The identification problem incorporated in feedback control of uncertain nonlinear systems exhibiting complex behavior has been solved in different ways. Some of these solutions have used artificial intelligence methods like fuzzy logic and neural networks. However, their individual implementation suffers from certain drawbacks, such as the black-box nature of neural network and the problem of finding suitable membership functions for fuzzy systems. These weaknesses can be avoided by implementing a hybrid structure combining these two approaches, the so-called neuro-fuzzy system. In this paper, a neuro-fuzzy system that implements differential neural networks (DNNs) as consequences of Takagi-Sugeno (T-S) fuzzy inference rules is proposed. The DNNs substitute the local linear systems that are used in the common T-S method. In this paper, DNNs are used to provide an effective instrument for dealing with the identification of the uncertain nonlinear system, while the T-S rules are used to provide the framework of previous knowledge of the system. The main idea is to carry out an online identification process of an uncertain nonlinear system with the aim to design a closed-loop trajectory tracking controller. The methodology developed in this study that supports the identification and trajectory control designs is based on the Lyapunov formalism. The DNN implementation results in a time-varying T-S system. As a consequence, the solution of two time-varying Riccati equations was used to adjust the learning laws in the DNN as well as to adjust the gains of the controller. Two results were provided to justify the existence of positive-definite solutions for the class of Riccati equations used in the learning laws of DNNs. A complete description of the learning laws used for the set of DNN identifiers is also obtained. An autonomous underwater vehicle system is used to demonstrate the performance of the controller on tracking a desired 3-D path by this combination of the DNN and the T-S system.

Many Takagi-Sugeno (T-S) fuzzy systems use the linear matrix inequality (LMI) to design the controllers. Although LMI can be solved off-line, there are still some difficulties in T-S fuzzy control design with LMI, such as complexities in the analysis and the computation, and conservativeness for complex systems. In this paper, we first transform the T-S fuzzy system into a time-varying nonlinear system. Then we apply the Riccati differential equation to design the fuzzy control. The stability of the T-S fuzzy control is proven. The trajectory tracking error converges to a bounded zone. This novel design method for T-S fuzzy control is more simple and effective than LMI-based methods. The on-line solutions of the Riccati equation can be obtained directly from a designed differential equation.

In this paper, the coordination and trajectory tracking control design of multiple mini rotorcraft systems are discussed. The dynamic model of a mini rotorcraft is presented using the Newton-Euler formalism. Our approach is based on a leader/follower structure of multiple robot systems. The centroid of the coordinated control subsystem is used for trajectory tracking purposes. A nonlinear coordinated control design for multiple autonomous vehicle synchronization is developed. The analytic results are supported by simulation tests.

An original configuration of a small aerial vehicle having eight rotors is presented. Four rotors are devoted to the stabilization of the orientation of the helicopter, and the other four are used to drive the lateral displacements. A precompensation on the roll and pitch angles has been introduced so that the attitude dynamics is practically independent of the translational dynamics. This compensation is directly related to the velocity of the lateral motors. The dynamical model is obtained using the Euler–Lagrange approach. The proposed configuration is particularly useful for image processing since the the camera orientation is held constant. The eight-rotor rotorcraft is simpler to pilot than other rotorcrafts. A control strategy is proposed that uses the optical flow measurements to achieve a hover flight that is robust with respect to perturbations like wind. The new aerial configuration and control strategy have been tested in real-time experiments.

This paper presents an original configuration for a small aerial vehicle based on three rotors with fixed-angle propellers. The process of control vectors generation is described, and a detailed mathematical modeling of the aircraft dynamics is presented. We have also proposed a control strategy for full stabilization of the tri-rotor aircraft considering some real constraints specific to small UAVs. The synthesized controller is simple and results in good performance as demonstrated in simulations and in real-time experiments. We have also developed a useful Simulink-based platform for real-time control purposes

We present a dynamical model of an original trirotor helicopter obtained using the Euler-Lagrange approach. The obtained nonlinear model is then used for designing a nonlinear controller based on nested saturations. The controller performance is shown in real-time experiments.