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Hybrid systems have acquired deep attention in research as many systems in industries and laboratories manifest hybrid dynamic behaviors. Optimal control design for hybrid systems exhibits challenges for controlling the interacting continuous and discrete dynamics. A double-tank hybrid system (DTHS) is proposed in this paper with a mixed-integer actuation mechanism. The proposed DTHS aims to control the liquid level of the two tanks simultaneously by a networked hybrid predictive controller. Hybrid frameworks are provided to investigate the system through reachability analysis. An explicit hybrid model predictive control (EHMPC) has been developed to control the levels of the DTHS. Hardware-In-the-Loop (HIL) simulation is performed for assessing the performance of the EHMPC with a hardware real-time networked controller. The networked real-time controller is constructed and implemented using the Node.js frameworks. The simulation and realtime results are presented and discussed to evaluate the hybrid controller performance. [ABSTRACT FROM AUTHOR]

Wind energy has proven to be the highest reliable source of renewables due to the maturity of the technology. Wind turbine (WT) systems are complex systems with undergoing development process that requires innovative methods of design and control. In this paper, WTs are studied through hybrid systems framework. Hybrid models of WT are extracted with representable dynamics from nonlinear complex design code. The WT model is formulated into a mixed-logical dynamical model (MLD) and a piecewise affine (PWA) model. Then, a receding horizon control strategy is applied to WT hybrid model resulting in a hybrid model predictive control (HMPC) with mixed-integer programming (MIP) problem. The performance of the proposed controller is compared against the baseline controller within a simulation environment for the National Renewable Energy Laboratory (NREL) 5MW benchmark WT as a case study. The analysis and investigation of HMPC highlight its capability as a potential tool for exploiting the control objectives of WT systems. [ABSTRACT FROM AUTHOR]

Helicopter systems are considered a complex and challenging control problem due to strong couplings and high non-linearities. In this paper, simulated annealing (SA), as one of the leading methods in search and optimization, is applied to tune a multivariable controller of a lab-scale helicopter system. The lab-scale helicopter system is a multivariable experimental aerodynamic test rig that resembles the behaviour of a real helicopter. The control objectives are quickly to reach a desired position or track a trajectory. A centralized cross-coupled PID controller is used to achieve these objectives. First, SA optimizations are carried out with 24 different initial configurations. Then, the best results of these SA configurations are compared with other controllers obtained with evolutionary algorithms (EAs) of genetic algorithms (GAs), modified particle swarm optimization (MPSO) and differential evolution (DE). The comparisons are based on statistical measures of 20 independent trials, non-linear computer simulations of different input signals and real-time measurements for various commands of positions or trajectories. Results show that SA obtained the best performance index and acceptable time-domain performance on reaching hovering point, following a step command and tracking a sine trajectory compared with the investigated EAs. [ABSTRACT FROM AUTHOR]

The twin-rotor, multi-input, multi-output system is a typical lab-scale helicopter system, since it resembles behavior of helicopter systems. This experimental complex aerodynamic test rig needs precise modeling to assure satisfactory control performance. Therefore, this article is intended to provide a gray-box approach for the modeling of the twin-rotor, multi-input, multi-output system. An algorithm is proposed by dividing the system into independent subsystems that can be estimated for an excitation input or a characteristic response. The proposed algorithm uses trust region optimization methods for successive estimations. The iterative optimization methods of Levenberg–Marquardt, gradient descent active set, and interior point are compared with respect to trust region methods. The model is validated with various sets of scenarios: different inputs, different types of motion, and different operating points. Effect of operating point selection on the parameter estimation process is investigated. Time domain response comparison of the single-degree-of-freedom vertical, single-degree-of-freedom horizontal, and two-degrees-of-freedom models are acceptable. [ABSTRACT FROM AUTHOR]