Simultaneous Perturbation Stochastic Approximation Optimization Research Articles

Autonomous flight performance optimization of fixed-wing unmanned aerial vehicle with morphing wingtip

PurposeThe purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.Design/methodology/approachThe main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.FindingsLongitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.Originality/valueThis paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

Simultaneous arm morphing quadcopter and autonomous flight system design

Purpose The purpose of this paper is to obtain values that stabilize the lateral and longitudinal flight of the quadrotor for which the morphing amount and the best Proportional-Integral-Derivative (PID) coefficients are determined by using the simultaneous perturbation stochastic approximation (SPSA) optimization algorithm. Design/methodology/approach Quadrotor consists of body and arms; there are propellers at the ends of the arms to take off and rotors that rotate them. By reducing the angle between mechanism 1 and the rotors with the horizontal plane, the angle between mechanism 2 and the arms, the rotors rise and different configurations are obtained. Conventional multi-rotor aircraft has a fixed fuselage and does not need a tail rotor to change course as helicopters do. The translational and rotational movements are provided by the rotation of the rotors of the aircraft at different speeds by creating moments about the geometric center in 6-degree-of-freedom (DOF) space. These commands sent from the ground are provided by the flight control board in the aircraft. The longitudinal and lateral flight stability and properties of different configurations evaluated by dynamic analysis and simulations in 6 DOF spaces are investigated. An algorithm and PID controller are being developed using SPSA to achieve in-flight position and attitude control of an active deformable aircraft. The results are compared with the results of the literature review and the results of the previous article. Findings With SPSA, the best PID coefficients were obtained in case of morphing. Research limitations/implications The effects of quadrotor arm height and hub angle changes affect flight stability. With the SPSA optimization method presented in this study, the attitude is quickly stabilized. Practical implications With the optimization method, the most suitable PID coefficients and angle values for the lateral and longitudinal flight stability of the quadrotor are obtained. Social implications The transition rate and PID coefficients are determined by using the optimization method, which is advantageous in terms of cost and practicality. Originality/value With the proposed method, the aircraft can change shape to adapt to different environments, and the parameters required for more stable flight for each situation will be calculated, and this will be obtained more quickly and safely with the SPSA optimization method.

Structural optimization of vertical isolated rocking core-moment frames

Dual earthquake-resistant archetypes suffer residual damage following a severe earthquake leading to socio-economic drawbacks. A vertical isolated rocking core-moment frame (VI-RCMF) provides a resilience technique to curtail the seismic vulnerability using the isolation of subsystems. Viscous dampers, installed at each floor level of the VI-RCMF archetype, separate moment frame (MF) from rocking-core (RC). The self-centering RC performs as a passive strong-back core, which can uplift on its toes. Although the VI-RCMFs have exhibited superior performance compared to non-isolated dual frames, there are analytical challenges to come up with the optimal design due to the complex interaction of subsystems. This paper presents an optimization framework for quantifying the optimal design parameters of VI-RCMFs. Accordingly, the simultaneous perturbation stochastic approximation (SPSA) method is employed as the optimization algorithm. The objective function is defined to minimize the displacement of MF and the design vector includes mass, stiffness, and damping ratios. The SPSA optimization analyses are conducted for a set of archetypes using OpenSees software. Optimal normalized responses of subsystems are computed for 44 far-field ground motions and aleatory uncertainties are quantified for optimal design variables. The results demonstrate the effectiveness of the proposed procedure for optimizing VI-RCMFs. The derived optimal design vector can be used for the preliminary design of low-to mid-rise archetypes.

Simultaneous autonomous system and powerplant design for morphing quadrotors

PurposeThis study aims to optimize autonomous performance (i.e. both longitudinal and lateral) and endurance of the quadrotor type aerial vehicle simultaneously depending on the autopilot gain coefficients and battery weight.Design/methodology/approachQuadrotor design processes are critical to performance. Unmanned aerial vehicle durability is an important performance parameter. One of the factors affecting durability is the battery. Battery weight, energy capacity and discharge rate are important design parameters of the battery. In this study, proper autopilot gain coefficients and battery weight are obtained by using a stochastic optimization method named as simultaneous perturbation stochastic approximation (SPSA). Because there is no direct correlation between battery weight and battery energy density, artificial neural network (ANN) is benefited to obtain battery energy density corresponding to resulted battery weight found from SPSA algorithm. By using the SPSA algorithm optimum performance index is obtained, then obtained data is used for longitudinal and lateral autonomous flight simulations.FindingsWith SPSA, the best proportional integrator and derivative (PID) coefficients and battery weight, energy efficiency and endurance were obtained in case of morphing.Research limitations/implicationsIt takes a long time to find the most suitable battery values depending on quadrotor endurance. However, this situation can be overcome with the proposed SPSA.Practical implicationsIt is very useful to determine quadrotor endurance, PID coefficients and morphing rate using the optimization method.Social implicationsDetermining quadrotor endurance, PID coefficients and morphing rate using the optimization method provides advantages in terms of time, cost and practicality.Originality/valueThe proposed method improves quadrotor endurance. In addition, with the SPSA optimization method and ANN, the parameters required for endurance will be obtained faster and more securely. In addition, the energy density according to the battery weight also contributes to the clean environment and energy efficiency.

AN OPTIMIZATION-BASED APPROACH TO SPECIAL-EVENTS TRAFFIC SIGNAL TIMING CONTROL

A sudden traffic surge immediately after special events (e.g., conventions, sporting events, concerts) can create substantial traffic congestion in the area where the events are held. One desirable solution is to develop a short-term traffic signal timing adjustment for the high-volume traffic movements associated with special events so that progression is as efficient as possible. In this paper, we present a case study of special-events traffic signal timing control for a small-scale network. A neural network (NN) is used as a signal controller with its weights determined via the Simultaneous Perturbation Stochastic Approximation (SPSA) method. The SPSA optimization is conducted by minimizing a chosen tolerance index as our performance criterion. The timing plans are developed, and the performance evaluations using the existing signal timing and the one generated by the proposed algorithm are also investigated. Our study shows the advantage and potential of using the NN-based SPSA optimization approach to special-events traffic signal timing control. Although this paper presents a case study, the results can be easily modified and applied to large-scale events traffic control.

A modified second‐order SPSA optimization algorithm for finite samples

AbstractWe propose a modification to the simultaneous perturbation stochastic approximation (SPSA) methods based on the comparisons made between the first‐ and second‐order SPSA (1SPSA and 2SPSA) algorithms from the perspective of loss function Hessian. At finite iterations, the accuracy of the algorithm depends on the matrix conditioning of the loss function Hessian. The error of 2SPSA algorithm for a loss function with an ill‐conditioned Hessian is greater than the one with a well‐conditioned Hessian. On the other hand, the 1SPSA algorithm is less sensitive to the matrix conditioning of loss function Hessians. The modified 2SPSA (M2SPSA) eliminates the error amplification caused by the inversion of an ill‐conditioned Hessian. This leads to significant improvements in its algorithm efficiency in problems with an ill‐conditioned Hessian matrix. Asymptotically, the efficiency analysis shows that M2SPSA is also superior to 2SPSA in a large parameter domain. It is shown that the ratio of the mean square errors for M2SPSA to 2SPSA is always less than one except for a perfectly conditioned Hessian or for an asymptotically optimal setting of the gain sequence. Copyright © 2002 John Wiley & Sons, Ltd.

Adaptive optics based on analog parallel stochastic optimization: analysis and experimental demonstration

Wave-front distortion compensation using direct system performance metric optimization is studied both theoretically and experimentally. It is shown how different requirements for wave-front control can be incorporated, and how information from different wave-front sensor types can be fused, within a generalized gradient descent optimization paradigm. In our experiments a very-large-scale integration (VLSI) system implementing a simultaneous perturbation stochastic approximation optimization algorithm was applied for real-time adaptive control of multielement wave-front correctors. The custom-chip controller is used in two adaptive laser beam focusing systems, one with a 127-element liquid-crystal phase modulator and the other with beam steering and 37-control channel micromachined deformable mirrors. The submillisecond response time of the micromachined deformable mirror and the parallel nature of the analog VLSI control architecture provide for high-speed adaptive compensation of dynamical phase aberrations of a laser beam under conditions of strong intensity scintillations. Experimental results demonstrate improvement of laser beam quality at the receiver plane in the spectral band up to 60 Hz.