1. Field of the Invention
The present invention relates to feedback control system optimization, and more specifically to a control optimization method for helicopters carrying suspended loads that provides an anti-swing feedback control system for the loads carried by the helicopter.
2. Description of the Related Art
Helicopters can be used in carrying heavy loads in civil, military, and rescue operations where the use of ground based equipment would be impractical or impossible. An unmanned small-scale helicopter can be used also in landmine detection application. The global landmine problem is indeed significant, with the United Nations estimating that there are more than 100 million mines in the ground and that 50 people are killed each day by mines worldwide. The idea is to suspend the mine detection equipment as a slung load underneath a low-cost model helicopter, which has the considerable advantage over a ground based vehicle that it needs no contact with the ground. Therefore, there is no risk of the mines being detonated in the detection process. In these applications, the external load behaves like a pendulum. If the pendulous motion of the load exceeds certain limits, it may damage the load or threaten the life of the rescued person. Moreover, the external load can change natural frequencies and mode shapes of the low frequency modes of the helicopter. In addition, the aerodynamics of the load may make it unstable in certain flight conditions. These problems slow or even prevent an accurate pickup or placement of the load. Moreover, it adds extra effort on the pilot.
The dynamics of a helicopter with external suspended loads received considerable attention in the late 1960's and early 1970's. Two reasons for this interest were the extensive external load operations in the Vietnam War, and the Heavy Lift Helicopter program (HLH). This interest has been renewed recently with the advances in modern control technologies. A lot of efforts were made for modeling the slung load and studying its effect on helicopter dynamics, however there are relatively few works that discussed control of a helicopter sling. Examples of automatic control for helicopters with slung loads include a single-cable dynamic model developed using a straightforward application of Lagrange equations, and an expanded version of this model, which includes two tandem cables. However, such a formulation was based on the Newton-Euler equations of motion for small perturbations separated into longitudinal and lateral sets. The disadvantage was that aerodynamic forces on the cables and the load were neglected, as were the helicopter rotor dynamic modes.
Other methods involved the computerized simulation of a helicopter and external load in real time with a pilot in the loop. Load aerodynamics was incorporated into the model, as well as rotor-downwash effects in hover.
Moreover, other developed control requirements for sling-load stabilization involved linearized equations of motion of the helicopter, winch, cable, and load for variable suspension geometry and were then used in conjunction with modern control theory resulting in a design of several control systems for each type of suspension.
Other researchers examined the feasibility of stabilizing external loads by means of controllable fins attached to the cargo. A major disadvantage was that in such a system, a simple linear model representing the yawing and the pendulous oscillations of the slung-load system assumes that the helicopter motion is unaffected by the load. Additionally, the use of active aerodynamic Load Stabilization Systems for a helicopter sling-load system has been investigated.
All the above studies are based on the classical control techniques. What is needed is a new anti-swing controller for a helicopter slung load system near hover flight. Such a controller should be based on time-delayed feedback of the load swing angles. The output from such a controller would be additional displacements that are added to the helicopter trajectory in the longitudinal and lateral directions. Hence, its implementation would be simple and need small modification to the software of a helicopter position controller. It would be desirable to determine parameters of the controllers using particle swarms optimization technique (PSO) by minimizing an index, which is a function of the history of load swing.
Particle swarm optimization is a population based stochastic optimization technique, which is inspired by social behavior of bird flocking, or fish schooling. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. Compared to GA, the advantages of PSO are that PSO is easy to implement and there are few parameters to adjust.
Moreover, PSO like all evolutionary algorithms optimizes a performance index based on input/output relationships only; therefore, minimal knowledge of the plant under investigation is required. In addition, because derivative information is not needed in the execution of the algorithm, many pitfalls that gradient search methods suffer from can be overcome.
Thus, a control optimization method for helicopters carrying suspended loads solving the aforementioned problems is desired.
USPTO Backgrounds
USPTO Backgrounds