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Register a new userAn Output Containment Approach to Cooperative Control of Multiple Unmanned and Manned Vehicles
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This paper investigates the cooperative control of multiple unmanned and manned vehicles via an output containment control approach for heterogeneous discrete-time multiagent systems. The unmanned vehicles act as leading vehicles to guide the manned vehicles, i.e., following vehicles. The objective is to develop a distributed output feedback control law such that the output of the following vehicles can converge to the convex hull spanned by the output of the leading vehicles exponentially. The convex hull formed by the output of the leading vehicles and the system matrix of leading vehicles are estimated via a distributed containment observer. Based on this observer, a distributed dynamic output feedback control protocol is first devised for heterogeneous discrete-time multi-agent systems using only neighboring relative output information. The proof is depicted by showing certain output containment errors converge to zero exponentially, which indicates the containment control objective is well achieved. A distributed dynamic state-feedback control law is deduced as a special case of the output feedback control. Finally, numerical simulations with application to cooperative control of multiple vehicles validate the effectiveness and the computational feasibility of the proposed control protocols.
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The hybrid electric system has good potential for unmanned tracked vehicles due to its excellent power and economy. Due to unmanned tracked vehicles have no traditional driving devices, and the driving cycle is uncertain, it brings new challenges to conventional energy management strategies. This paper proposes a novel energy management strategy for unmanned tracked vehicles based on local speed planning. The contributions are threefold. Firstly, a local speed planning algorithm is adopted for the input of driving cycle prediction to avoid the dependence of traditional vehicles on drivers operation. Secondly, a prediction model based on Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) is proposed, which is used to process both the planned and the historical velocity series to improve the prediction accuracy. Finally, based on the prediction results, the model predictive control algorithm is used to realize the real-time optimization of energy management. The validity of the method is verified by simulation using collected data from actual field experiments of our unmanned tracked vehicle. Compared with multi-step neural networks, the prediction model based on CNN-LSTM improves the prediction accuracy by 20%. Compared with the traditional regular energy management strategy, the energy management strategy based on model predictive control reduces fuel consumption by 7%.
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