Optimization of Aircraft Control Stick Force Using Bio-Inspired Swarm Intelligence Algorithms

For both manually and automatically controlled aircraft, it is important to keep the required control stick force within the limits of pilot effort and actuator capability. Therefore, the control surface generating the control stick force should be designed with consideration of aircraft stability and control derivatives. The control stick force can only be optimized by keeping the derivatives within their limits. However, due to the large number of coupled nonlinear aerodynamic and geometric variables affecting the control stick force, its optimization becomes a complex process. Fortunately, bio-inspired swarm algorithms are valuable candidates to deal with these complex optimization problems. In this paper, the particle swarm optimization, the ant colony optimization, the whale optimization algorithm, and the gray wolf optimizer are four bio-inspired swarm algorithms that are evaluated for optimizing aircraft control stick force requirements. The performance of these algorithms is demonstrated, and the results are validated with aerodynamic and geometric variables of a benchmark aircraft, focusing on pitch control (elevator), decoupled from other axes. It is concluded that the aircraft control stick force can be optimized by using all four swarm algorithms while maintaining the variation interval for the stability and control derivatives.