Robust Adaptive Dynamic Surface Control for a Class of Nonlinear Dynamical Systems with Unknown Hysteresis

The output tracking problem for a class of uncertain strict-feedback nonlinear systems with unknown Duhem hysteresis input is investigated. In order to handle the undesirable effects caused by unknown hysteresis, the properties in respect to Duhem model are used to decompose it as a nonlinear smooth term and a nonlinear bounded “disturbance-like” term, which makes it possible to deal with the unknown hysteresis without constructing inverse in the controller design. By combining robust control and dynamic surface control technique, an adaptive controller is proposed in this paper to avoid “the explosion complexity” in the standard backstepping design procedure. The negative effects caused by the unknown hysteresis can be mitigated effectively, and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system is obtained. The effectiveness of the proposed scheme is validated through a simulation example

Controller Design for a Second-Order Plant with Uncertain Parameters and Disturbance: Application to a DC Motor

This paper shows the controller design for a second-order plant with unknown varying behavior in the parameters and in the disturbance. The state adaptive backstepping technique is used as control framework, but important modifications are introduced. The controller design achieves mainly the following two benefits: upper or lower bounds of the time-varying parameters of the model are not required, and the formulation of the control and update laws and stability analysis are simpler than closely related works that use the Nussbaum gain method. The controller has been developed and tested for a DC motor speed control and it has been implemented in a Rapid Control Prototyping system based on Digital Signal Processing for dSPACE platform. The motor speed converges to a predefined desired output signal

Adaptive control and neural network control of nonlinear discrete-time systems

Ph.DDOCTOR OF PHILOSOPH

Tendon-Sheath Mechanisms in Flexible Membrane Wing Mini-UAVs: Control and Performance

Flexible membrane wings (FMWs) are known for two inherent advantages, that is, adaptability to gusty airflow as the wings can flex according to the gust load to reduce the effective angle of attack and the ability to be folded for compact storage purposes. However, the maneuverability of UAV with FMWs is rather limited as it is impossible to install conventional ailerons. The maneuver relies only on the rudders. Some applications utilize torque rods to warp the wings, but this approach makes the FMW become unfoldable. In this research, we proposed the application of a tendon-sheath mechanism to manipulate the wing shape of UAV. Tendon-sheath mechanism is relatively flexible; thus, it can also be folded together with the wings. However, its severe nonlinearity in its dynamics makes the wing warping difficult to control. To compensate for the nonlinearity, a dedicated adaptive controller is designed and implemented. The proposed approach is validated experimentally in a wind tunnel facility with imitated gusty condition and subsequently tested in a real flight condition. The results demonstrate a stable and robust wing warping actuation, while the adaptive washout capability is also validated. Accurate wing warping is achieved and the UAV is easily controlled in a real flight test

Model-Guided Data-Driven Optimization and Control for Internal Combustion Engine Systems

The incorporation of electronic components into modern Internal Combustion, IC, engine systems have facilitated the reduction of fuel consumption and emission from IC engine operations. As more mechanical functions are being replaced by electric or electronic devices, the IC engine systems are becoming more complex in structure. Sophisticated control strategies are called in to help the engine systems meet the drivability demands and to comply with the emission regulations. Different model-based or data-driven algorithms have been applied to the optimization and control of IC engine systems. For the conventional model-based algorithms, the accuracy of the applied system models has a crucial impact on the quality of the feedback system performance. With computable analytic solutions and a good estimation of the real physical processes, the model-based control embedded systems are able to achieve good transient performances. However, the analytic solutions of some nonlinear models are difficult to obtain. Even if the solutions are available, because of the presence of unavoidable modeling uncertainties, the model-based controllers are designed conservatively