Control optimization method for helicopters carrying suspended loads

• US 8,190,307 B2
• Filed: 08/23/2010
• Issued: 05/29/2012
• Est. Priority Date: 08/23/2010
• Status: Expired due to Fees

• Current Assignee: King Fahd University of Petroleum & Minerals (Government of Saudi Arabia)
• Original Assignee: King Fahd University of Petroleum & Minerals (Government of Saudi Arabia)
• Inventors: Omar, Hanafy M.
• Primary Examiner(s): Mawari, Redhwan K
• Application Number:
  US12/861,541
• Publication Number:
  US 20120043414A1
• Time in Patent Office: 645 Days
• Field of Search: 701/3, 701/4, 701/27, 701/40, 701/44, 701/46, 701/57, 701/58, 701/59, 701/60, 701/10, 701/98, 701/106, 700/28, 700/29, 700/48, 700/50, 700/54, 700/55, 244/17.11, 244/26, 244/3.21, 244/164, 244/171, 244/177, 244/184, 244/183, 244/191, 706/2, 706/3, 706/4, 706/5, 706/6, 706/13, 706/903, 706/905, 706/913
• US Class Current: 701/4
• CPC Class Codes: G05D 1/102 specially adapted for vertical take-off of aircraft

Abstract

The control optimization method for helicopters carrying suspended loads during hover flight utilizes a controller based on time-delayed feedback of the load swing angles. The controller outputs include additional displacements, which are added to the helicopter trajectory in the longitudinal and lateral directions. This simple implementation requires only a small modification to the software of the helicopter position controller. Moreover, the implementation of this controller does not need rates of the swing angles. The parameters of the controllers are optimized using the method of particle swarms by minimizing an index that is a function of the history of the load swing. Simulation results show the effectiveness of the controller in suppressing the swing of the slung load while stabilizing the helicopter.

Claims

1. A control optimization method for a helicopter carrying a suspended load while in hover flight, the method comprising the steps of:

designing a helicopter attitude and position tracking controller, the helicopter attitude and position tracking controller generating outputs for stabilizing the helicopter while accepting tracking commands from a reference source and displacement commands from a feedback source as inputs, the design including feedback gain (k) based on minimizing a load swing history, wherein the load swing history is represented by a Linear Quadratic Regulator method, the Linear Quadratic Regulator method depending on minimizing the quadratic function;

2. The control optimization method for a helicopter according to claim 1, further comprising the step of generating a damping map of stable and unstable regions of the load swing, said particle swarm optimization algorithm being operable to find optimum gains and time delays over the stable region of the load swing damping map.

3. The control optimization method for a helicopter according to claim 1, further comprising the step of using approximately thirty particles, a maximum particle velocity of approximately two, and a learning factor of approximately one in said particle swarm optimization algorithm.

4. A computer software product, comprising a non-transitory storage medium readable by a processor, the medium having stored thereon a set of instructions for establishing optimized control parameters for a helicopter carrying a suspended load while in hover flight, the set of instructions including:

(a) a first sequence of instructions which, when executed by the processor, causes said processor to configure a helicopter attitude and position tracking controller, the helicopter attitude and position tracking controller being designed to generate outputs for stabilizing the helicopter while accepting tracking commands from a reference source and displacement commands from a feedback source as inputs, the design configuration including feedback gain k based on minimizing a load swing history, wherein the load swing history is represented by a Linear Quadratic Regulator method, the Linear Quadratic Regulator method depending on minimizing the quadratic function,

5. The computer software product according to claim 4, wherein the set of instruction further comprises a seventh sequence of instructions which, when executed by the processor, causes said processor to generate a damping map of stable and unstable regions of said load swing, said particle swarm optimization algorithm being operable to find optimum gains and time delays over said stable region of said load swing damping map.

6. The computer software product according to claim 4, wherein the set of instruction further comprises an eighth sequence of instructions which, when executed by the processor, causes said processor to use approximately thirty particles, a maximum particle velocity of approximately two, and a learning factor of approximately one in said particle swarm optimization algorithm.