Implementing a Hardware-Based Big-Bang Big-Crunch optimized controller with Fractional-Order for a Heating, Ventilation, and air conditioning system

[Display omitted] •Implementation of a Hardware-Based BB-BC Optimized Controller with Fractional-Order for a HVAC System.•An FPGA implementation of a fractional order proportional-integral-derivative controller for an HVAC system.•Adoption of the BB-BC algorithm for optimal tuning of the controller parameters.•Use of a second-order plus time delay model to represent the HVAC system.•Illustration of the effective tracking and rejection of disturbance signals by the FOPID controller based on the BB-BC algorithm.•High-performance pipelined implementation of the BB-BC algorithm and FOPID controller on a Xilinx FPGA device. Industrial and commercial buildings are significant energy consumers, worldwide, emphasizing the need for effective control techniques to reduce power consumption. This paper presents an FPGA implementation of a fractional order proportional-integral-derivative (FOPID) controller for a Heating, Ventilation and Air Conditioning (HVAC) system. The FOPID model enhanced improved dynamic performance and system robustness compared to classical PID controllers. However, tuning the FOPID controller is challenging due to the need to adjust several parameters, i.e., the proportional, integral, and derivative gains of the PID model, as well as the fractional differential and integral orders. To address this, the Big Bang-Big Crunch (BB-BC) evolutionary algorithm is adopted to tune the controller parameters optimally. The BB-BC algorithm is executed to minimize six different performance indices. The HVAC system is represented by a second-order plus time delay (SOPTD) model, yielding six different cases are obtained by combining three delay values and two gain values. A high-performance pipelined implementation of the BB-BC algorithm andthe FOPID controller is designed and realized on a Xilinx FPGA device. The implementation has successfully reduced the oscillations of the HVAC output signal by employing auto-tuning of control parameters based on BB-BC, while improved results have been also achieved in terms of rapid convergence. Additionally, a substantial reduction of 80 % in the output control signal and 73 % in error has been observed when using a window size (L) of L = 256, in comparison to L = 32. The simulation results demonstrate the highly efficient performance of the FOPID-BB-BC control unit after optimal tuning.