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Effective target tracking and obstacle avoidance strategies are essential to the success of unmanned aerial vehicle (UAV) missions. This paper describes a decentralized model-based predictive control to calculate the optimal UAV trajectory that will lead each UAV to the interception of a target at a desired time and with desired speed, heading and flight path angle, while avoiding dynamic ellipsoidal obstacles detected en route. Obstacle avoidance is based on the minimization of the UAV collision probability with all known obstacles on a future horizon, while ensuring that the collision probability with any given obstacle at each prediction step does not surpass a preset threshold. Cooperation between UAVs is possible by exchanging information about the obstacles they detect. To respect the arrival specifications, a virtual target is created and tracked. The virtual target moves at the desired speed and along a straight line that is correctly oriented to intercept the real target position at the desired arrival time. Arrival specifications are respected if tracking of the virtual target by the UAV is successful. Simulations are presented to demonstrate the effectiveness of the proposed approach. They also illustrate that cooperation can improve the UAV performance.

Two ways to model a process are physical modeling and empirical modeling. Physical models rely on basic laws of physics whereas empirical models are obtained by fitting an empirical equation to experimental data. Empirical models are less complex and easier to obtain but they are valid only for specific process operating conditions. Moreover, their parameters do not have any physical meaning and a prioriinformation if often completely neglected. Part I of this paper presents two models of a pilot flotation column by mixing both physical and empirical approaches. The models are accurate but remain simple enough to be used for controller design. Part II of the paper details the backstepping control based on the first model and the model reference nonlinear control using the second model. Comparison is then made with a typical industrial PI control for the same process.

Two ways to model a process are physical modeling and empirical modeling. Physical models rely on basic laws of physics whereas empirical models are obtained by fitting an empirical equation to experimental data. Empirical models are less complex and easier to obtain but they are valid only for specific process operating conditions. Moreover, their parameters do not have any physical meaning and a prioriinformation if often completely neglected. Part I of this paper presents two models of a pilot flotation column by mixing both physical and empirical approaches. The models are accurate but remain simple enough to be used for controller design. Part II of the paper details the backstepping control based on the first model and the model reference non linear control using the second model. Comparison is then made with a typical industrial PI control for the same process.