This paper considers the nonlinear adaptive model following control of a 3-DOF model helicopter. The system model here is not decouplable by static state feedback, and the nonlinear structure algorithm is applied. When a simple model following controller is designed, it is not easy to obtain a good control performance mainly due to the parameter uncertainties. Then, two parameter identification schemes are discussed: The first scheme is based on the differential equation model. This scheme is unable to obtain a good tracking control performance because of the inaccuracy of the estimated velocity and acceleration signals. The second scheme is designed for a dynamics model derived by applying integral operators to the differential equations expressing the system dynamics. Hence, this identification algorithm requires neither velocity nor acceleration signals. The experimental results show that the second method yields a better tracking result, although tracking errors still remain. Finally, we introduce additional terms into the equations of motion to express model uncertainties and external disturbances. With reference to experimental results, this modification is shown to further improve the tracking control performance.

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The field of mechatronics (which is the synergistic combination of precision mechanical engineering, electronic control and systems thinking in the design of products and manufacturing processes) is gaining much attention in industries and academics. It was detected that the topics of computer vision, control and robotics are imperative for the successful of mechatronics systems. This book includes several chapters which report successful study cases about computer vision, control and robotics. The readers will have the latest information related to mechatronics, that contains the details of implementation, and the description of the test scenarios.

The two-volume set CCIS 1918 and 1919 constitutes the refereed post-conference proceedings of the 8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023, held in Luoyang, China, during August 10–12, 2023. The 52 full papers presented in these proceedings were carefully reviewed and selected from 136 submissions. The papers are organized in the following topical sections: Volume I : Award; Algorithm & Control; and Application. Volume II: Robotics & Bioinformatics; and Vision.

These proceedings are based on the 2013 International Conference on Future Information & Communication Engineering (ICFICE 2013), which will be held at Shenyang in China from June 24-26, 2013. The conference is open to all over the world, and participation from Asia-Pacific region is particularly encouraged. The focus of this conference is on all technical aspects of electronics, information, and communications ICFICE-13 will provide an opportunity for academic and industry professionals to discuss the latest issues and progress in the area of FICE. In addition, the conference will publish high quality papers which are closely related to the various theories and practical applications in FICE. Furthermore, we expect that the conference and its publications will be a trigger for further related research and technology improvements in this important subject.

A helicopter is a complex nonlinear system and also an under actuated system with fewer independent control actuators than degrees of freedom to be controlled, making the control difficult. There is a growing interest in the modeling and control of such systems using nonlinear dynamic models and nonlinear control. Analytical techniques based on Lyapunov theory are then used to design the controller and still the design can become extremely complex. Hence the existing control methods use linearization techniques on the actual nonlinear dynamics of the plant and linear control techniques. The resulting performance may not be satisfactory, especially when the system is subjected to unknown and sudden disturbances. In this thesis, we present a new Nonlinear and Adaptive controller design which uses the actual nonlinear model of the helicopter and not a linearized version. The design methodology basically involves making the combined dynamics of the helicopter and the controller resemble the dynamics of a nonlinear time varying electrical circuit having the required properties using a process similar to reverse engineering. The circuit template in turn is formed from well defined time varying and/or nonlinear electrical elements and using proper interconnections. The kind of elements used and the general form of the dynamics derived will depend upon the application. For example in the helicopter case, the closed loop dynamics of the helicopter and the controller expressed in terms of the error variable should point to a NLTV circuit with only passive elements. For this, the reactive elements should have their relaxation points (the points where the stored energy is zero) at and only at the origin. Also the stored energy should be monotonically increasing. We can bring in any knowledge including the structure that we have about the plant being controlled in enhancing the circuit.