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This paper presented a model predictive control strategy with guaranteed nominal stability, applicable to systems with repeated integrating poles. The offset-free control law is obtained inside a one-layer optimization formulation through the development of a state-space model based on an analytical expression of the system step response. While the stability of the closed-loop system is achieved by considering an infinite prediction horizon along with the inclusion of terminal equality constraints, its feasibility to tackle model uncertainty is ensured by means of an always feasible-optimization formulation through the insertion of a suitable set of slack variables. The effectiveness of the method is illustrated for the output tracking and disturbance rejection problem in a benchmark system, “ball-and-plate” apparatus, including a nonlinear plant-model mismatch scenario.

This work presents the benefits of using an adaptive model predictive control approach for controlling an ESP-lifted oil well system. The idea behind the proposed scheme is the successive linearization of the plant nonlinear model and incorporate it into an infinite-horizon MPC formulation, which handles feasibility issues by considering slacked terminal constraints. Also, suitable use of a zone control strategy to deal with time-varying ESP operating envelope constraints (downthrust and upthrust) becomes the approach implementable in practice. Nonlinear plant-model mismatch scenarios, including simulation of unmeasured disturbances and the tracking of ESP operation economic targets, show the effectiveness of the proposed controller concerning different operating conditions of the oil production process equipped with ESP.

Data-driven sensors are techniques capable of providing real-time information of unmeasured variables based on instrument measurements. They are valuable tools in several engineering fields, from car automation to chemical processes. However, they are subject to several sources of uncertainty, and in this way, they need to be able to deal with uncertainties. A way to deal with this problem is by using soft sensors and evaluating their uncertainties. On the other hand, the advent of deep learning (DL) has been providing a powerful tool for the field of data-driven modeling. The DL presents a potential to improve the soft sensor reliability. However, the uncertainty identification of the soft sensors model is a known issue in the literature. In this scenario, this work presents a strategy to identify the uncertainty of DL models prediction based on a novel Monte Carlo uncertainties training strategy. The proposed methodology is applied to identify a Soft Sensor to provide a real-time prediction of the productivity of a chemical process. The results demonstrate that the proposed methodology can yield a soft sensor based on DL that provides reliable predictions, with precision being proven by its corresponding coverage region.

This paper proposes the integration of an Adaptive Artificial Potential Fields algorithm with a new end effector orientation control technique for real-time robot path planning. The development of autonomous robotic systems has undergone several advances in path planning algorithms. These systems generate object collision-free paths in the robot’s workspace. In this context, the Artificial Potential Fields technique has been the focus of improvements in recent years due to its simplicity of application and efficiency in real-time systems, since it does not require a global mapping of the robot’s workspace. In spite of its efficiency, this technique is susceptible to local minimum problems of different natures, such as Goals Non-Reachable with Obstacles Nearby (GNRON). To solve this problem, we suggest the use of an improvement called Adaptive Artificial Potential Fields used in conjunction with the proposed end effector orientation control technique, which allows reaching a desired orientation of the end effector. The resulting force, generated from the Adaptive Artificial Potential Field, guides the robot end effector to the goal. The Robot Operating System (ROS) framework and a collaborative robot manipulator UR5 are used to validate the proposed method on an approaching task for an object on a 3D printer tray.

This article presents a solution of the inverse kinematics problem of 7-degrees-of-freedom serial redundant manipulators. A 7-degrees-of-freedom (7-DoF) redundant manipulator can avoid obstacles and thus improve operational performance. However, its inverse kinematics is difficult to solve since it has one more DoF than that necessary for reaching the whole workspace, which causes infinite solutions. In this article, Gröbner bases theory is proposed to solve the inverse kinematics. First, the Denavit–Hartenberg model for the manipulator is established. Second, different joint configurations are obtained using Gröbner bases theory. All solutions are confirmed with the aid of algebraic computing software, confirming that this method is accurate and easy to be implemented.

This work presents a path-following controller for a unicycle robot. The main contribution of this paper is to demonstrate the restriction of transverse feedback linearization (TFL) to obtuse angles on piecewise linear paths. This restriction is experimentally demonstrated on a Kobuki mobile robot, where it is possible to observe, as a result of the limitation of the TFL, the convergence to another domain of attraction.

This paper presents an application of a stabilizing model predictive control (IHMPC) strategy with the underlying guarantee of feasibility to an oil production well system with Electric Submersible Pump (ESP) installation. The proposed controller is compared with a conventional finite-horizon MPC which provides some unfeasible solutions in the presence of an unmeasured disturbance, due to the typical conflict among the ESP-lifted oil well system constraints. The results show IHMPC as a viable choice to improve ESP-lifted well production since it can incorporate the desired requirements and provides stabilizing control actions.

An Electric Submersible Pump (ESP) artificial lifting downhole sensor data transmission may fail due to a reduced ESP electric cable isolation. An electronic recovering system prototype is proposed to re-establish the lost sensor signal due to the electrical current leakage that flows from the conductors to the ground. An innovative electronic system prototype has been developed to monitor an ESP downhole sensor operation and recover its lost signal due to the current leakage is proposed. Experimental tests are discussed, simulating high current leakage, and the transmission data recovered by the prototype are compared with data from a conventional oil industry downhole sensor.

Oil exploitation has moved into deeper reservoirs with the advances in drilling techniques and thus the development of new pumping techniques has become a challenge to improve production. The positive displacement pumping system proposed in this paper is presented as an artificial lift technique. Here we present a new pumping device, its estimated operational curves through numerical simulations and a prototype of the device, denominated a Double Acting Submersible Linear Pump (SLP), which is a hydraulic system adapted for inside well operations. Design parameters used in the tests were optimized using the Particle Swarm Optimization (PSO) algorithm to maximize oil production while optimizing the parameters (characterstics) of the pump, such as maximum pump diameters and submersible electric motor power, pressure and volumetric displacement of the hydraulic pump. Also presented are the operating curves estimated from the numerical simulation of the power and pump modules, as well as the SLP0 and SLP1 experimental test curves in a controlled environment using the optimized parameters obtained using PSO The data obtained were compared to the computational simulations performed with the Automation Studio™ software. The SLP0 and SLP1 optimal design tests showed the importance to delimit the range of operation within which the SLP must operate, since the pipes and hydraulic components must be suitable for the flow of hydraulic oil and the production oil. The findings presented here play an important role in the production process of a fully operational SLP prototype.

In control theory, the inverted pendulum is a class of dynamic systems widely used as a benchmarking for evaluating several control strategies. Such a system is characterized by an underactuated behavior. It is also nonlinear and presents open-loop unstable and integrating modes. These dynamic features make the control more difficult, mainly when the controller synthesis seeks to include constraints and the guarantee of stability of the closed-loop system. This chapter presents a stabilizing model predictive control (MPC) strategy for inverted pendulum-like behaved systems. It has an offset-free control law based on an only optimization problem (one-layer control formulation), and the Lyapunov stability of the closed-loop system is achieved by adopting an infinite prediction horizon. The controller feasibility is also assured by imposing a suitable set of slacked terminal constraints associated with the unstable and integrating states of the system. The effectiveness of the implementable and stabilizing MPC controller is experimentally demonstrated in a commercial-didactic rotary inverted pendulum prototype, considering both cases of stabilization of the pendulum in the upright position and the output tracking of the rotary arm angle.

This article presents an integrated method for estimating parameters for an electric submersible pump system with process variables data. Validation of a phenomenological model is also performed. The parameters and the associated probability density function are obtained through Bayesian inference, and the model validation is achieved in two stages. The first one is the validation of the dynamic response in which the model is compared with the experimental data. The second is achieved by comparing the regions covered by the experimental data and the model, both in steady-state. The experimental data’s uncertainty is assessed using the Guide for the Expression of Uncertainty in Measurement. In turn, the uncertainty of the model’s prediction is obtained by propagating the probability density function parameters. The results indicate that the method can provide a model to represent the system behavior within the existing uncertainties. Additionally, the procedure can be applied in oil production fields to provide substitute models for general purposes, such as production control, optimization, and assistance.

An Electric Submersible Pump (ESP) vibration analysis was performed, considering different wear states and operational conditions. The pumps were tested with different fluid viscosities, operating points, and speeds to evaluate their vibration behavior, with the aim of providing characteristics for non-invasive differential vibration diagnosis. A specific frequency spectrum estimation method is described, focusing on the extraction of frequency component vibration amplitudes used in the petroleum industry-standard vibration analysis. A time-interval definition procedure was proposed to reduce signal amplitude losses due to variation of synchronous operating frequency. The results indicate that the frequency component peak amplitudes can be more accurately identified during synchronous frequency variation by the proposed method than by typical estimation methods. In this study, an ESP vibration differential diagnosis was achieved by analyzing relations between orders of the synchronous frequency, and by distinguishing that the synchronous component amplitude rose approximately proportionally to the square of the rotating speed; this differentiates unbalance fault from a bent shaft or a misalignment. A correlation matrix analysis is provided to demonstrate that variation in the fluid-temperature difference between the pump intake and discharge is related to the vibration amplitude variation in a pump with a vibration fault.