrix Inequalities (LMIs). The approach gives a direct expression of the linear state feedback controller that respects the condition of saturation. Also, the robustness of these systems for four types of structural uncertainties: (1) matching conditions, (2) norm bounded, (3) value bounded and (4) linear combinations is treated. Finally, numerical examples are given to illustrate the usefulness of the proposed results.

This paper investigates the problem of stator current control in presence of uncertainties and unmeasurable rotor current for a six-phase induction motor drive. An inner control loop based on a robust discrete-time sliding mode with time delay estimation method is proposed to ensure the finite-time convergence of the stator currents to their desired references while the proportional-integral controller is used for the outer speed control. Sufficient conditions are established to ensure the stability of the closed-loop system. Simulation results were carried out to verify the performance of the proposed robust control strategy for a six-phase induction motor drive.

The paper presents a robust sliding mode with time delay estimation method for controlling the attitude of a tri-rotor unmanned aerial vehicle (UAV) in presence of uncertainties and disturbances. The proposed control algorithm allows high accuracy tracking since a good disturbance estimation is provided using time delay estimation method and allows chattering reduction. The stability analysis of the closed-loop system is presented using the theory of Lyapunov. Finally, two numerical simulations are presented in the presence of disturbances to show the effectiveness of the proposed nonlinear control scheme.

Multiphase induction motor drives have gained popularity due to their fault tolerance and better power/current distribution per phase which are very attractive for industrial applications. Current control strategies with the estimation of unmeasurable variables (typically rotor variables) has been recently proposed for multiphase induction drive. This paper proposes a novel current control based on super-twisting algorithm with time delay estimation for a five-phase induction motor drives. This method considers the estimation of rotor variables and disturbances, and also ensures the stability by using Lyapunov theory. Simulation results are provided to show the effectiveness of the proposed method and also to compare with other controllers such as a proportional-integral and a model-based predictive controllers.

This paper proposes a non-singular terminal sliding mode combined with time delay controller for fast tracking trajectory of uncertain second-order nonlinear systems in presence of external disturbances. The motivation for using Non-Singular Terminal Sliding Mode (NSTSM) mainly relies on its appreciable features, such as simplicity of design and implementation, high precision and fast convergence. However, the NSTSM problem is the well-known chattering, which degrades the controlled system. Then, using the time delay to estimate uncertainties and disturbances allows chattering reduction. The stability and the robustness of the proposed controller are verified by using the classical Lyapunov criterion. The proposed controller is implemented in real time on the seven-degrees-of-freedom (7-DOF) ANAT robot arm and compared with the Sliding Mode with Time Delay Control (SMTDC) with classical sliding surface to prove its superiority.