Abstract In low income countries, existing drip irrigation systems are cost prohibitive to many smallholder farmers. Companies are working to develop efficient, low-cost irrigation systems by using technologies such as positive displacement (PD) pumps and pressure compensating (PC) emitters. However, these two technologies have not been paired in an efficient and cost-effective manner. Here we describe a proof-of-concept pump control algorithm that demonstrates the feasibility of exploiting the physical relationship between the input electrical power to a PD pump and the hydraulic behavior of a system of PC emitters in order to determine the optimal pump operating point. The development and validation of this control algorithm was conducted in partnership with the Kenya-based irrigation company SunCulture. This control method is expected to reduce cost, improve system efficiency, and increase accessibility of irrigation systems to smallholder farmers.

Abstract This paper presents a technique for the lane keeping and the longitudinal speed control of an autonomous vehicle with the combination of an MPC and a PID control. The goal of the proposed control method is to minimize the lateral deviation and relative yaw angle with respect to the planned trajectory, while driving the vehicle at the highest acceptable longitudinal speed. The reference profile of the longitudinal speed is computed considering both the lateral and longitudinal dynamic of the vehicle. The vehicle is represented by means of a linear 3-DoF bicycle model. The control algorithm takes the road lane boundaries as the only external input. The proposed strategy is validated in simulation on three distinct driving scenarios.

Abstract Impact forces are a destructive, yet common occurrence in legged locomotion. Every step produces a collision when the leg’s inertia immediately stops upon ground contact. This results in peak impact forces and high frequency vibrations that resonate through the system, damage components, and complicate control algorithms. Rubber or other damping material is the assumed solution for mitigating these impacts. However, we show the benefit of using foot springs where both stiffness and maximum compression are customized to the leg. Such springs eliminate peak impact forces by gradually bringing the leg’s inertia to rest. The maximum compression point (i.e. a hard stop) then provides a rigid surface during stance. We provide a methodology for designing this passive dynamic foot that is validated through simulation and physical testing. Our results show this methodology reduces rigid body impacts and foot vibrations in a way traditional methods, reliant upon rubber or damping, are yet to achieve.

Abstract Wind energy is currently the fastest growing electricity source. To meet the output demand, wind turbines are becoming larger and more flexible leading to the problems of structural load especially in case of offshore turbines. Advanced control algorithms are developed to reduce the load, allowing to build larger turbines, and expand their lifetime. Observer-based control algorithms such as Linear-Quadratic-Gaussian LQG control which uses LQR to calculate the optimal observer and controller gains are commonly applied to wind turbines in literature. However the approach requires to calculate the observer and control gains separately. In addition, linear models used for parameter calculation may have errors with respect to the nonlinearities of wind turbines and induced to unmodeled dynamical properties. These modeling errors need to be considered to to guarantee the stability of the controlled system. Alternatively a robust design assuming bounds and limits of models have to be realized to guarantee stability while ignoring details of modeling. This paper proposes an optimal robust observer-based state feedback controller for large-scale wind turbines to realize multi objectives, including structural load mitigation and rotor speed regulation. The novel contribution is that the observer gain parameters, control gains, and integral action are optimized at the same time within H ∞ mixed sensitivity framework to achieve desired performance with respect to power regulation, structural load mitigation, and also robustness for the wind turbine control system. The control performances have been verified by a high fidelity simulation software and are compared to those of a classical baseline controller.

Abstract This contribution deals with the parallelization of multibody systems by making use of co-simulation techniques. The overall model is split into a user-defined number of subsystems, which are coupled and computed by means of a co-simulation approach. The co-simulation methods considered here are weak coupling approaches, which implies that each subsystem is solved independently from the other subsystems within a macro-time step. Information (i.e. coupling variables) is only exchanged between the subsystems at certain communication-time points (macro-time points). Within each macro-time step, the unknown coupling variables are approximated by extrapolation polynomials. The separate integration of the subsystems is the crucial point for a parallelized computation. A main drawback of many co-simulation implementations is that they are based on a constant macro-step size. Using an equidistant communication-time grid may in many practical applications be not very efficient with respect to computation time, especially in connection with highly nonlinear models or in context with models with strongly varying quantities. Here, a co-simulation approach is presented which incorporates a macro-step size and order control algorithm. Numerical examples show the benefit of this implementation and the significant reduction in computation time compared to an implementation with an equidistant communication-time grid.

Abstract Combustion phasing, which can be defined as the crank angle of fifty percent mass fraction burned (CA50), is one of the most important parameters affecting engine efficiency, torque output, and emissions. In homogeneous spark-ignition (SI) engines, ignition timing control algorithms are typically map-based with several multipliers, which requires significant calibration efforts. This work presents a framework of model-based ignition timing prediction using a computationally efficient control-oriented combustion model for the purpose of real-time combustion phasing control. Burn duration from ignition timing to CA50 (Δθ IGN-CA50 ) on an individual cylinder cycle-by-cycle basis is predicted by the combustion model developed in this work. The model is based on the physics of turbulent flame propagation in SI engines and contains the most important control parameters, including ignition timing, variable valve timing, air-fuel ratio, and engine load mostly affected by combination of the throttle opening position and the previous three parameters. With 64 test points used for model calibration, the developed combustion model is shown to cover wide engine operating conditions, thereby significantly reducing the calibration effort. A Root Mean Square Error (RMSE) of 1.7 Crank Angle Degrees (CAD) and correlation coefficient (R 2 ) of 0.95 illustrates the accuracy of the calibrated model. On-road vehicle testing data is used to evaluate the performance of the developed model-based burn duration and ignition timing algorithm. When comparing the model predicted burn duration and ignition timing with experimental data, 83% of the prediction error falls within ±3 CAD.

Abstract Temperature control is present in many industrial processes, making this skill mandatory for the control engineers. For this reason, different training temperature platforms have been created for this purpose. However, many of these platforms are expensive, require elaborate facility accommodations, and have higher heating and cooling times, making not suitable for teaching and training. This paper presents a low-cost educational platform for temperature control training. The platform employs a Peltier module as a heating element, which has lower heating and cooling time than other thermal system implementations. A low-cost real-time thermal camera is employed as a temperature feedback sensor instead of a standard thermal sensor. The control algorithm is developed in Matlab-Simulink and employs an Arduino board as hardware in the loop to manage the Peltier module. A temperature control experiment is performed to show that the platform is suitable for teaching and training experiences not only in the classroom but for engineers in the industry.

External mechanical loading is a major contributing factor in pressure ulcer formation and is a major health concern for wheelchair users. Seat cushion technologies are employed to reduce the magnitude and duration of this loading using soft seating surfaces and pressure offloading techniques. However, pressure offloading often results in the creation of new high pressure points which can still lead to pressure ulcer formation. In order to mitigate the issue, a novel closed-loop controlled seat cushion system is developed with sensorized air cell arrays which can continuously monitor pressure profile of a seated person and modulate this interface pressure. This paper presents the control implementation of this seat cushion system using a novel scheduling control algorithm based on bang-bang control as well as the corresponding electronics and pneumatic layout. The effectiveness of the system is demonstrated for real-time pressure mapping, offloading, and redistribution of seating interface pressure and its capabilities of instantaneous local pressure measurement as well as automated pressure modulation are verified.

Active, transtibial prostheses typically use finite state control algorithms that struggle with cadence and gait variability of the amputee. Recent work in artificial neural networks (ANN) have shown the possibility to predict the users intent based on EMG activity and the current position of the ankle, which can be used as an input signal into an improved controller. This paper examines how to implement an ANN signal into a zero order impedance controller, i.e., a stiffness controller, on a specific active transtibial prosthesis. The prosthesis incorporates a linear spiral spring in parallel with a four-bar mechanism. In order to implement stiffness control, the spring was moved to being in series with the four-bar mechanism to establish a relationship between the torque of the spring and the position of the motor. To ensure stiffness control is feasible, a MATLAB Simulink model of the system was created to test the robustness of the controller and the effect of moving the spring from parallel to series. The robustness of the controller was verified as the ankle position and torque requirements are met in the simulation. The Simulink model accurately models the new system and can be used in the future to optimize the motor or the four-bar mechanism for this new type of control.

Control strategies of electric-bikes (e-bikes) do not take the physiological characteristics (e.g. aerobic fitness status) of the rider into account. By means of mathematical modelling, our aim was to analyse different assistive strategies that include these characteristics. Particularly, we applied an Optimal Control (OC) algorithm to test whether an attentive control strategy could guarantee a sustainable effort for the rider throughout an entire climbing course with varying slope. We found that the contribution of the electric motor was pivotal during accelerations, so the effort for the kinetic energy conversion was shared between the electric motor and the cyclist. OC seems to fit very well in a scenario where e-bikes are adopted on a daily basis for commuting or to increase the level of physical activity in a sedentary population. We suggest that intelligent control algorithms, like OC, could be embedded in the electric motors to improve e-bike experience, especially in sedentary adults.

Electric Vehicles (EVs) with single-ratio gearbox provide high levels of smoothness, but using two-speed gearbox can provide significant benefits in terms of vehicle acceleration, max speed, and energy consumption. However, a smooth and seamless shifting is hard to be achieved due to the relatively large transmission ratio gap between two gears and the high speed characteristic of drive motor (DM). The purpose of this paper is to demonstrate a new seamless shifting control algorithm using DM torque for a novel multi-speed EV powertrain. First of all, a novel two-speed uninterrupted mechanical transmission, which consists of a planetary gear, a dry clutch and a band brake, is presented and modeled. In addition to clutch and band brake shift control strategies, new DM torque integration strategies are involved based on quick torque response feature of motors. According to the proposed algorithm, DM torque compensation in torque phase during upshift and less off-going clutch disengagement with DM torque compensation in inertia phase during downshift can both contribute to the uninterrupted output. To shorten the inertia phase time, the half-engagement of oncoming brake and reverse DM torque compensation are recommended. Simulation studies are taken to determine the shift transient behavior of the clutch, band brake and DM during two typical shifting conditions, power-on (when positive DM power is transmitted through the transmission when vehicles speed up or cruise) upshift and power-on downshift, which particularly demand uninterrupted output torque. The simulation results prove that the algorithm works effectively.

This paper presents an autonomous multivehicle control algorithm capable of persistently searching and tracking targets in a defined search area subject to operational endurance constraints of individual agents. The algorithm development is modular to allow scalability and a control architecture that can be modified to any type of autonomous vehicle, search area, or target. In practical application, a target can be anything from heat signatures to radioactive material; therefore, this work employs a generic emitter-detector pair as a placeholder relationship for real world applications. The control strategy accounts for the appearance, motion, and disappearance of multiple targets in the search space constituting the utility of creating a team of multiple search agents. When agent battery level drops below a predetermined threshold, the agent returns to a base station to recharge and be relaunched into the mission. Remaining agents must account for this loss and gain of other team members as they exit the search environment. The contributions of this work are 1) the design of search trajectories for autonomous vehicles with limited endurance, 2) incorporation of return-to-base and recharge time requirements, and 3) coordination of multiple vehicles by developing a decision-making model to and assign agents to operational modes. We have run an extensive number of experimental trials to collect and analyze performance data for further development and testing.

Although active disturbance rejection control (ADRC) has demonstrated promising results in many applications, in some safety critical applications e.g. power plant, unmanned aerial vehicle, etc., it is often reluctant to switch from an existing control algorithm, either the traditional PID controller or other advanced control algorithms, to a new one. If the switching between the control algorithms can be made seamless, many more opportunities may open up. In this paper, the problem of making the switching to the ADRC smooth is studied. Previous attempts to solve the problem are not thorough enough, since only a second order system was addressed. A very simple and effective method is proposed based on analysis to make the transition seamless. The solution is very general, and can be applied to system of any order or even system with time-delay. At the core of the solution is the initialization of the “total disturbance”.

For improving the dynamics of systems in the last decades model-based control design approaches are continuously developed. The task to design an accurate model is the most relevant and related task for control engineers, which is time consuming and difficult if in the case of complex nonlinear systems a complex modeling or identification problem arises. For this reason model-free control methods become attractive as alternative to avoid modeling. This contribution focuses on design methods of a model-free adaptive-based controller and modified model-free adaptive-based controller. Modified approach is based on the same adaptive model-free control algorithm performing tracking error optimization. Both approaches are designed for non-linear systems with uncertainties and in the presence of disturbances in order to assure suitable performance as well as robustness against unknown inputs. Using this approach, the controller requires neither the information about the systems dynamical structure nor the knowledge about systems physical behaviors. The task is solved using only the system outputs and inputs, which are measurable. The effectiveness of the proposed method is validated by experiments using a three-tank system.

Electricity has traditionally been a commodity that is bought and sold through a rigid marketplace between an electric utility and a ratepayer. Today, however, the electricity market is rapidly evolving to be comprised of distributed energy resources and microgrids that change the structure of the technical and financial relationship between utilities and ratepayers. Regulation, a reduction in cost of renewable energy technologies, interoperability and improved communications, and public interest in green power are facilitating this transition. Microgrids require an additional layer of control, often use preprogrammed rule sets, and lack bi-directional self-awareness, self-management, and self-diagnostics necessary to dynamically adapt to changes on-site and in the grid. Research is needed in optimization and controls. This study explores the viability of self-organizing control algorithms to manage multiple distributed energy resources within a distribution network and reduce electricity cost to one or more ratepayers having such resources installed on-site. Such research provides insight into the transition from a traditional power distribution architecture into a flexible smart network that is better prepared for future technological advances, renewables integration, and customer-side control. Agent-based techniques are employed for least-cost optimization and implements these to manage transactions between three decentralized distributed energy resource systems within an electrical network.

Regenerative braking of EV (electric vehicle) can enhance fuel efficiency to a great extent in urban areas. In addition, transmission plays a great role on the vehicle fuel economy and comfort and there are some research focus on the multi-speed transmission on EV. However, only limited number of scholars discussed about the influence of multi-speed transmission system on regenerative braking system. This paper focus on the effects of Electric Vehicle equipped with a Two-speed Uninterrupted Mechanical Transmission., which consist of a set of planetary gear, band brake and friction clutch. The transmission is capable of achieving seamless downshift which indicates that it can still transfer torque while downshifting. At the same time, as traction interruption of shift exerted an influence on the comfort during brake, this article put forward with a cooperative control algorithm considering the real response of electrohydraulic braking system in order to compensate the traction interruption and established an dynamic simulation model to testify the algorithm. The transmission dynamic model is developed by utilizing Euler-Lagrange equations to derive the motion while the other models are some simplified models. The whole model is applied by using the SimDriveline library of the MATLAB/Simulink. The simulation results of EV which commit a downshift while brake and the EV keep the gear are compared at the last demonstrate that the downshift strategy can save more energy without excessive oscillations.

There are two basic types of transmissions — manual and automatic. While manual transmissions have greater transmission efficiency and better overall driving experience, it is difficult and not as easy to handle as automatic transmissions. Automatic transmissions make life a lot easier for the driver but are less efficient and laggard. So, the aim of this project is to take the best of both worlds and combine it in one, i.e. a transmission system that is capable of working as both a manual and an automatic transmission. For this, we plan to automate a regular manual transmission with a manual override option on both the clutch and the gearbox systems. Motors will be used to control the clutch and the changing of gears in the gearbox. In the manual mode, the motors will be operated by user input switches (clutch pedal and stick shift). In the automatic mode, the same motors will be controlled by a control algorithm based on various inputs like weight of the car, inclination, speed of the engine and the car etc.

The dynamics equations of multibody systems are often expressed in the form of a system of highly nonlinear Differential Algebraic Equations (DAEs). Some applications of multibody dynamics, however, require a linear expression of the equations of motion. Such is the case of the plant representations demanded by a wide variety of control algorithms and the system models needed by state estimators like Kalman filters. The choice of generalized coordinates used to describe a mechanical system greatly influences the behavior of the resultant linearized models and the way in which they convey information about the original system dynamics. Several approaches to arrive at the linearized dynamics equations have been proposed in the literature. In this work, these were categorized into three major groups, defined by the way in which the kinematic constraints are handled. The properties of each approach and the differences between them were studied through the linearization of the dynamics of a simple example with a method representative of each class.

This paper presents the kinematics analysis of a three-fingered metamorphic hand (Metahand) and the LabView based control system of the robotic hand. In this study, the structure of the Metahand composed of an articulated palm and three fingers is introduced first. With the design parameters of the hybrid structure of the Metahand, analytical modeling of both forward and inverse kinematics is revealed, leading to reachable workspace and trajectory of all three fingertips. In particular, the analytical modeling of inverse kinematics is further verified by a numeric method. Following the theoretical investigation, a complete prototype with integrated servomotors and electric circuit for control system are produced. Further, a control system with LabView software is developed to realize the kinematics based control algorithm. A number of grasping tests are implemented to demonstrate the dexterity of the three-fingered Metahand.

A variable ratio gearbox (VRG) provides discrete variable rotor speed operation, and thus increases wind capture, for small fixed-speed wind turbines. It is a low-cost, reliable alternative to conventional variable speed operation, which requires special power-conditioning equipment. The authors’ previous work has demonstrated the benefit of using a VRG in a fixed-speed system with passive blades. This work characterizes the performance of the VRG when used with active blades. The main contribution of the study is an integrative design framework that maximizes power production while mitigating stress in the blade root. As part of the procedure, three gear ratios are selected for the VRG. It establishes the control rules by defining the gear ratio and pitch angle used in relation to wind speed and mechanical torque. A 300 kW wind turbine model is used for a case study that demonstrates how the framework is implemented. The model consists of aerodynamic, mechanical, and electrical submodels, which work collaboratively to convert kinetic air to electrical power. Blade element momentum theory is used in the aerodynamic model to compute the blade loads. The resulting torque is passed through a mechanical system and subsequently to the induction machine model to generate power. The BEM method also provides the thrust and bending loads that contribute to blade-root stress. The stress in the root of the blade is also computed in response to these loads, as well as those caused by gravity and centrifugal force. Two case studies are performed using wind data that was obtained from the National Renewable Energy Laboratory (NREL). Each of these represents an installation site with a unique set of wind conditions that are used to customize the wind turbine design. The framework uses dynamic programming to simulate the performance of an exhaustive set of combinations. Each combination is evaluated over each set of recorded wind data. The combinations are evaluated in terms of the total energy and stress that is produced over the simulation period. Weights are applied to a multi-objective cost function that identifies the optimal design configurations with respect to the design objectives. As a final design step, a VRG combination is selected, and a control algorithm is established for each set of wind data. During operation, the cost function can also be used to bias the system towards higher power production or lower stress. The results suggest a VRG can improve wind energy production in Region 2 by roughly 10% in both the low and high wind regions. In both cases, stress is also increased in Region 2, as the power increases. However, the stress in Region 3 may be reduced for some wind speeds through the optimal selection of gear combinations.