Energy-Balanced Leader-Switching Policy for Formation Rotation Control of Multi-Agent Systems Inspired by Bird Flocks

This paper addresses an energy-balanced leader-switching policy for formation rotation control of multi-agent systems inspired by bird flocks. Birds that flock in V-formation with a leader rotation strategy are able to travel longer distances due to reduced drag and therefore less energy expenditure. This flocking behavior with a leader rotation will result in more conservation of overall energy and will be particularly beneficial to migrating birds that should fly long distances without landing. In this paper, we propose an energy-balanced leader-switching policy inspired by this bird flocking behavior in order to increase the flight range for multi-agent systems. The formation control of multi-agent systems is achieved by the consensus algorithm, which is fully decentralized through the use of information exchanges between agents. The proposed leader-switching method is not necessarily incorporated with the consensus dynamics and thus, the leader-switching algorithm can be decoupled from formation control dynamics. Therefore, the proposed method can simplify the leader-switching algorithm, making it easy to implement. Moreover, we propose the analytic flight distance based on the energy consumption model for each agent. To test the validity of the developed method, several simulation results are presented.

A Numerical Study of an Electrically Assisted Boosting System for Turbocharged Diesel Engines

Modern diesel engines are normally turbocharged in order to achieve desired fuel economy and meet emission requirements. The well-known “turbo-lag”, delayed engine torque response to driver’s demand, is the main disadvantage for turbocharged engines operated under transient conditions. In addition, at low engine speed, the peak engine output torque is heavily limited by the available turbine energy. As a result, turbocharged engines have degraded peak torque at low speed and slow transient responses in general. Various technologies (variable geometry turbine, electrically assisted turbocharger, hydraulically assisted turbocharger, etc.) have been developed to improve transient response and low-speed torque performance. This paper presents a numerical study of an electrically assisted boosting (eBoost) system for a turbocharged diesel engine through 1-D simulations. This study focuses on two main areas: the electrical compensation at steady-state and turbo-lag reduction under transient operation. It is shown that the eBoost system is capable of increasing engine fuel economy at mid-speed and greatly improving low-speed peak torque. In addition, the eBoost system improves engine transient performance by reducing response time up to 60%.

Engine Calibration Optimization Based on its Surrogate Models

Diesel engines are becoming increasingly complex to control and calibrate with the desire of improving fuel economy and reducing emissions (NOx and Soot) due to global warming and energy usage. With ever increased control features, it is becoming more and more difficult to calibrate engine control parameters using the traditional engine mapping based methods due to unreasonable calibration time required. Therefore, this research focuses on the problem of performing engine calibration within a limited budget by efficiently optimizing three control parameters: namely variable geometry turbocharger (VGT) position, exhaust gas recirculation (EGR) valve position, and start of injection (SOI). Engine performance in terms of fuel consumption (BSFC) and emissions (NOX) are considered as objective function here with the constraint on boost pressure and engine load (BMEP). Since the engine calibration process requires a large number of high-fidelity evaluations, surrogate modeling methods are used to perform calibration quickly with a significantly reduced computational budget. Kriging metamodeling is used for this work with Expected Improvement (EI) as acquisition function. Results show more than 60% decrease in computational cost with results close to actual near Pareto optimal set.

Model Predictive Control of Micro-CSP Integrated Into a Building HVAC System for Load Following Demand Response Programs

Building heat, ventilation and air conditioning (HVAC) systems are good candidates for demand response (DR) programs as they can flexibly alter their consumption to provide ancillary services to the grid and contribute to frequency and voltage regulation. One of the major ancillary services is the load following demand response (DR) program where the demand side tries to track a DR load profile required by the grid. This paper presents a real-time Model Predictive Control (MPC) framework for optimal operations of a micro-scale concentrated solar power (MicroCSP) system integrated into an office building HVAC system providing ancillary services to the grid. To decrease the energy cost of the building, the designed MPC exploits, along with the flexibility of the building’s HVAC system, the dispatching capabilities of the MicroCSP with thermal energy storage (TES) in order to control the power flow in the building and respond to the DR incentives sent by the grid. The results show the effect of incentives in the building participation to the load following DR program in the presence of a MicroCSP system and to what extent this participation is affected by seasonal weather variations and dynamic pricing.

Control-Oriented Physics-Based NOx Emission Model for a Diesel Engine With Exhaust Gas Recirculation

Publisher’s Note: This paper was selected for publication in ASME Letters in Dynamic Systems and Control. https://www.asmedigitalcollection.asme.org/lettersdynsys/article/doi/10.1115/1.4046450/1074858/Control-Oriented-Physics-Based-NOX-Emission-Model

A Real-Time Pressure Wave Model for Predicting Engine Knock

This paper proposes a control-oriented pressure wave model, utilizing outputs of a reaction-based two-zone engine combustion model developed earlier, to accurately predict the key knock characteristics. The model can be used for model-based knock prediction and control. An in-cylinder pressure wave model of oscillation magnitude decay is proposed and simplified to describe pressure oscillations due to knock combustion, and the boundary and initial conditions of the pressure wave model at knock onset are provided by the two-zone reaction-based combustion model. The proposed pressure wave model is calibrated using experimental data, and the chemical kinetic-based Arrhenius integral (ARI) and maximum amplitude of pressure oscillations (MAPO) are used as the evaluation criteria for predicting knock onset and intensity, and the knock frequency is studied with the fast Fourier transform (FFT). The calibrated model is validated for predicting knock onset timing, knock intensity and frequency. Simulation results are compared with the experimental ones to demonstrate the capability of predicting engine knock characteristics by the proposed model.

Towards Cooperative Transportation of Multiple Mecanum-Wheeled Automated Guided Vehicles

Utilizing multiple small-sized automated guided vehicles (AGVs) in cooperatively transport large and heavy objects in manufacturing factories or logistics is an emerging research direction. Flexibility and efficiency can be enhanced by using multi-AGV comparing to a large AGV with higher capacity especially in clutter environments. In this paper, a multi-AGV system by using Mecanum wheels to provide omnidirectional movement is proposed for cooperative transportation. Accordingly, the proposed Mecanum-wheeled automated guided vehicles (MWAGVs) composed of Mecanum wheels and a rotary platform provides not only non-constrained movement but also planar displacement for allowance of distance errors. In the proposed MWAGVs, the formation control with fixed geometry during operation is significant especially with unknown object information, dynamic uncertainties, and external disturbances. Therefore, the passivity-based adaptive synchronizing control algorithm is developed to ensure stability and tracking performance with uncertain dynamic parameters. Simulations and Experiments show the efficacy of designed Mecanum-wheeled AGV.

Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator

In this paper, we examine the autonomous operation of a high-DOF robot manipulator. We investigate a pick-and-place task where the position and orientation of an object, an obstacle, and a target pad are initially unknown and need to be autonomously determined. In order to complete this task, we employ a combination of computer vision, deep learning, and control techniques. First, we locate the center of each item in two captured images utilizing HSV-based scanning. Second, we utilize stereo vision techniques to determine the 3D position of each item. Third, we implement a Convolutional Neural Network in order to determine the orientation of the object. Finally, we use the calculated 3D positions of each item to establish an obstacle avoidance trajectory lifting the object over the obstacle and onto the target pad. Through the results of our research, we demonstrate that our combination of techniques has minimal error, is capable of running in real-time, and is able to reliably perform the task. Thus, we demonstrate that through the combination of specialized autonomous techniques, generalization to a complex autonomous task is possible.

Modeling and Control of Wheel Motor Based Articulated Vehicle System

This paper investigates an effectiveness of vehicle dynamic control (VDC) system based on torque vectoring technique using in-wheel-motors to improve the performance of articulated vehicle systems. A 10 degree-of-freedom (DOF) articulated vehicle model including a tractor and a single axle trailer has been developed and its responses are validated with commercial vehicle software of Trucksim. This model includes a nonlinear tire model (MF tire), a hydraulic damping at the hitch, and a traction system using in-wheel-motors at the trailer axle. In this paper, a yaw control system is developed to track the reference yaw rate with application of yaw moment at the trailer axle using torque distribution of in-wheel-motors. The effectiveness of the proposed control system is validated through simulation of sinusoidal steering maneuver on high mu and slippery road conditions. The simulation results show that in-wheel-motors can improve safety and performance of articulate vehicle systems.

Modeling the Gas Exchange Processes of a Modern Diesel Engine With an Integrated Physics-Based and Data-Driven Approach

The gas exchange processes of engines are becoming increasingly complex since modern engines leverage technologies including variable valve actuation, turbochargers, and exhaust gas recirculation. Control of these many devices and the underlying gas flows is essential for high efficiency engine concepts. If these processes are to be controlled and estimated using model-based techniques, accurate models are required. This work explores a model framework that leverages a data-driven model of the turbocharger along with submodels of the intercooler, intake and exhaust manifolds and engine processes to provide cylinder-specific predictions of the pressure and temperatures of the gases across the system. This model is developed and validated using data from a 2.0 liter VW turbocharged, direct-injection diesel engine and shown to provide accurate prediction of critical gas properties.