A.J. Chipperfield

Simplifying Particle Swarm Optimization

The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. The focus of past research has been with making the PSO method more complex, as this is frequently believed to increase its adaptability to other optimization problems. This study takes the opposite approach and simplifies the PSO method. To compare the efficacy of the original PSO and the simplified variant here, an easy technique is presented for efficiently tuning their behavioural parameters. The technique works by employing an overlaid meta-optimizer, which is capable of simultaneously tuning parameters with regard to multiple optimization problems, whereas previous approaches to meta-optimization have tuned behavioural parameters to work well on just a single optimization problem. It is then found that not only the PSO method and its simplified variant have comparable performance for optimizing a number of Artificial Neural Network problems, but also the simplified variant appears to offer a small improvement in some cases.

Multiobjective gas turbine engine controller design using genetic algorithms

This paper describes the use of multiobjective genetic algorithms (MOGAs) in the design of a multivariable control system for a gas turbine engine. The mechanisms employed to facilitate multiobjective search with the genetic algorithm are described with the aid of an example. It is shown that the MOGA confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This allows the engineer to examine the trade-offs between the different design objectives and configurations during the course of an optimization. In addition, the paper demonstrates how the genetic algorithm can be used to search in both controller structure and parameter space thereby offering a potentially more general approach to optimization in controller design than traditional numerical methods. While the example in the paper deals with control system design, the approach described can be expected to be applicable to more general problems in the fields of computer aided design (CAD) and computer aided engineering (CAE).

Fuzzy scheduling control of a gas turbine aero-engine: a multiobjective approach

This paper investigates the use of a nonconventional approach to control a gas turbine aero-engine. The rationale behind this study is the need to develop advanced tools and techniques that can assist in improving the performance of the system and simultaneously enhance the flexibility of the control strategy. Modern techniques are required for many complex systems where increasingly strict performance and regulatory requirements must be achieved. This is particularly true of aerospace systems where consideration of safety, reliability, maintainability, and environmental impact are all necessary as part of the control requirements. This paper investigates a combination of two such potential techniques: fuzzy logic and evolutionary algorithms. Emerging from new requirements for gas turbine aero-engine control, a flexible gain scheduler is developed and analyzed. A hierarchical multiobjective genetic algorithm is employed to search and optimize the potential solutions for a wide envelope controller covering idle, cruise, and full-power conditions. The overall strategy is demonstrated to be a straightforward and feasible method of refining the control system performance and increasing its flexibility.

Nonlinear modelling of a twin rotor MIMO system using radial basis function networks

Modelling of innovative aircraft such as UAVs, X-wing, tilt body and delta-wing is not easy. This paper presents a nonlinear system identification method for modelling air vehicles of complex configuration. This approach is demonstrated through a laboratory helicopter. Extensive time and frequency-domain model-validation tests are employed to instil confidence in the estimated model. The estimated model has a good predictive capability and can be utilized for nonlinear flight simulation studies. The approach presented is suitable for modelling new generation air vehicles.

Multi-objective optimization approach to the ALSTOM gasifier problem

Abstract A control system design procedure based on the optimization of multiple objectives is used to realize the control design specifications of the linear gasification plant models. A multi-objective genetic algorithm (MOGA) is used in conjunction with an H∞ loop-shaping design procedure (LSDP) in order to satisfy the requirements of this critical system. The H∞ LSDP is used to guarantee the stability and robustness of the controller while its associated weighting matrix parameters are selected using the multi-objective search method in order to achieve performance requirements. A controller emerges which is stable but unable to completely meet some of the control objectives. Despite this shortcoming, the study is an excellent vehicle for introduction to an effective H∞ loop-shaping procedure. Further work, beyond the scope of this challenge has subsequently produced an improved controller design.

Dynamic modeling and optimal control of a twin rotor MIMO system

A dynamic model for the characterising of a one-degree-of-freedom (DOF) twin rotor MIMO system (TRMS) in hover is extracted using a black-box system identification technique. The behaviour of the TRMS in certain aspects resembles that of a helicopter. Hence, it is an interesting identification and control problem. Identification for a 1-DOF rigid-body, discrete-time linear model is presented. The extracted model is employed in the design of a feedback LQG compensator. This has a good tracking capability, but requires high control effort and has inadequate authority over residual vibration of the system. These problems are resolved by further augmenting the system with a command path prefilter. The combined feedforward and feedback compensator satisfies the performance objectives and obeys the actuator constraint.

Evaluation of a new high power, wide separation laser Doppler probe: Potential measurement of deeper tissue blood flow

OBJECTIVE To compare the output from a novel high power, wide separation laser Doppler flow probe (DP1-V2-HP, 4 mm, with IRLD20) with that of a standard flow probe (DP1-V2, 0.5 mm, with DRT4) (Moor UK) and to explore its potential for use in the noninvasive measurement of blood flow in deeper tissues in humans. METHODS Monte Carlo modeling was used to predict depths of light scattering in skin with each probe, geometry. Experimentally, forearm blood flow was measured at rest and during local warming of the skin surface and post occlusion reactive hyperaemia (PORH). Laser Doppler blood flux (LDF) and the power spectral density of its component frequency intervals, were compared. RESULTS Monte Carlo modeling indicated that while the majority of wide probe LD signal derives from deeper tissue, a significant portion is from superficial (dermal) tissue (and vice versa for standard probe). Perturbation of local blood flow differentially increased LDF and spectral power as measured by the two probes, with the standard skin probe showing a significantly greater response to local skin warming (p<0.01). CONCLUSIONS These differences support our hypothesis that the wide probe is recording predominantly blood flux within the vasculature of sub-dermal tissue. This is in agreement with Monte Carlo simulation.

Parametric modelling and dynamic characterization of a two-degree-of-freedom twin-rotor multi-input multi-output system:

Abstract A mathematical model for the dynamic characterization of a two-degree-of-freedom (2 DOF) twin-rotor multi-input multi-output (MIMO) system (TRMS) in hover is extracted using a black box system identification technique. The behaviour of the TRMS, in certain aspects, resembles that of a helicopter, with a significant cross-coupling between longitudinal and lateral directional motions. Hence, it is an interesting identification and control problem. Identification for a 2 DOF, rigid-body, discrete-time linear model is presented in detail. The extracted model has a good degree of prediction capability. The modelling approach presented is suitable for complex new-generation air vehicles.

Reconfigurable flight control strategies using model predictive control

Tracking control for large amplitude manoeuvres in the presence of damages to airframe and control surfaces is addressed. The control reconfiguration algorithm is based on model predictive control, a constrained receding horizon optimisation is solved under the constraints of hard limits of actuator position and rate saturations and critical aircraft state limits imposed by allowable structural loads. Changed stability and control derivatives of the damaged aircraft are identified online and used by the receding horizon controller as internal model for prediction. An emphasis is given to incorporate handling quality specifications according to MIL-STD-1797A. The results demonstrate the ability of the proposed scheme to maintain level flight after a failure, to track the pilot commands despite the loss of actuator effectiveness, and to coordinate the use of the remaining active control surfaces to provide the decoupling between the rotational axes. Finally, the issue of online (onboard) implementation of constrained optimisation is examined.

Dynamic modelling and open-loop control of a twin rotor multi-input multi-output system

Abstract A dynamic model for a one-degree-of-freedom (DOF) twin rotor multi-input multi-output (MIMO) system (TRMS) in hover is obtained using a black-box system identification technique. The behaviour of the TRMS in certain aspects resembles that of a helicopter; hence, it is an interesting identification and control problem. This paper investigates modelling and open-loop control of the longitudinal axis alone, while the lateral axis movement is physically constrained. It is argued that some aspects of the modelling approach presented are suitable for a class of new generation or innovative air vehicles with complex dynamics. The extracted model is employed for designing and implementing a feedforward/open-loop control. Open-loop control is often the preliminary step for development of more complex feedback control laws. Open-loop control strategies using shaped command inputs are accordingly investigated for resonance suppression in the TRMS. Digital low-pass and band-stop filter shaped inputs are used on the TRMS testbed, based on the identified vibrational modes. A comparative performance study is carried out and the corresponding results presented. The low-pass filter is shown to result in better vibration reduction.