Optimal workplacement for robotic friction stir welding task

Robotic manipulators are widely used in industry for welding processes. Inadequate joint stiffness in the manipulators often limits their use for high quality welding operations because of the deformation errors produced during the process. As a matter of fact, welding quality deteriorates with decreasing joint stiffness. This paper presents an approach to determine an optimal workspace of operation by minimizing the lateral deflection errors in position and orientation of the end effector during Friction Stir Welding. This has been done by estimating the errors in position and orientation of the end effector, also the point of contact with work piece which directly affects welding quality, when it experiences a wrench during welding operation. The technique was applied to an elastodynamic model of a 6 DOF manipulator with different path constraints for welding process to achieve optimal task placement. In a nutshell, optimal starting position or an optimal direction of motion for best welding quality can be precisely computed or even both together can be calculated but with numerical complexity.ANR COROUSS

Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems

Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. Current DR approaches are completely manual and rule-based or in- volve deriving first principles based models which are ex- tremely cost and time prohibitive to build. We consider the problem of data-driven end-user DR for large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies and synthesizing DR control actions. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed- loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm out- performs rule-based DR by 17% for a large DoE commercial reference building and leads to a curtailment of 380kW and over $45, 000 in savings. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. DR-Advisor achieves 92.8% to 98.9% pre- diction accuracy for 8 buildings on Penn’s campus. We com- pare DR-Advisor with other data driven methods and rank 2nd on ASHRAE’s benchmarking data-set for energy predic- tion

Methods For Data-Driven Model Predictive Control

Model predictive control (MPC) is essential to optimal decision making in a broad range of applications like building energy management and autonomous racing. MPC provides significant energy cost savings in building operations in the form of energy-efficient control with better occupant comfort, lower peak demand charges, and risk-free participation in demand response. In autonomous racing, MPC computes a safe minimum-time trajectory while driving at the limit of a vehicle’s handling capability. However, the ease in controller design depends upon the modeling complexity of the underlying physical system. For example, the identification of physics-based models of buildings is considered to be the biggest bottleneck in making MPC scalable to real buildings due to massive engineering effort. Thus, the traditional modeling approaches like the white-box and the grey-box techniques, although detailed, are considered cost and time prohibitive. In the case of autonomous racing, one of the fundamental challenges lies in predicting the vehicle’s future states like position, orientation, and speed with high accuracy because it is inevitably hard to identify vehicle model parameters that capture its real nonlinear dynamics in the presence of lateral tire slip. To this end, we present methods for data-driven MPC that combine predictive control and tools from machine learning such as Gaussian processes, neural networks, and random forests to reduce the cost of model identification and controller design in these applications. First, we introduce learning and control algorithms for building energy management based on black-box modeling that require minimum external intervention and solve some of the fundamental practical challenges ranging from experiment design to predictive control to online model update. We learn dynamical models of energy consumption and zone temperatures with high accuracy, and demonstrate load curtailment during demand response, energy savings during regular operations, and better occupant comfort compared to the default system controller. We validate our methods on several buildings in different case studies, including a real house in Italy. Next, we present a model-based planning and control framework for autonomous racing based on discrepancy error modeling that significantly reduces the effort required in system identification of the vehicle model. We start with an easy-to-tune but inaccurate physics-based model of the vehicle dynamics and thereafter correct the model predictions by learning from prior experience. Our approach bridges the gap between the design in a simulation and the real world by learning from on-board sensor measurements. We demonstrate its efficacy on a 1/43 scale autonomous racing simulation platform

Data Predictive Control using Regression Trees and Ensemble Learning

Decisions on how to best operate large complex plants such as natural gas processing, oil refineries, and energy efficient buildings are becoming ever so complex that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC, is the cost, time, and effort associated with learning first-principles dynamical models of the underlying physical system. An alternative approach is to employ learning algorithms to build black-box models which rely only on real-time data from the sensors. Machine learning is widely used for regression and classification, but thus far data-driven models have not been used for closed-loop control. We present novel Data Predictive Control (DPC) algorithms that use Regression Trees and Random Forests for receding horizon control. We demonstrate the strength of our approach with a case study on a bilinear building model identified using real weather data and sensor measurements. In a one-to-one comparison, we show that DPC explains 70\% variation in the MPC controller. We further apply DPC to a large scale multi-story EnergyPlus building model to curtail total power consumption in a Demand Response setting. In such cases, when the model-based controllers fail due to modeling cost, complexity and scalability, our results show that DPC curtails the desired power usage with high confidence

Learning and Control using Gaussian Processes

Building physics-based models of complex physical systems like buildings and chemical plants is extremely cost and time prohibitive for applications such as real-time optimal control, production planning and supply chain logistics. Machine learning algorithms can reduce this cost and time complexity, and are, consequently, more scalable for large-scale physical systems. However, there are many practical challenges that must be addressed before employing machine learning for closed-loop control. This paper proposes the use of Gaussian Processes (GP) for learning control-oriented models: (1) We develop methods for the optimal experiment design (OED) of functional tests to learn models of a physical system, subject to stringent operational constraints and limited availability of the system. Using a Bayesian approach with GP, our methods seek to select the most informative data for optimally updating an existing model. (2) We also show that black-box GP models can be used for receding horizon optimal control with probabilistic guarantees on constraint satisfaction through chance constraints. (3) We further propose an online method for continuously improving the GP model in closed-loop with a real-time controller. Our methods are demonstrated and validated in a case study of building energy control and Demand Response

Data-driven Switched Affine Modeling for Model Predictive Control

Model Predictive Control (MPC) is a well-consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict the system’s behavior over a predictive horizon. However, building physics-based models for large-scale systems, such as buildings and process control, can be cost and time prohibitive. To overcome this problem we propose in this paper a methodology to exploit machine learning techniques (i.e. regression trees and random forests) in order to build a state-space switched affine dynamical model of a large scale system only using historical data. Finite Receding Horizon Control (RHC) setup using control-oriented data-driven models based on regression trees and random forests is presented as well. A comparison with an optimal MPC benchmark and a related methodology is provided on an energy management system to show the performance of the proposed modeling framework. Simulation results show that the proposed approach is very close to the optimum and provides better performance with respect to the related methodology in terms of cost function optimization

Data-driven model predictive control using random forests for building energy optimization and climate control

Model Predictive Control (MPC) is a model-based technique widely and successfully used over the past years to improve control systems performance. A key factor prohibiting the widespread adoption of MPC for complex systems such as buildings is related to the difficulties (cost, time and effort) associated with the identification of a predictive model of a building. To overcome this problem, we introduce a novel idea for predictive control based on historical building data leveraging machine learning algorithms like regression trees and random forests. We call this approach Data-driven model Predictive Control (DPC), and we apply it to three different case studies to demonstrate its performance, scalability, and robustness. In the first case study we consider a benchmark MPC controller using a bilinear building model, then we apply DPC to a data-set simulated from such bilinear model and derive a controller based only on the data. Our results demonstrate that DPC can provide comparable performance with respect to MPC applied to a perfectly known mathematical model. In the second case study, we apply DPC to a 6 story 22 zone building model in EnergyPlus, for which model-based control is not economical and practical due to extreme complexity, and address a Demand Response problem. Our results demonstrate scalability and efficiency of DPC showing that DPC provides the desired power curtailment with an average error of 3%. In the third case study, we implement and test DPC on real data from an off-grid house located in L’Aquila, Italy. We compare the total amount of energy saved with respect to the classical bang-bang controller, showing that we can perform an energy saving up to 49.2%. Our results demonstrate the robustness of our method to uncertainties both in real data acquisition and weather forecast