Small-scale unmanned helicopters are widely used in military and commercial field, because of its superior flight characteristics: vertical take-off and landing, hovering, longitude flight, lateral flight, pirouette, and bank to turn. Possible applications include close-up inspection of power lines, terrain surveying, search-and-rescue missions, filming movies, and the investigation and clean-up of hazardous waste sites. Unmanned Helicopters are hard to control because of their nonlinear dynamic model and the constraints on actuators and outputs. Generally, PID, LQR, NN etc. is hard to handle the constraints problem. This paper presents a novel control method, model predictive control, which can deal with the input and output saturation constraints well for unmanned helicopters. The final results show that model predictive controller works well on the control of roll and pitch axes. It reduces the settling time and maximum overshoot and performs good stability and robustness comparing with PID controller. The paper present two controllers, MPC and PID, for flight control. The simulation results shows that MPC controller has better performance. MPC controller has shorter settling time and smaller energy consume than PID controller. On the other hand, MPC controller needs more computation time and consumes lots of resources of the processor. Decreasing the computation time will be the next research direction. The simulation results shows that MPC controller has better performance. MPC controller has shorter settling time and smaller energy consume than PID controller. On the other hand, MPC controller needs more computation time and consumes lots of resources of the processor. Decreasing the computation time will be the next research direction.
Model predictive control with application to a small-scale unmanned helicopter
2006
9 Seiten, 11 Bilder, 3 Tabellen, 6 Quellen
Conference paper
English
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