Search

This article presents a performance improvement over a previously published software-based embedded implementation of the single-input single-output (SISO) adaptive predictive control (APC) algorithm. This article mainly focuses on reducing the execution time of a control iteration of the SISO APC algorithm, while preserving precision. A profiling study of the algorithm is developed, which identifies the performance bottlenecks. Analyzing the outcomes of the profiling study, a set of enhancements for efficient computations of the Recursion and Adaptation stages of the SISO APC algorithm is proposed, which greatly reduces the computational complexity of the algorithm. The ZYBO development board is the selected platform for testing. Firmware development and optimization is oriented to provide speedup using hardware acceleration, exploiting the processor in the ZYBO at its full extents, targeting the available coprocessors embedded in the core. Comparison with previously published work shows outstanding improvements in execution time, obtaining speedup increments up to $\text{12.0}\times$ . The enhancements performed to the SISO APC algorithm along with firmware optimization allow the proposed implementation to run at execution times as low as 0.7 $\mu$ s, which are considerably lower than existing embedded MPC implementations found in the literature.

The Model Predictive Control technique is widely used for optimizing the performance of constrained multi-input multi-output processes. However, due to its mathematical complexity and heavy computation effort, it is mainly suitable in processes with slow dynamics. Based on the Exact Penalization Theorem, this paper presents a discrete-time state-space Model Predictive Control strategy with a relaxed performance index, where the constraints are implicitly defined in the weighting matrices, computed at each sampling time. The performance validation for the Model Predictive Control strategy with the proposed relaxed cost function uses the simulation of a tape transport system and a jet transport aircraft during cruise flight. Without affecting the tracking performance, numerical results show that the execution time is notably decreased compared with two well-known discrete-time state-space Model Predictive Control strategies. This makes the proposed Model Predictive Control mainly suitable for constrained multivariable processes with fast dynamics.

The control of an automotive suspension system by means of a hydraulic actuator is a complex nonlinear control problem. In this work, a linear parameter varying (LPV) model is proposed to reduce the complexity of the system while preserving the nonlinear behavior. In terms of control, a dual controller consisting of a model predictive control (MPC) and a Linear Quadratic Regulator (LQR) is implemented. To ensure stability, quadratic stability conditions are imposed in terms of Linear Matrix Inequalities (LMI). Simulation results for quarter-car model over several disturbances are tested in both frequency and time domain to show the effectiveness of the proposed algorithm.

Most of the mechanical dynamic systems are subjected to parametric uncertainty, unmodeled dynamics, and undesired external vibrating disturbances while are motion controlled. In this regard, new adaptive and robust, advanced control theories have been developed to efficiently regulate the motion trajectories of these dynamic systems while dealing with several kinds of variable disturbances. In this work, a novel adaptive robust neural control design approach for efficient motion trajectory tracking control tasks for a considerably disturbed non-linear under-actuated quadrotor system is introduced. Self-adaptive disturbance signal modeling based on Taylor-series expansions to handle dynamic uncertainty is adopted. Dynamic compensators of planned motion tracking errors are then used for designing a baseline controller with adaptive capabilities provided by three layers B-spline artificial neural networks (Bs-ANN). In the presented adaptive robust control scheme, measurements of position signals are only required. Moreover, real-time accurate estimation of time-varying disturbances and time derivatives of error signals are unnecessary. Integral reconstructors of velocity error signals are properly integrated in the output error signal feedback control scheme. In addition, the appropriate combination of several mathematical tools, such as particle swarm optimization (PSO), Bézier polynomials, artificial neural networks, and Taylor-series expansions, are advantageously exploited in the proposed control design perspective. In this fashion, the present contribution introduces a new adaptive desired motion tracking control solution based on B-spline neural networks, along with dynamic tracking error compensators for quadrotor non-linear systems. Several numeric experiments were performed to assess and highlight the effectiveness of the adaptive robust motion tracking control for a quadrotor unmanned aerial vehicle while subjected to undesired vibrating disturbances. Experiments include important scenarios that commonly face the quadrotors as path and trajectory tracking, take-off and landing, variations of the quadrotor nominal mass and basic navigation. Obtained results evidence a satisfactory quadrotor motion control while acceptable attenuation levels of vibrating disturbances are exhibited.

HighlightsControl theory applied to precision irrigation reduces water consumption and improves crop productivity.A model-based control using hybrid automata is proposed to describe soil, crop, and weather dynamics.The model was developed and validated with experimental data from a grass irrigation process.The proposed model can be used to develop robust and real-time control algorithms to improve irrigation systems. Abstract. Closed-loop control for precision irrigation represents an effective method to provide water savings in this resource-intensive activity. Typically, implementation of these systems uses an on-off control approach with soil moisture as the feedback variable. In these cases, no process modeling is required due to the simplicity of the implemented control algorithm. However, the amount of water consumed by irrigation globally presents an interesting application area for the control discipline. Therefore, to obtain greater water savings and to improve crop productivity through control theory, an irrigation model is required that integrates the three main elements in the irrigation process: soil, crop, and weather. This article proposes a hybrid automata model for a closed-loop irrigation system applied to a grass-covered area. The main measured variables used to represent the process dynamics are soil moisture, vegetation index, and evapotranspiration. The hybrid automata approach allows a complex system to be modeled as a finite state machine in which there is a specific linear model for each state. When certain conditions are met, the system transitions from one state to another, and the new behavior is represented by a different linear model. To develop the proposed model, experimental data were obtained from an irrigation process, the data were analyzed to produce a model using the hybrid automata approach, and finally the model was validated with a new set of measured data. Keywords: Closed-loop irrigation, Evapotranspiration, Hybrid automata, NDVI, Precision irrigation modeling.

Passenger comfort and vehicle stability are key aspects that must be guaranteed on ground vehicles, and semi-active suspensions have offered an outstanding solution to meet these opposite objectives. This contribution describes a novel autoregressive with exogenous input (ARX) model-based predictive control strategy handled by a driver block applied on a semi-active vehicle suspension to improve passenger comfort and road holding when compared against a passive vehicle suspension system and another more complex control designs reported in the literature. The ARX model employs a driver block to reduce the computational load of the closed-loop semi-active suspension. In addition, the controller’s formulation and the case study consider the actuator’s physical constraints to achieve more realistic results. This case-study includes a one-quarter semi-active suspension with two degrees-of-freedom, and the numerical data comes from a real magnetorheological damper characterization. The results, in frequency-domain and time-domain, are measured based on specific performance criteria. A substantial improvement against a passive suspension is quantified and discussed. For a broader perspective of the findings, the results are compared against another reported work. This research effort could be the basis of further studies to achieve more robust solutions such as adaptive/optimal predictive controllers to improve vehicle’s comfort and stability.

This paper deals with the reference trajectory tracking problem and simultaneous active disturbance suppression on a class of controlled aerial mechanical systems by processing measurable output signals. A novel dynamic control method for desired motion reference trajectory tracking for quadrotor helicopters is introduced. Measurements of position output signals for efficient and robust tracking of motion profiles specified for the unmanned aerial vehicle are only required. Thus, differentiation of signals and real-time estimation of disturbances affecting the multi-input multioutput, underactuated nonlinear dynamic system are unnecessary. The presented active control approach can be directly extended for a class of vibrating mechanical systems. Analytical, experimental, and numerical results are presented to prove the satisfactory performance of the proposed trajectory tracking control approach for considerably perturbed operating scenarios.

This paper proposes a novel finite dimensional discrete-time Nonlinear Model Predictive Control. This technique is based on discrete-time state-space models, Taylor series expansion for prediction and performance index optimization. Furthermore, the technique extends the concept of the Lie derivative for the discrete time case using Euler backwards method. The performance validation for the discrete-time Nonlinear Model Predictive Control uses the simulation of a single-link flexible joint robot and the inverted pendulum. Comparison of the proposed finite dimensional discrete-time Nonlinear Model Predictive Control technique with Feedback Linearization Control is also discussed. Analytical and numerical results show excellent performances for both, the single-link flexible joint and inverted pendulum controllers using the proposed discrete-time Nonlinear Model Predictive Control technique.

Oil fields around the world produce petroleum that is often accompanied by significant amounts of water and salt. Therefore, to avoid operational problems, dehydration/desalting plants are installed in crude oil production units to remove water-soluble salts from an oil stream. In this paper, a well-designed dehydration/desalting plant of crude oil is modeled and simulated in dynamic mode using Aspen HYSYS®. Furthermore, control strategies are implemented to maintain international standard quality values in the resulting crude oil. Finally, the behavior of the system under typical and proposed control structures is compared.

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.

An active vibration control approach from an online estimation perspective of unavailable feedback signals for a quarter-vehicle suspension system is introduced. The application of a new signal differentiation technique for the online estimation of disturbance trajectories due to irregular road surfaces and velocity state variables is described. It is assumed that position measurements are only available for active disturbance suppression control implementation. Real-time signal differentiation is independent of detailed mathematical models of specific dynamic systems and control force generation mechanisms. Active control forces can be supplied by electromagnetic or hydraulic actuators. Analytical and simulation results prove the effective and fast dynamic performance of the online signal estimation as well as a satisfactory active disturbance attenuation on a quarter-vehicle active suspension system.

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.

En este artículo se aborda el problema de control para tareas de seguimiento de trayectorias de referencia variantes en el tiempo para la posición de un objeto en un sistema de levitación magnética no lineal diferencialmente plano, usando únicamente mediciones de posición. Se propone un esquema novedoso de diferenciación de señales para la estimación asintótica de la velocidad y aceleración. Este diferenciador de señales se puede utilizar en muchas aplicaciones de control de sistemas de ingeniería prácticos en donde se requiera la diferenciación de cualquier señal. La diferenciación de señales se combina con un controlador basado en planitud diferencial para el seguimiento asintótico de trayectorias de referencias. Se incluyen algunos resultados en simulación para mostrar el desempeño eficiente del esquema de control-diferenciador de señales propuesto. Se presentan dos casos para tareas de seguimiento de trayectorias de referencia. El primero considera el problema de transferir el objeto desde una posición de equilibrio nominal deseada a otra. El segundo aborda el seguimiento de una trayectoria sinusoidal para la posición del objeto.

Francisco Beltran-Carbajal1, Esteban Chavez-Conde2, Gerardo Silva Navarro3, Benjamin Vazquez Gonzalez1 and Antonio Favela Contreras4 1Universidad Autonoma Metropolitana, Plantel Azcapotzalco, Departamento de Energia, Mexico, D.F. 2Universidad del Papaloapan, Campus Loma Bonita, Departamento de Ingenieria en Mecatronica, Instituto de Agroingenieria, Loma Bonita, Oaxaca 3Centro de Investigacion y de Estudios Avanzados del I.P.N., Departamento de Ingenieria Electrica, Seccion de Mecatronica, Mexico, D.F. 4ITESM Campus Monterrey, Monterrey, N.L. Mexico

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.

En este artículo se presenta un enfoque de diseño de esquemas de control activo robusto para sistemas de suspensión de vehículos, usando técnicas de control Proporcional-Integral Generalizado, modos deslizantes y planitud diferencial, para atenuar vibraciones indeseables inducidas por perturbaciones de terreno irregular. Se proponen dos esquemas de control: uno de ellos emplea mediciones de posición y de velocidad y el otro requiere únicamente mediciones de posición. Se propone la reconstrucción integral de las derivadas de la salida plana para evitar el uso de sensores de velocidad y de aceleración, así como la información de perturbaciones de terreno desconocidas.

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).