This paper deals with a state model identification of a gas turbine used for gas transport, using a subspace approach of the state space model. This method provides a reliable and robust state representation of the model, taking advantage of its benefits in the control, monitoring, and supervision of this machine. The model for each variable is set so that the state matrices associated with the gas turbine model are determined from their real input/output data. The comparison of the obtained identification results with those of the actual turbine operation serves to validate the proposed model in this work. This numerical algorithm of the subspace identification method is full of information and more accurate in terms of residual modeling error, and expresses a very high level of confidence in the identified turbine system dynamics. Hence, the controllability and observability tests of turbine operation for different input/output variables allowed to validate the real-time operating stability of the turbine.

The controller in an automated vehicle relies on sensors to collect the information needed for handling traffic situations, and reducing the frequency of using sensors could prolong their lifespans. We present in this paper the application of dynamic sensor activation algorithms in discrete event systems to activate/deactivate sensors for collecting information when it is only necessary to automatically operate headlights based on traffic rules. The framework developed in this paper forms a basis for automatically activating/deactivating sensors for other components in an automated vehicle in the future.

In this paper, we consider distributed Nash equilibrium (NE) seeking in potential games over a multi-agent network, where each agent can not observe the actions of all its rivals. Based on the best response dynamics, we design a distributed NE seeking algorithm by incorporating the non-smooth finite-time average tracking dynamics, where each agent only needs to know its own action and exchange information with its neighbours through a communication graph. We give a sufficient condition for the Lipschitz continuity of the best response mapping for potential games, and then prove the convergence of the proposed algorithm based on the Lyapunov theory. Numerical simulations are given to verify the result and illustrate the effectiveness of the algorithm.

The purpose of this paper is to extend the notion of regional controllability for hyperbolic parameter dependent systems. The key idea is the characterization of the averaged regional control with minimal energy. This control steers the state average (with respect to such a parameter) towards the desired state only on a given part of the system evolution domain. In this paper, we give the precis definition and the properties of this new concept. Then, we use an approach based on an extension of the Hilbert uniqueness method devoted to the calculation of the control in two different cases: zone control and pointwise control.

This paper proposes H∞ controller design for platform position transfer and regulation of floating offshore wind turbines. The platform movability of floating wind turbines can be utilized in mitigating the wake effect in the wind farm, thereby maximizing the wind farm’s total power capture and efficiency. The controller is designed so that aerodynamic force is adjusted to meet the three objectives simultaneously, that is, 1) to generate the desired electrical power level, 2) to achieve the desired platform position, and 3) to suppress the platform oscillation. To acquire sufficient aerodynamic force to move the heavy platform, the pitch-to-stall blade pitching strategy is taken instead of the commonly-used pitch-to-feather strategy. The desired power level is attained by the standard constant-power strategy for the generator torque, while H∞ state-feedback control of blade pitch and nacelle yaw angles is adopted for the position regulation and platform oscillation suppression. Weighting constants for the H∞ controller design are adjusted to take the trade-off between the position regulation accuracy and the platform motion reduction. To demonstrate the efficiency of the proposed controller, a virtual 5-MW semi-submersible wind turbine is considered. Simulation results show that the designed H∞ controller successfully accomplishes the platform position transfer and regulation as well as the platform oscillation reduction against wind and wave disturbances, and that it outperforms a previously-proposed linear quadratic controller with an integrator.

This study presents a parametric system identification approach to estimate the dynamics of a chemical plant from experimental data and develops a robust PID controller for the plant. Parametric system identification of the heat exchanger system has been carried out using experimental data and prediction error method. The estimated model of the heat exchanger system is a time-delay model and a robust PID controller for the time-delayed model has been designed considering weighted sensitivity criteria. The mathematical background of parametric system identification, stability analysis, and H∞ weighted sensitivity analysis have been provided in this paper. A graphical plot has been provided to determine the stability region in the (Kp,Ki), (Kp,Kd) and (Ki,Kd) plane. The stability region is a locus dependent on parameters of the controller and frequency, in the parameter plane.

Flight controllers for micro-air UAVs are generally designed using proportional-integral-derivative (PID) methods, where the tuning of gains is difficult and time-consuming, and performance is not guaranteed. In this paper, we develop a rigorous method based on the sliding mode analysis and nonlinear backstepping to design a PID controller with guaranteed performance. This technique provides the structure and gains for the PID controller, such that a robust and fast response of the UAV (unmanned aerial vehicle) for trajectory tracking is achieved. First, the second-order sliding variable errors are used in a rigorous nonlinear backstepping design to obtain guaranteed performance for the nonlinear UAV dynamics. Then, using a small angle approximation and rigorous geometric manipulations, this nonlinear design is converted into a PID controller whose structure is naturally determined through the backstepping procedure. PID gains that guarantee robust UAV performance are finally computed from the sliding mode gains and from stabilizing gains for tracking error dynamics. We prove that the desired Euler angles of the inner attitude controller loop are related to the dynamics of the outer backstepping tracker loop by inverse kinematics, which provides a seamless connection with existing built-in UAV attitude controllers. We implement the proposed method on actual UAV, and experimental flight tests prove the validity of these algorithms. It is seen that our PID design procedure yields tighter UAV performance than an existing popular PID control technique.

This paper presents a system identification method to derive accurate mathematical models for an unmanned circulation control aerial vehicle (UC2AV) that account for the effects of circulation control (CC) on the vehicle dynamics. The X-plane flight simulator and the CIFER system identification software are utilized to first derive simulation models to verify and validate the proposed system identification methodology. This is followed by flight tests to derive mathematical models and stability derivatives for the aircraft with CC-on and CC-off. Flight tests indicate a nose down pitching moment effect induced by CC, which in turn alter the UAV trim values and dynamics. Analysis of the two sets of mathematical models reveal that CC changes the longitudinal trim values and improves the lateral maneuverability of the UAV. Verification experiments indicate an acceptable match between the derived models and UAV dynamics by calculating root mean square (RMS) error values and by quantifying the model predictive ability through calculating the Theil inequality coefficient (TIC).

Starting of combustion engines is a typical transient operating mode that has significant influence to the engine performance. Due to the distinct variations in the pathes of air intake and fuel injection, the model of the engine system contains considerable uncertain parameters. To search effective control schemes that guarantee desired performance, engine starting control is proposed as a benchmark challenge problem. As a challenging result, a model-based control scheme is developed perviously. In this work, the benchmark problem is revisited and a modification for the fuel injection path control of the previous work is proposed by integrating a time sequence regressive based parameter tuning strategy. Validation by the benchmark problem simulator shows that although the new strategy has simple structure, similar control performance is obtained. Especially, the new strategy has potential extensibility with learning based methods to further improve the performance of the benchmark problem on engine starting control.