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This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles, which can be applied to different types of USV. In order to assist in the choice of the SOA method for a particular vessel and to accelerate the pretuning process necessary for its implementation, this paper proposes a new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) methods applied to USVs. In this environment, a new simplified modelling of a LIDAR (Laser Imaging Detection and Ranging) sensor is proposed based on numerical simulations. This sensor model provides a realistic environment for the tuning of SOA methods that, due to its low load computation, is used by evolutionary algorithms for the autotuning. In order to analyze the proposed ATESOA, three SOA methods were adapted and implemented to consider the measurements given by the LIDAR model. Furthermore, a mathematical model is proposed and evaluated for using as USV in the simulation enviroment. The results obtained in numerical simulations show how the new ATESOA is able to adjust the SOA methods in scenarios with different obstacle distributions.

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In this work, a new pre-tuning multivariable PID (Proportional Integral Derivative) controllers method for quadrotors is put forward. A procedure based on LQR/LQG (Linear Quadratic Regulator/Gaussian) theory is proposed for attitude and altitude control, which suposes a considerable simplification of the design problem due to only one pretuning parameter being used. With the aim to analyze the performance and robustness of the proposed method, a non-linear mathematical model of the DJI-F450 quadrotor is employed, where rotors dynamics, together with sensors drift/bias properties and noise characteristics of low-cost commercial sensors typically used in this type of applications are considered. In order to estimate the state vector and compensate bias/drift effects in the measures, a combination of filtering and data fusion algorithms (Kalman filter and Madgwick algorithm for attitude estimation) are proposed and implemented. Performance and robustness analysis of the control system is carried out by employing numerical simulations, which take into account the presence of uncertainty in the plant model and external disturbances. The obtained results show the proposed controller design method for multivariable PID controller is robust with respect to: (a) parametric uncertainty in the plant model, (b) disturbances acting at the plant input, (c) sensors measurement and estimation errors.

This paper describes the application of an LQG/LTR controller for automatic steering of ships. The controller is based on Nomoto's model and on the innovation model to identify the discrete time system. The system is a non-minimum phase, which is the sample version of the continuous system. The benefits of this controller are demonstrated by simulation.The Linear Quadratic Regulator that we used is described in section 2. In section 3 we described the system model of the ship steering. In the next section we ilustrated the proposed method with an example by simulation and finally the major conclusions to be drawn are given. Linear Quadratic Regulator Introduction Considering the process model (ARMAX),The requirements of control on ship steering have been increasing in the past few years to improve the performance specifications. The sensitivity of control systems to the parameters and structure variation in the plant is an important aspect to consider. So therefore, one of the main requirements of process control is that the controller should be robust, in order to overcome possible inaccuracies in the model such as modifications in the plant, disturbancies, etc.One of the possible solutions to controlling this kind of plant is by means of an LQR regulator. This type of controller works well when the state is accessible, but its robustness can be weakened when an observer is introduced [5].In order to solve this problem it is proposed that the observer should be modified so as to recuperate the stability margins in effect when the state is accessible. This procedure is called in recent literature Loop Transfer Recovery (LTR). The use of this methodology leads to more robust controllers [5,12].The recovery condition for continuous system [6] and in some cases for discrete systems has been demonstrated for minimum phase systems Ill]. In the case of non-minimum phase systems recovery cannot be quaranted, even through it is produced in certain cases. We now propose to extend this idea to control a special case of non-minimum phase systems, that is, to control ship steering.A ship is a special case of a non-minimun system. It dynamics basically depends on factors such as: class of ship, ship's speed, depth of water, load condition and trim.For determined conditions (steering operation with small rudder angles and constant speed) it is possible to describe its dynamics by a linear model (Nomoto's model). This model captures the essencial characteristics of ship steering dynamics what has been verified by extensive experiments on niaiiy ships [1,8].It can be written [l], in state space form as,IC,, is the optimal steady-state gain in the Iialman filter. This model is called the innovation model. This structure of the model has the advantage of obtaining Kay, directly, without having to solve the Riccati equation. Introducing the loss function, J' = E ( x T ( k ) Q c 4 k ) + Rcu2(k)) k=O the optimal regulator is given by, u(k) = -Zic;.(k) Considering plant model equation 1, we have, 447