This paper presents the use of Support Vector Machines (SVM) methodology for fault detection and diagnosis. Two approaches are addressed: the SVM for classification (Support Vector Classification-SVC) and SVM for regression (Support Vector Regression-SVR). A comparison was made between the two techniques through the study of a reactor of cyclopentenol production. In the case studied, different fault scenarios were introduced and it was evaluated which technique was able to detect and diagnose them. Finally, a comparison was made between the fault detection methodologies based on SVM and Dynamic Principal Component Analysis (DPCA) based detection techniques for a jacketed CSTR.

The use of Fault Tolerant Control (FTC) strategies provides alternatives for control loops in presence of faults by exploring physical and analytical redundancies available in the process. Therefore, the use of FTC techniques is essential to adequate industrial control systems concerning availability and reliability while preserving the closed-loop system performance. In this paper, we propose the extension of the fault-hiding approach for the control reconfiguration to a dynamic optimization problem in order to incorporate system constraints. This method uses the concepts of the virtual actuator and a moving horizon framework extending the original control reconfiguration problem to a quadratic programming problem. A multivariable neutralization process subjected to communication loss between the master controller and the final control elements is used as an example to analyze and demonstrate the performance of the proposed approach.

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Path planning techniques are of major importance for the motion of autonomous systems. In addition, the chosen path, safety, and computational burden are essential for ensuring the successful application of such strategies in the presence of obstacles. In this context, this work introduces a modified potential field method that is capable of providing obstacle avoidance, as well as eliminating local minima problems and oscillations in the influence threshold of repulsive fields. A three-dimensional (3D) vortex field is introduced for this purpose so that each robot can choose the best direction of the vortex field rotation automatically and independently according to its position with respect to each object in the workspace. A scenario that addresses swarm flight with sequential cooperation and the pursuit of moving targets in dynamic environments is proposed. Experimental results are presented and thoroughly discussed using a Crazyflie 2.0 aircraft associated with the loco positioning system for state estimation. It is effectively demonstrated that the proposed algorithm can generate feasible paths while taking into account the aforementioned problems in real-time applications.

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