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Articles 1 - 16 of 16
Full-Text Articles in Mechanical Engineering
Neuroevolution Application To Collaborative And Heuristics-Based Connected And Autonomous Vehicle Cohort Simulation At Uncontrolled Intersection, Frederic Jacquelin, Jungyun Bae, Bo Chen, Darrell Robinette
Neuroevolution Application To Collaborative And Heuristics-Based Connected And Autonomous Vehicle Cohort Simulation At Uncontrolled Intersection, Frederic Jacquelin, Jungyun Bae, Bo Chen, Darrell Robinette
Michigan Tech Publications, Part 2
Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation’s simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go …
Real-Time Suitable Predictive Control Using Spat Information From Automated Traffic Lights, Pradeep Bhat, Bo Chen
Real-Time Suitable Predictive Control Using Spat Information From Automated Traffic Lights, Pradeep Bhat, Bo Chen
Michigan Tech Publications, Part 2
Traffic intersections throughout the United States combine fixed, semi-actuated, and fully actuated intersections. In the case of the semi-actuated and actuated intersections, uncertainties are considered in phase duration. These uncertainties are due to car waiting queues and pedestrian crossing. Intelligent transportation systems deployed in traffic infrastructure can communicate Signal and Phase Timing messages (SPaT) to vehicles approaching intersections. In the connected and automated vehicle ecosystem, the fuel savings potential has been explored. Prior studies have predominantly focused on fixed time control for the driver. However, in the case of actuated signals, there is a different and significant challenge due to …
Antagonistic Co-Contraction Can Minimize Muscular Effort In Systems With Uncertainty, Anne D. Koelewijn, Antonie J. Van Den Bogert
Antagonistic Co-Contraction Can Minimize Muscular Effort In Systems With Uncertainty, Anne D. Koelewijn, Antonie J. Van Den Bogert
Mechanical Engineering Faculty Publications
Muscular co-contraction of antagonistic muscle pairs is often observed in human movement, but it is considered inefficient and it can currently not be predicted in
simulations where muscular effort or metabolic energy are minimized. Here, we investigated the relationship between minimizing effort and muscular co-contraction
in systems with random uncertainty to see if muscular co-contraction can minimize effort in such system. We also investigated the effect of time delay in the muscle, by varying the time delay in the neural control as well as the activation time constant.We solved optimal control problems for a one-degree-of-freedom pendulum actuated by two identical …
Optimal Tracking In Switched Systems With Free Final Time And Fixed Mode Sequence Using Approximate Dynamic Programming, Tohid Sardarmehni, Xingyong Song
Optimal Tracking In Switched Systems With Free Final Time And Fixed Mode Sequence Using Approximate Dynamic Programming, Tohid Sardarmehni, Xingyong Song
Mechanical Engineering Faculty Publications and Presentations
Optimal tracking in switched systems with fixed mode sequence and free final time is studied in this article. In the optimal control problem formulation, the switching times and the final time are treated as parameters. For solving the optimal control problem, approximate dynamic programming (ADP) is used. The ADP solution uses an inner loop to converge to the optimal policy at each time step. In order to decrease the computational burden of the solution, a new method is introduced, which uses evolving suboptimal policies (not the optimal policies), to learn the optimal solution. The effectiveness of the proposed solutions is …
Centralized And Decentralized Optimal Control Of Variable Speed Heat Pumps, Ryan S. Montrose, John F. Gardner, Aykut C. Satici
Centralized And Decentralized Optimal Control Of Variable Speed Heat Pumps, Ryan S. Montrose, John F. Gardner, Aykut C. Satici
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, as a result of naturally occurring and demand-response events, has the potential to damage power distribution equipment. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this paper is the development of an optimal control algorithm for commonly used variable speed heat pumps. By means of selective peer-to-peer communication, our control architecture allows for the regulation of home …
Cnn-Based Estimation Of Sagittal Plane Walking And Running Biomechanics From Measured And Simulated Inertial Sensor Data, Eva Dorschky, Marlies Nitschke, Christine F. Martindale, Antonie J. Van Den Bogert, Anne D. Koelewijn, Bjoern M. Eskofier
Cnn-Based Estimation Of Sagittal Plane Walking And Running Biomechanics From Measured And Simulated Inertial Sensor Data, Eva Dorschky, Marlies Nitschke, Christine F. Martindale, Antonie J. Van Den Bogert, Anne D. Koelewijn, Bjoern M. Eskofier
Mechanical Engineering Faculty Publications
Machine learning is a promising approach to evaluate human movement based on wearable sensor data. A representative dataset for training data-driven models is crucial to ensure that the model generalizes well to unseen data. However, the acquisition of sufficient data is time-consuming and often infeasible. We present a method to create realistic inertial sensor data with corresponding biomechanical variables by 2D walking and running simulations. We augmented a measured inertial sensor dataset with simulated data for the training of convolutional neural networks to estimate sagittal plane joint angles, joint moments, and ground reaction forces (GRFs) of walking and running. When …
Optimal And Decentralized Control Strategies For Inverter-Based Ac Microgrids, Michael D. Cook, Eddy H. Trinklein, Gordon Parker, Rush D. Robinett Iii, Wayne Weaver
Optimal And Decentralized Control Strategies For Inverter-Based Ac Microgrids, Michael D. Cook, Eddy H. Trinklein, Gordon Parker, Rush D. Robinett Iii, Wayne Weaver
Michigan Tech Publications
This paper presents two control strategies: (i) An optimal exergy destruction (OXD) controller and (ii) a decentralized power apportionment (DPA) controller. The OXD controller is an analytical, closed-loop optimal feedforward controller developed utilizing exergy analysis to minimize exergy destruction in an AC inverter microgrid. The OXD controller requires a star or fully connected topology, whereas the DPA operates with no communication among the inverters. The DPA presents a viable alternative to conventional P−ω/Q−V droop control, and does not suffer from fluctuations in bus frequency or steady-state voltage while taking advantage of distributed storage assets necessary for the high penetration of …
Docking Control Of An Autonomous Underwater Vehicle Using Reinforcement Learning, Enrico Anderlini, Gordon Parker, Giles Thomas
Docking Control Of An Autonomous Underwater Vehicle Using Reinforcement Learning, Enrico Anderlini, Gordon Parker, Giles Thomas
Michigan Tech Publications
To achieve persistent systems in the future, autonomous underwater vehicles (AUVs) will need to autonomously dock onto a charging station. Here, reinforcement learning strategies were applied for the first time to control the docking of an AUV onto a fixed platform in a simulation environment. Two reinforcement learning schemes were investigated: one with continuous state and action spaces, deep deterministic policy gradient (DDPG), and one with continuous state but discrete action spaces, deep Q network (DQN). For DQN, the discrete actions were selected as step changes in the control input signals. The performance of the reinforcement learning strategies was compared …
Opty: Software For Trajectory Optimization And Parameter Identification Using Direct Collocation, Jason K. Moore, Antonie J. Van Den Bogert
Opty: Software For Trajectory Optimization And Parameter Identification Using Direct Collocation, Jason K. Moore, Antonie J. Van Den Bogert
Mechanical Engineering Faculty Publications
opty is a tool for describing and solving trajectory optimization and parameter identification problems based on symbolic descriptions of ordinary differential equations and differential algebraic equations that describe a dynamical system. The motivation for its development resides in the need to solve optimal control problems of biomechanical systems. The target audience is engineers and scientists interested in solving nonlinear optimal control and parameter identification problems with minimal computational overhead.
Predictive Musculoskeletal Simulation Using Optimal Control: Effects Of Added Limb Mass On Energy Cost And Kinematics Of Walking And Running, Antonie J. Van Den Bogert, Maarten Hupperets, Heiko Schlarb, Berthold Krabbe
Predictive Musculoskeletal Simulation Using Optimal Control: Effects Of Added Limb Mass On Energy Cost And Kinematics Of Walking And Running, Antonie J. Van Den Bogert, Maarten Hupperets, Heiko Schlarb, Berthold Krabbe
Mechanical Engineering Faculty Publications
When designing sports equipment, it is often desirable to predict how certain design parameters will affect human performance. In many instances, this requires a consideration of human musculoskeletal mechanics and adaptive neuromuscular control. Current computational methods do not represent these mechanisms, and design optimization typically requires several iterations of prototyping and human testing. This paper introduces a computational method based on musculoskeletal modeling and optimal control, which has the capability to predict the effect of mechanical equipment properties on human performance. The underlying assumption is that users will adapt their neuromuscular control according to an optimality principle, which balances task …
Predictive Simulation Of Gait At Low Gravity Reveals Skipping As The Preferred Locomotion Strategy, Marko Ackermann, Antonie J. Van Den Bogert
Predictive Simulation Of Gait At Low Gravity Reveals Skipping As The Preferred Locomotion Strategy, Marko Ackermann, Antonie J. Van Den Bogert
Mechanical Engineering Faculty Publications
The investigation of gait strategies at low gravity environments gained momentum recently as manned missions to the Moon and to Mars are reconsidered. Although reports by astronauts of the Apollo missions indicate alternative gait strategies might be favored on the Moon, computational simulations and experimental investigations have been almost exclusively limited to the study of either walking or running, the locomotion modes preferred under Earth's gravity. In order to investigate the gait strategies likely to be favored at low gravity a series of predictive, computational simulations of gait are performed using a physiological model of the musculoskeletal system, without assuming …
Implicit Methods For Efficient Musculoskeletal Simulation And Optimal Control, Antonie J. Van Den Bogert, Dimitra Blana, Dieter Heinrich
Implicit Methods For Efficient Musculoskeletal Simulation And Optimal Control, Antonie J. Van Den Bogert, Dimitra Blana, Dieter Heinrich
Mechanical Engineering Faculty Publications
The ordinary differential equations for musculoskeletal dynamics are often numerically stiff and highly nonlinear. Consequently, simulations require small time steps, and optimal control problems are slow to solve and have poor convergence. In this paper, we present an implicit formulation of musculoskeletal dynamics, which leads to new numerical methods for simulation and optimal control, with the expectation that we can mitigate some of these problems. A first order Rosenbrock method was developed for solving forward dynamic problems using the implicit formulation. It was used to perform real-time dynamic simulation of a complex shoulder arm system with extreme dynamic stiffness. Simulations …
Optimality Principles For Model-Based Prediction Of Human Gait, Marko Ackermann, Antonie J. Van Den Bogert
Optimality Principles For Model-Based Prediction Of Human Gait, Marko Ackermann, Antonie J. Van Den Bogert
Mechanical Engineering Faculty Publications
Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient's gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like …
Integrated Guidance And Control Of Missiles With Θ-D Method, Ming Xin, S. N. Balakrishnan, Ernest J. Ohlmeyer
Integrated Guidance And Control Of Missiles With Θ-D Method, Ming Xin, S. N. Balakrishnan, Ernest J. Ohlmeyer
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A new suboptimal control method is proposed in this study to effectively design an integrated guidance and control system for missiles. Optimal formulations allow designers to bring together concerns about guidance law performance and autopilot responses under one unified framework. They lead to a natural integration of these different functions. by modifying the appropriate cost functions, different responses, control saturations (autopilot related), miss distance (guidance related), etc., which are of primary concern to a missile system designer, can be easily studied. A new suboptimal control method, called the θ-D method, is employed to obtain an approximate closed-form solution to this …
Optimal Design Of Low Order Controllers Satisfying Sensitivity And Robustness Constraint, Mark L. Nagurka, O. Yaniv
Optimal Design Of Low Order Controllers Satisfying Sensitivity And Robustness Constraint, Mark L. Nagurka, O. Yaniv
Mechanical Engineering Faculty Research and Publications
The set of all stabilizing controllers of a given low order structure that guarantee specifications on the gain margin, phase margin and a bound on the sensitivity corresponds to a region in n-dimensional space defined by the coefficients of the controllers. For several practical criteria defined in the paper it is shown that the optimal controller lies on the surface of that region. Moreover, it is shown how to reduce that region to avoid actuator saturation during operation.
Nonlinear Optimal Control Design Of A Missile Autopilot, Tim Mclain, Randal W. Beard
Nonlinear Optimal Control Design Of A Missile Autopilot, Tim Mclain, Randal W. Beard
Faculty Publications
The application of a new nonlinear optimal control strategy to the design of missile autopilots is presented. The control approach described and demonstrated here is based upon the numerical solution of the Hamilton-Jacobi-Bellman equation by Successive Galerkin Approximation. Using this approach, feedback controllers are computed by an iterative application of a numerical Galerkin-type PDE solver. Simulation results demonstrating the application of this approach to the design of a missile autopilot are presented.