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SummaryDesigning the boundary layer thickness and switching gain in the nonlinear part of sliding mode controller (SMC) is one of the main subjects in SMC design that needs human experience, knowledge on the amplitude of disturbances, and information about the bounds of system uncertainties. In this paper, to reduce the trial-and-error effort by the designer(s) two different fitness functions in the horizontal and vertical planes are presented and a heuristic method is used for their optimization. The optimal switching gain in the proposed approach properly compensates the unmodeled dynamics, model uncertainty, and external disturbances. Chattering phenomenon in control signals and noise measurement effect are reduced by the optimal selection of boundary layer thickness. This proposed method is applied to an autonomous underwater vehicle (AUV) and evaluated through the real-time and cost-effective manner. The execution code is implemented on a single-board computer (SBC) through the xPC Target and is evaluated by the processor-in-the-loop (PIL) test. The results of the PIL test in the two different test cases indicate that the chattering phenomenon and amplitude of control signal applied to the actuators are reduced in comparison with the three conventional approaches in the AUV motion control.

In this paper, unstructured system identification algorithm based on orthonormal Laguerre functions is combined with predictive functional control such that similar classical PI controller is constructed. Lack of mathematical model and initial information about process is not a restriction for mentioned algorithm and unstructured system identification based on Laguerre functions can overcome these restrictions. Augmenting new state variables to system state space, a new algorithm is constructed. This algorithm has similar structure with classical PI controller and in predictive control's cost function, in addition to tracking error, system states is utilized, that leads to improve controller dynamical performance. This new algorithm is simulated on the superheated steam temperature system in thermal power plant. Simulation results show capabilities of this algorithm.

In this paper, optical target tracking, by regular target bearing measurements and target range in a lower and scheduled measurement rate is considered. Variance of the target range estimation error is used as scheduling criterion. For this purpose, target dynamic state vector in modified spherical coordinates is stated in such a way that all target states be decoupled from range-related target state.Target state dynamic equations in modified spherical coordinates for nearly constant velocity, nearly constant acceleration and coordinated turn rate kinematic models, are analytically derived. For resulted state dynamic equations, a UKF-IMM filter with range measurement scheduling is utilized as a tracking filter. It is shown that target states are estimated properly and applied filter has high performance in maneuvering target tracking.