Mechanical Engineering Commons^™

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

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 …

Near-Optimal Control Of A Quadcopter Using Reinforcement Learning, Alberto Velazquez-Estrada

Theses and Dissertations

This paper presents a novel control method for quadcopters that achieves near-optimal tracking control for input-affine nonlinear quadcopter dynamics. The method uses a reinforcement learning algorithm called Single Network Adaptive Critics (SNAC), which approximates a solution to the discrete-time Hamilton-Jacobi-Bellman (DT-HJB) equation using a single neural network trained offline. The control method involves two SNAC controllers, with the outer loop controlling the linear position and velocities (position control) and the inner loop controlling the angular position and velocities (attitude control). The resulting quadcopter controller provides optimal feedback control and tracks a trajectory for an infinite-horizon, and it is compared with …

Using Actor-Critic Reinforcement Learning For Control Of A Quadrotor Dynamics, Edgar Adrian Torres

Theses and Dissertations

This paper presents a quadrotor controller using reinforcement learning to generate near-optimal control signals. Two actor-critic algorithms are trained to control quadrotor dynamics. The dynamics are further simplified using small angle approximation. The actor-critic algorithm’s control policy is derived from Bellman’s equation providing a sufficient condition to optimality. Additionally, a smoother converter is implemented into the trajectory providing more reliable results. This paper provides derivations to the quadrotor’s dynamics and explains the control using the actor-critic algorithm. The results and simulations are compared to solutions from a commercial, optimal control solver, called DIDO.

Advances In Control Of Uncertain Dynamical Systems With Optimal And Adaptive Approaches, Meryem Deniz

Doctoral Dissertations

"This research starts with designing optimal control for uncertain systems, adding the adaptive control input to suppress the uncertainty. It then follows with designing the optimal control for state constraint, designing the optimal nonlinear reference model, designing adaptive control to handle state constraint and uncertainty. Finally, it designs the finite-time controller for uncertain multiagent systems.

It is well known that the design of the control algorithm for an uncertain dynamical system is not trivial. Motivated by this standpoint, this study focuses on optimal and adaptive control approaches with stability and performance guarantees for uncertain sole and multiagent dynamical systems. From …

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 …

Flutter Suppression By Active Controller Of A Two-Dimensional Wing With A Flap, Abdallah Tarabulsi

Theses

Flutter is a divergent oscillation of an aeroelastic structure, and one of a family of aeroelastic instability phenomena, that results from the interaction of elastic and inertial forces of the structure with the surrounding aerodynamic forces. Airfoil Flutter is important due to its catastrophic effect on the durability and operational safety of the structure. Traditionally, flutter is prevented within an aircraft's flight envelope using passive approaches such as optimizing stiffness distribution, mass balancing, or modifying geometry during the design phase. Although these methods are effective but they led to heavier airfoil designs. On the other hand, active control methods allow …

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

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 …

Optimization Of Shape And Control Of Linear And Nonlinear Wave Energy Converters, Jiajun Song

Dissertations, Master's Theses and Master's Reports

In this dissertation, we address the optimal control and shape optimization of Wave Energy Converters. The wave energy converters considered in this study are the single-body heaving wave energy converters, and the two-body heaving wave energy converters. Different types of wave energy converters are modeled mathematically, and different optimal controls are developed for them. The concept of shape optimization is introduced in this dissertation; the goal is to leverage nonlinear hydrodynamic forces which are dependent on the buoy shape. In this dissertation, shape optimization is carried out and its impact on energy extraction is investigated. In all the studies conducted …

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

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

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 …

Closed Loop Energy Maximizing Control Of A Wave Energy Converter Using An Estimated Linear Model That Approximates The Nonlinear Froude-Krylov Force, Yaqzan Mohd Yaqzan

Dissertations, Master's Theses and Master's Reports

Wave energy converters (WECs) exploit ocean wave energy and convert it into useful forms such as electricity. But for WECs to be successful on a large scale, two primary conditions need to be satisfied. The energy generated must satisfy the network requirements, and second, energy flow from waves to the grid needs to be maximized. In this dissertation, we address the second problem. Most control techniques for WECs today use the Cummins' linear model to simulate WEC hydrodynamics. However, it has been shown that under the application of a control force, where WEC motions are amplified, the linear model diverges …

Optimization And Control Of Arrays Of Wave Energy Converters, Jianyang Lyu

Dissertations, Master's Theses and Master's Reports

Wave Energy Converter Array is a practical approach to harvest ocean wave energy. To leverage the potential of the WEC array in terms of energy extraction, it is essential to have a properly designed array configuration and control system. This thesis explores the optimal configuration of Wave Energy Converters (WECs) arrays and their optimal control. The optimization of the WEC array allows both dimensions of individual WECs as well as the array layout to varying. In the first optimization problem, cylindrical buoys are assumed in the array where their radii and drafts are optimization parameters. Genetic Algorithms are used for …

Forward-Looking, Velocity-Driven, Powertrain Modeling And Optimal Control For Continuous Variable Transmission, Paresh Deshmukh

Masters Theses

This thesis is the second part of a two-part study focused on improving Continuous Variable Transmission (CVT) ratio management and control. The objective of the overall project was to develop a methodology for a vehicle with a CVT and a downsized gasoline engine to deliver the maximum vehicle fuel economy within drivability and performance constraints. The first part of this study, as described in [1], focuses on developing a cycle driven model for optimizing the CVT ratio. The study presented in this paper focuses on developing a velocity driven model to simulate the real-time behavior of a vehicle. The results …

Adaptive Control For Inflatable Soft Robotic Manipulators With Unknown Payloads, Jonathan Spencer Terry

Theses and Dissertations

Soft robotic platforms are becoming increasingly popular as they are generally safer, lighter, and easier to manufacture than their more rigid, heavy, traditional counterparts. These soft platforms, while inherently safer, come with significant drawbacks. Their compliant components are more difficult to model, and their underdamped nature makes them difficult to control. Additionally, they are so lightweight that a payload of just a few pounds has a significant impact on the manipulator dynamics. This thesis presents novel methods for addressing these issues. In previous research, Model Predictive Control has been demonstrably useful for joint angle control for these soft robots, using …

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.

Position And Stiffness Control Of Inflatable Robotic Links Using Rotary Pneumatic Actuation, Charles Mansel Best

Theses and Dissertations

Inflatable robots with pneumatic actuation are naturally lightweight and compliant. Both of these characteristics make a robot of this type better suited for human environments where unintentional impacts will occur. The dynamics of an inflatable robot are complex and dynamic models that explicitly allow variable stiffness control have not been well developed. In this thesis, a dynamic model was developed for an antagonistic, pneumatically actuated joint with inflatable links.The antagonistic nature of the joint allows for the control of two states, primarily joint position and stiffness. First a model was developed to describe the position states. The model was used …

Implementing A Linear Quadratic Spacecraft Attitude Control System, Daniel Kolosa

Masters Theses

This thesis implements a linear quadratic attitude control system for a low-thrust spacecraft. The goal is to maintain spacecraft alignment with a time-varying thrust vector needed for trajectory change maneuvers. A linear quadratic attitude control approach is used to maintain spacecraft pointing throughout flight. This attitude control strategy uses the thrust-acceleration input obtained from a linear quadratic optimal trajectory control model that simulates the trajectory of a spacecraft in orbit maneuvers. This attitude model simulates a CubeSat, a small satellite that is equipped with a low-thrust propulsion and attitude control system. An orbit raising and a plane change scenario is …

Optimal Control Of Unknown Nonlinear System From Inputoutput Data, Xin Jin

Open Access Theses

Optimal control designers usually require a plant model to design a controller. The problem is the controller's performance heavily depends on the accuracy of the plant model. However, in many situations, it is very time-consuming to implement the system identification procedure and an accurate structure of a plant model is very difficult to obtain. On the other hand, neuro-fuzzy models with product inference engine, singleton fuzzifier, center average defuzzifier, and Gaussian membership functions can be easily trained by many well-established learning algorithms based on given input-output data pairs. Therefore, this kind of model is used in the current optimal controller …

The Optimal Control Of A Flexible Hull Robotic Undersea Vehicle Propelled By An Oscillating Foil, David Barrett, Mark Grosenbaugh, Michael Triantafyllou

David Barrett

Determining the optimal swimming motion for a flexible hull robotic undersea vehicle propelled by an oscillating foil is an acutely complex problem involving the vehicle's body kinematics and the hydrodynamics of the surrounding water. The overall intractability of the hydrodynamics of a flexible body precludes a purely analytical solution. The immense size of the experimental variable space prevents a purely empirical one. In order to overcome both difficulties, we have developed a self-optimizing motion controller based on a genetic algorithm. This controller effectively uses evolutionary principles to exponentially optimize swimming performance.

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

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

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

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 …

Trajectory Shaping Of Surface-To-Surface Missile With Terminal Impact Angle Constraint, S. Subchan

Makara Journal of Technology

This paper presents trajectory shaping of a surface-to-surface missile attacking a fixed with terminal impact angle constraint. The missile must hit the target from above, subject to the missile dynamics and path constraints. The problem is reinterpreted using optimal control theory resulting in the formulation of minimum integrated altitude. The formulation entails nonlinear, two-dimensional missile flight dynamics, boundary conditions and path constraints. The generic shape of optimal trajectory is: level flight, climbing, diving; this combination of the three flight phases is called the bunt manoeuvre. The numerical solution of optimal control problem is solved by a direct collocation method. The …

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

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

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.