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Articles 1 - 10 of 10
Full-Text Articles in Engineering
Optimal Compensation Control Strategy For Four-Axle Vehicle With All-Wheel Steering System, Baohua Wang, Yu Chao
Optimal Compensation Control Strategy For Four-Axle Vehicle With All-Wheel Steering System, Baohua Wang, Yu Chao
Journal of System Simulation
Abstract: Aiming at the problem of poor stability of traditional multi-axle commercial vehicles, an optimal control strategy for multi-axle steering wheel is proposed. Through the establishment of the four-axis vehicle model, the wheel angle proportional feed-forward control algorithm and the yaw rate feedback control algorithm are studied. The optimal compensating control algorithm of the wheel angle proportional feed-forward and yaw rate feedback is proposed, and the control strategy is also verified through joint simulation. The results show that the full-wheel steering optimal compensation control strategy can keep the center-of-mass skew angle close to zero at all vehicle speeds, …
Multiple Pursuer Multiple Evader Differential Games, Eloy Garcia, David Casbeer, Alexander Von Moll, Meir Pachter
Multiple Pursuer Multiple Evader Differential Games, Eloy Garcia, David Casbeer, Alexander Von Moll, Meir Pachter
Faculty Publications
In this paper an N-pursuer vs. M-evader team conflict is studied. The differential game of border defense is addressed and we focus on the game of degree in the region of the state space where the pursuers are able to win. This work extends classical differential game theory to simultaneously address weapon assignments and multi-player pursuit-evasion scenarios. Saddle-point strategies that provide guaranteed performance for each team regardless of the actual strategies implemented by the opponent are devised. The players' optimal strategies require the co-design of cooperative optimal assignments and optimal guidance laws. A representative measure of performance is proposed and …
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 …
Optimal Sampling Paths For Autonomous Vehicles In Uncertain Ocean Flows, Andrew J. De Stefan
Optimal Sampling Paths For Autonomous Vehicles In Uncertain Ocean Flows, Andrew J. De Stefan
Dissertations
Despite an extensive history of oceanic observation, researchers have only begun to build a complete picture of oceanic currents. Sparsity of instrumentation has created the need to maximize the information extracted from every source of data in building this picture. Within the last few decades, autonomous vehicles, or AVs, have been employed as tools to aid in this research initiative. Unmanned and self-propelled, AVs are capable of spending weeks, if not months, exploring and monitoring the oceans. However, the quality of data acquired by these vehicles is highly dependent on the paths along which they collect their observational data. The …
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 …
Model Predictive Control Synthesis For The Innovative Control Effector Tailless Fighter Aircraft, Christopher Proctor
Model Predictive Control Synthesis For The Innovative Control Effector Tailless Fighter Aircraft, Christopher Proctor
Masters Theses
A nonlinear model predictive control law was developed for the Lockheed Martin Innovative Control Effector tailless fighter aircraft to track way points. In general, aircraft are described by nonlinear dynamics that are dependent on the regime of flight. Additionally strict requirements on state and actuator constraints are common to all aircraft. Tailless aircraft are usually overdetermined systems, meaning solutions to control problems are not unique, and the system is non-affine. The proposed nonlinear control law considers those constraints during run-time, and solves the nonlinear control problem for a range of points within different flight regimes. The control law was developed …
Optimal Control And Simulation Of Hard Shoulder Running On Highways, Ruimin Li, Ye Zhen, Bin Li
Optimal Control And Simulation Of Hard Shoulder Running On Highways, Ruimin Li, Ye Zhen, Bin Li
Journal of System Simulation
Abstract: This paper summarizes the problems in the operation of hard shoulder running (HSR). A new algorithm for optimizing HSR on highways is proposed, which aims to minimize the total time spent (TTS) in the whole network. Based on the METANET model of highways, the algorithm utilizes genetic algorithm and sliding windows technique to forecast and optimize the operation of HSR under different constraint conditions and target functions. The strategy is tested on the I-80E highway in California. The experimental results show that when the change frequency of HSR is less than or equal to 14 during a sliding window …
Optimizing Control Of Total Heat Supply Based On Machine Learning, Li Qi, Xingqi Hu, Jianmin Zhao
Optimizing Control Of Total Heat Supply Based On Machine Learning, Li Qi, Xingqi Hu, Jianmin Zhao
Journal of System Simulation
Abstract: The central heating system has complex structure, along with the characteristics of hysteresis, strong coupling and nonlinear. Contraposing the problem that the process is difficult to be identified and controlled by the mechanism modeling, an optimal control method of heat source total heat production based on machine learning is proposed. The heat source model of central heating system is established by BP neural network and long short-term memory neural network. Under the premise of meeting the demand of heating quality, with the total energy consumption as the optimization objective, the optimal control sequence of water supply temperature and water …
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
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
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 …