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This paper proposes a novel adaptive controller based on digital twin (DT) by integrating software-in-loop (SIL) and hardware-in-loop (HIL). This work aims to reduce the difference between the SIL controller and its physical controller counterpart using the DT concept. To highlight the applicability of the suggested methodology, the regulation control of a horizontal variable speed wind turbine (WT) is considered for the design and assessment purposes. In the presented digital twin framework, the active disturbance rejection controller (ADRC) is implemented for the pitch angle control of the WT plant in both SIL and HIL environments. The design of the ADRC controllers in the DT framework is accomplished by adopting deep deterministic policy gradient (DDPG) in two stages: ( i ) by employing a fitness evaluation of wind speed error, the internal coefficients of HIL controller are adjusted based on DDPG for the regulation of WT plant, and ( ii ) the difference between the rotor speed waveforms in HIL and SIL are reduced by DDPG to obtain a similar output behavior of the system in these environments. Some examinations based on DT are conducted to validate the effectiveness, high dynamic performance, robustness and adaptability of the suggested method in comparison to the prevalent state-of-the-art techniques. The suggested controller is seen to be significantly more efficient especially in the compensation of high aerodynamic variations, unknown uncertainties and also mechanical stresses on the plant drive train.

This paper proposes a deep deterministic policy gradient (DDPG) based nonlinear integral backstepping (NIB) in combination with model free control (MFC) for pitch angle control of variable speed wind turbine. In particular, the controller has been presented as a digital twin (DT) concept, which is an increasingly growing method in a variety of applications. In DDPG-NIB-MFC, the pitch angle is considered as the control input that depends on the optimal rotor speed, which is usually derived from effective wind speed. The system stability according to the Lyapunov theory can be achieved by the recursive nature of the backstepping theory and the integral action has been used to compensate for the steady-state error. Moreover, due to the nonlinear characteristics of wind turbines, the MFC aims to handle the un-modeled system dynamics and disturbances. The DDPG algorithm with actor-critic structure has been added in the proposed control structure to efficiently and adaptively tune the controller parameters embedded in the NIB controller. Under this effort, a digital twin of a presented controller is defined as a real-time and probabilistic model which is implemented on the digital signal processor (DSP) computing device. To ensure the performance of the proposed approach and output behavior of the system, software-in-loop (SIL) and hardware-in-loop (HIL) testing procedures have been considered. From the simulation and implementation outcomes, it can be concluded that the proposed backstepping controller based DDPG is more effective, robust, and adaptive than the backstepping and proportional-integral (PI) controllers optimized by particle swarm optimization (PSO) in the presence of uncertainties and disturbances.

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As heating, ventilation, and air conditioning (HVAC) systems have become one of the most contributing systems in energy consumption in the world, the control of these large-scale systems remains a challenging duty due to the decoupling effects of control variables. Accordingly, the penetration of these types of systems in all-smart buildings has increased in recent years. Furthermore, the application of digital twin as a fast-growing concept is being developed. In HVAC systems, independent and accurate control of temperature and humidity of the indoor air has been playing an undeniable role in reducing energy consumption. In this paper, to have cost-effective energy management in a single-zone HVAC system, a new reliable digital twin proximal policy optimization (PPO)–based model-independent nonsingular terminal sliding-mode control (MINTSMC) methodology has been proposed. Moreover, due to the nonlinear characteristics of HVAC systems, MINTSMC tends to handle the un-modeled system dynamics and disturbances. For regulating parameters of proposed control, an efficient PPO algorithm has been developed due to its actor-critic-based reinforcement learning. Extensive examinations and comparative analyses with particle swarm optimization designed sliding-mode control and proportional–integral–derivative controller have been made using digital twin of the proposed controller to show the importance, accuracy, and application of this method in the comfort and energy management achievement of HVAC control systems. A digital signal processor computing device has been utilized for implementation by utilizing hardware-in-loop (HIL) in the concept of the digital twin. To determine the interface between established models, software-in-loop, and HIL, the Functional Mock-up Interface has been utilized. The outcomes revealed a superior performance of suggested digital twin-based controller than the compared control methodologies in the compensation of unknown uncertainties, fast-tracking, and smooth response.