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In smart farming, precision agriculture irrigation is essential to reduce water consumption and produce higher crop yields. Closed-loop irrigation based on soil moisture measurements has demonstrated the capability to achieve a considerable amount of water savings while growing healthy crops. Automated irrigation systems are typically implemented over wireless sensor networks, where the sensing devices are battery-powered, and thus they have to manage energy constraints by implementing efficient communication schemas. Self-triggered control is an aperiodic sampling strategy capable of reducing the number of networked messages compared to traditional periodical sampling. In this paper, we propose an energy-efficient communication strategy for closed-loop control irrigation, implemented over a wireless sensor network, where event-driven soil moisture measurements are conducted by the sensing devices only when needed. Thereby, the self-triggered algorithm estimates the occurrence of the next sampling period based on the process dynamics. The proposed strategy was evaluated in a pecan crop field and compared with periodical sampling implementations. The experimental results show that the proposed adaptive sampling rate technique decreased the number of communication messages more than 85% and reduced power consumption up to 20%, while still accomplishing the system control objectives in terms of the irrigation efficiency and water consumption.

In recent years, interest for petrochemical processes has been increasing, especially in refinement area. However, the high variability in the dynamic characteristics present in the atmospheric distillation column poses a challenge to obtain quality products. To improve distillates quality in spite of the changes in the input crude oil composition, this paper details a new design of a control strategy in a conventional crude oil distillation plant defined using formal interaction analysis tools. The process dynamic and its control are simulated on Aspen HYSYS® dynamic environment under real operating conditions. The simulation results are compared against a typical control strategy commonly used in crude oil atmospheric distillation columns.

Advancements in microelectronic technology provide the opportunity of implementing more complex and computation-heavy algorithms. One area that benefits from these advancements is the automation and control industry. Control techniques and algorithms, previously implemented in industrial and commercial personal computers, are being ported to embedded systems, taking advantage of their real-time and customization capabilities. This work introduces a software-based embedded implementation of a single-input single-output adaptive predictive control (APC) algorithm. The goal of the proposed implementation is to minimize execution time of the APC algorithm without affecting the precision of the results. The ZYBO Zynq-7000 development board was the selected platform for development and evaluation. Our proposed software-centric implementation relies on the development of libraries for matrix data storage and manipulation, and data structures to minimize data transactions. Experimental results included the comparison of APC implementations with different memory usage and data management approaches. An improvement on execution time was possible, reducing it nearly 50% from an initial implementation. Experimental results show no impact on the precision after comparing the implementation results to the results obtained using Scilab-based numerical computation.

Pulse firing offers significant process and productivity benefits, such as improved temperature uniformity and high heat transfer rates to the product load through maximum system turndown and utilizes the system’s burners at their most efficient firing rates. However, overshoot and undershoot is unavoidable. To minimize these effects, the system operates the burner at an enhanced turndown rate. Faster cycle rates improve temperature uniformity but reduce equipment lifetime. Therefore, a tradeoff exists between furnace temperature uniformity and the cycle rate used by the pulse firing control. This paper proposes models and simulates an advanced technique that improves temperature uniformity while decreasing the cycle time used by the pulse firing control. This provides reliable, safe furnace operating conditions, thereby extending the lifetime of the equipment. After an analysis of a furnace’s combustion system that utilizes the pulse firing method to control the heat demand of the furnace, non-linearities were found in the combustion system. To improve the performance of the temperature control, an error-driven function was coupled to the control strategy to compensate the signal error fed to a proportional–integral–derivative controller. The error-driven function was implemented using a fuzzy system, which improved the temperature uniformity and allowed a 60% duty cycle reduction in comparison with similar combustion systems.

Internet of things (IoT) is based on a global dynamic information network with cloud services where a great number of devices (things) exchange data to provide added-value services and products. There are several commercial and open source IoT platforms available in the market to connect devices to internet; however, they have cost and operational constraints that make them not suitable for academic projects. In this work, an IoT platform, known as Novus-io, is introduced in order to support academic projects for undergraduate students. With this platform and proper training, undergraduate students from different majors (not only from information technology and electronics) are capable to upgrade their school projects with IoT functionalities. The objective of this approach is to provide to any undergraduate student skills and knowledge on IoT, so they will be prepared, in their imminent step toward professionalism, to understand the relevance of digital services in today´s world.

A novel output feedback dynamic excitation control scheme is proposed for efficient load angle trajectory tracking tasks for synchronous generators. In this way, effective tracking of reference trajectories for the state variables and electric power is also guaranteed. The introduced control design approach is robust against parametric uncertainty, unmodelled dynamics, and variable mechanical power input commonly present in practical electric power systems. The control scheme only requires measurements of the load angle variable, and state and disturbance estimation is properly avoided. Computer simulation results are included to show the controller robustness and the satisfactory reference trajectory tracking specified for the machine operation for both continuous and switched control voltage signals.

An output feedback dynamic control scheme is proposed for efficient velocity trajectory tracking tasks for direct current (DC) electric motors. An asymptotic differentiation approach of signals is also introduced and applied for real-time angular acceleration estimation. The novel feature of this differentiator of signals is that it does not require any system mathematical model. Hence the presented differentiation approach admits a wide variety of practical applications in diverse electric power systems that require signal differentiation for their control and identification. The synthesis of the differentiation scheme considers the possible practical scenario of contaminated output signals with reasonably small noise. Some computer simulations results are included to show controller robustness to reject completely unknown chaotic load torque disturbances and parametric uncertainty, as well as effectiveness of the differentiation scheme of signals.

Improving performance and reducing the process operational costs represent a priority for the oil refinement industry. The challenge is given by high energy utilization and strict productivity specifications. Automatic control plays an essential role by providing theoretical and practical tools to overcome these challenges. This paper details the design of an Adaptive Predictive (AP) control strategy for an atmospheric distillation process. The strategy uses AP controllers to face the non-linear and time-varying process dynamics and was defined using classical interaction analysis tools such as the Relative Gain Array (RGA). The AP control strategy was simulated using ADEX and MATLAB simulation environments. The process was simulated using an Aspen Dynamic model. The controller performance is evaluated on a simulator of a crude oil atmospheric distillation process operating in a PEMEX refinery. The simulation results are also compared against a PID-based control strategy, showing an improvement of operational stability.

This paper proposes a novel output feedback control scheme for robust stabilization and tracking tasks in a magnetic suspension system. Active disturbance rejection control, differential flatness and on-line asymptotic disturbance estimation are properly used for the proposed control synthesis. The controlled system is subjected to a wide spectrum of unknown significant matched and unmatched disturbances due to external forces and voltages, parametric uncertainties, control and state-dependent perturbations and possibly input unmodeled dynamics. The effectiveness and robustness of the proposed active disturbance control scheme is verified by computer simulations for the robust tracking of a rest-to-rest reference position trajectory specified to firstly stabilize the suspended mass at a desired vertical position and next transfer it to another equilibrium position for both continuous and switched control voltage signals.

A generalized PI-like output feedback dynamic control scheme with additional integral compensation of the output error is proposed for robust tracking tasks of position reference trajectories for direct current electric motors with speed reduction gear head, using measurements of angular displacement only. The integral compensation is used to improve the robustness property of the controller, in regards to parametric uncertainty and variable load torque, and avoid the application of asymptotic estimation methods of the state vector. A family of Taylor polynomials approximates the perturbation signals, in order to reduce the control design complexity. These signals are compensated by the integral control action directly. Experimental and simulation results describe the effective performance of the control scheme proposed in this paper as an alternative solution to the efficient angular position control problem of direct current electric motors.

In this article is presented a differential flatness-based observer-control scheme for active nonlinear vehicle suspension systems subject to unknown exogenous disturbance excitations due to irregular road surfaces. Two active vibration controllers are proposed for hydraulic or electromagnetic suspension systems, which only require position measurements. The nonlinear effects, parameter variations, exogenous disturbances and possibly input un-modeled dynamics are lumped into an unknown bounded time-varying disturbance input signal affecting the differentially flat linear vehicle suspension system dynamics. The lumped disturbance signal and some time derivatives of the flat output are estimated by using a flat output-based linear high-gain dynamic observer. The proposed observer-control design methodology considered that the perturbation signal can be locally approximated by a family of Taylor polynomials. Some numerical simulation results are provided to show the efficiency, effectiveness and robust performance of the feedforward and feedback linearization control scheme for a nonlinear quarter-vehicle active suspension model.

This paper leads with the active cancellation problem of mechanical vibrations in rotor-bearing systems. The use of an active disk is proposed for actively balancing a rotor by means of locating a balancing mass at a suitable position. Two nonlinear controllers with integral compensation are proposed to put the balancing mass at a specific position. Algebraic identification is used for on-line eccentricity estimation, because of the implementation of this active disk is based on knowledge of the eccentricity. An important property of this algebraic identification is that the eccentricity identification is not asymptotic but algebraic, in contrast to most of the traditional identification methods, which generally suffer of poor speed performance. In addition, a velocity control is designed to take the rotor velocity to a desired operating point over the first critical speed. The controllers are developed in the context of an off-line prespecified reference trajectory tracking problem. Some numerical simulations are included to illustrate the dynamic performance of the closed loop system and the active vibration cancellation.

Three different schemes for Fault Tolerant Control (FTC) based on Adaptive Control in combination with Artificial Neural Networks (ANN), Robust Control and Linear Parameter Varying (LPV) systems are compared. These schemes include a Model Reference Adaptive Controller (MRAC), a MRAC with an ANN and a MRAC with an H∞ Loop Shaping Controller for 4 operating points of an LPV system (MRAC-4OP-LPV, MRAC-NN4OP-LPV and MRAC-H∞4OP-LPV, respectively). In order to compare the performance of these schemes, a coupled-tank system was used as testbed in which two different types of faults (abrupt and gradual) applied in sensor and actuators in different operating points were simulated. The simulation results showed that the use of ANN in combination with an adaptive controller for LPV-based system improves the FTC scheme, delivering a robust FTC system against abrupt and gradual sensor faults. For actuator faults, the only schemes that were fault tolerant were the MRAC-H∞4OP-LPV and the MRAC-4OP-LPV (i.e. the MRAC-H∞4OP-LPV was fault tolerant for actuator faults varying from 0 to 0.5 of magnitude).