Research On Nonlinear Control Of Two-degree-of-freedom Helicopter System Under Output Constraint

Unmanned Aerial Vehicles(UAVs),also known as drones,have found various applications and greatly contributed to advancements in aviation technology,providing opportunities for commercial and scientific exploration.Among the different types of UAVs,unmanned helicopters have promising application prospects due to their ability to transport materials and equipment,support military and civilian operations,and attract increasing research attention.However,in practical applications of unmanned helicopters,various environmental and equipment constraints are common,such as airflow disturbance,height and speed limitations,and body and rotor force constraints.Neglecting these constraints can lead to decreased performance,reduced safety,equipment damage,or even personnel injury.Therefore,it is essential to consider these constraints and incorporate them into the controller design,which increases the difficulty and complexity of control design.The presence of constraint conditions requires the helicopter controller to consider multiple input and output variables simultaneously and design complex constraint control algorithms to address these issues.Hence,constraint control presents a significant challenge to helicopter control design and requires in-depth theoretical and practical research.To achieve safer and more efficient helicopter control in design and application,these constraint characteristics must be fully considered and addressed.In this paper,we use Quanser Aero’s two-degree-of-freedom helicopter platform to conduct research on the nonlinear control of the two-degree-of-freedom helicopter system under limited output conditions.The paper addresses the following aspects:(1)The research focuses on the adaptive neural network control problem of the nonlinear two-degree-of-freedom helicopter system under output constraint conditions.A backstepping algorithm is constructed for hierarchical control,and a radial basis neural network is used to estimate unknown functions to achieve adaptive control of the system.The controller’s design is optimized using the barrier Lyapunov function construction method,and the adaptive parameters are set to ensure that the system’s output approaches the upper bound of the approximation error.The effectiveness of the proposed method is proved by simulation and experimentation.(2)The research deals with the adaptive neural network control problem of the nonlinear two-degree-of-freedom helicopter system under state constraints.The controller is designed by constructing a barrier Lyapunov function to control the state constraints of the helicopter system.A radial basis neural network is used to approximate the nonlinear functions in the helicopter system,ensuring that the system’s state is within the predetermined range.The effectiveness of the proposed method is verified through simulation and experimentation.(3)The paper proposes a neural network control method for a nonlinear two-degree-of-freedom unmanned helicopter system that meets prescribed performance constraints.By introducing a prescribed performance function to represent the system’s constraint conditions and transforming a constrained tracking control problem into an equivalent unconstrained stability problem through error transformation,the proposed neural network controller is designed to ensure that the tracking error converges to a small zero neighborhood while satisfying the prescribed performance constraints.The stability and error convergence of the system are analyzed using the direct Lyapunov method.The effectiveness of the proposed control scheme is verified through simulation and experimentation.