• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1080-1085
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• 2005

A hybrid control scheme is proposed for the stabilization of backward movement along simple paths for a vehicle composed of a truck and six trailers. The hybrid comprises the combination of a linear quadratic regulator (LQR) and a neurocontroller (NC) that is trained by a genetic algorithm (GA). Acting singly, either the NC or the LQR are unable to perform satisfactorily over the entire range of the operation required, but the proposed hybrid is shown to be capable of providing good overall system performance. The evaluation function of the NC in the hybrid design has been modified from the conventional type to incorporate both the squared errors and the running steps errors. The reverse movement of the trailer-truck system can be modeled as an unstable nonlinear system, with the control problem focusing on the steering angle. Achieving good backward movement is difficult because of the restraints of physical angular limitations. Due to these constraints the system is impossible to globally stabilize with standard smooth control techniques, since some initial states necessarily lead to jack-knife locks. This paper demonstrates that a hybrid of neural networks and LQR can be used effectively for the control of nonlinear dynamical systems. Results from simulated trials are reported.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.391-402
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• 2015

In this paper, we present implementation of backward movement control of truck-trailer vehicles using a fuzzy mode-based control scheme considering practical constraints and computational overhead. We propose a fuzzy feedback controller where output is predicted with the delay of a unit sampling period. Analysis and design of the proposed controller is very easy, because it is synchronized with sampling time. Stability analysis is also possible when quantization exists in the implementation of fuzzy control architectures, and we show that if the trivial solution of the fuzzy control system without quantization is asymptotically stable, then the solutions of the fuzzy control system with quantization are uniformly ultimately bounded. Experimental results using a toy truck show that the proposed control system outperforms a conventional system.

• 전자공학회논문지 IE
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• v.46 no.2
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• pp.33-45
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• 2009

In this paper, the implementation of the backward movement control of a truck-trailer type mobile robot using fuzzy model based control scheme considering the practical constraints, computing time-delay and quantization is presented. We propose the fuzzy feedback controller whose output is delayed with unit sampling period and predicted. The analysis and the design problem considering the computing time-delay become very easy because the proposed controller is syncronized with the sampling time. Also, the stability analysis is made when the quantization exists in the implementation of the fuzzy control architectures and it is shown that if the trivial solution of the fuzzy control system without quantization is asymptotically stable, then the solutions of the fuzzy control system with quantization are uniformly ultimately bounded. The experimental results are shown to verify the effectiveness of the proposed scheme.