Linear Quadratic Gaussian Control Approach Research Articles

LQG controller design for a quadrotor UAV based on particle swarm optimisation

This paper deals with the modelling and the linear quadratic Gaussian (LQG) control design of a quadrotor unmanned aerial vehicle (UAV) using different particle swarm optimisation (PSO) variants. Such a PSO-designed LQG controller is optimised in order to stabilise the position and the heading of the studied vertical take-off and landing (VTOL) quadrotor. Both canonical and recent variants of PSO algorithm, with linearly decreasing of inertia weight (PSO-In) and perturbed updating strategy (PSO-gbest), are considered for the systematically design and tuning of the LQG weighting matrices. These effective control parameters of the LQG approach represent the decision variables of the PSO-based LQG optimisation problem. Such an optimisation problem is formulated to minimise various performance time-domain criteria, like the integral of absolute error (IAE) and the maximum overshoot (MO) index, under nonlinear constraints related to the step responses of the closed-loop quadrotor dynamics. All proposed PSO algorithms are compared with each other and with the well known harmony search algorithm (HSA) and water cycle algorithm (WCA) metaheuristics for the stabilisation problem of the position and heading dynamics of the VTOL drone. Demonstrative simulation results are carried out in order to show the effectiveness of the proposed PSO variants-tuned LQG control approach.

LQG controller design for a quadrotor UAV based on particle swarm optimisation

This paper deals with the modelling and the linear quadratic Gaussian (LQG) control design of a quadrotor unmanned aerial vehicle (UAV) using different particle swarm optimisation (PSO) variants. Such a PSO-designed LQG controller is optimised in order to stabilise the position and the heading of the studied vertical take-off and landing (VTOL) quadrotor. Both canonical and recent variants of PSO algorithm, with linearly decreasing of inertia weight (PSO-In) and perturbed updating strategy (PSO-gbest), are considered for the systematically design and tuning of the LQG weighting matrices. These effective control parameters of the LQG approach represent the decision variables of the PSO-based LQG optimisation problem. Such an optimisation problem is formulated to minimise various performance time-domain criteria, like the integral of absolute error (IAE) and the maximum overshoot (MO) index, under nonlinear constraints related to the step responses of the closed-loop quadrotor dynamics. All proposed PSO algorithms are compared with each other and with the well known harmony search algorithm (HSA) and water cycle algorithm (WCA) metaheuristics for the stabilisation problem of the position and heading dynamics of the VTOL drone. Demonstrative simulation results are carried out in order to show the effectiveness of the proposed PSO variants-tuned LQG control approach.

Dynamics modeling and advanced metaheuristics based LQG controller design for a Quad Tilt Wing UAV

This paper deals with the modelling and advanced metaheuristics-based tuning of a Linear Quadratic Gaussian (LQG) controller for a particular class of convertible Unmanned Aerial Vehicles (UAVs), called Quad Tilt-Wing (QTW). A dynamical model for the vertical, transition and horizontal flights is firstly derived using the Newton–Euler formalism. Since the main design parameters in the LQG control approach are the weighting matrices which are usually selected by classical and hard trials-errors based procedures, an optimization problem under operating constraints is formulated for systematically tuning these decision variables. Such an optimization-based LQG control problem is systematically solved using advanced and recent metaheuristics such as the Harmonic Search Algorithm, Water Cycle Algorithm (WCA) and Fractional Particle Swarm Optimization-based Memetic Algorithm (FPSOMA). To assess the performance of the proposed algorithms and conclude about their control parameters choices, an empirical comparison study is performed for solving different test functions from the single-objective optimization literature. The classical Particle Swarm Optimization and Ant Bee Colony algorithms are retained as comparison tools. Value- and Friedmans rank-based statistical analyses are presented. After that, experiments are conducted to achieve optimal coefficients of weighting matrices of LQG controllers in solving QTW position and heading stabilization problem. Demonstrative simulations show that the WCA, outperforming each one of other metaheuristics, provides the best results in terms of robustness and performance for the problem in hand. Results verify the efficiency the proposed metaheuristics-tuned LQG control structures.

A Linear Quadratic Gaussian Approach for Power Transfer Maximization of a Point Absorber Wave Energy Converter

This article studies the problem of maximizing the take-off power from a buoy-type point absorber of a wave energy converter. The article offers a solution framework for the above-mentioned problem under the linear quadratic Gaussian control approach. The standard Kalman estimator technique is used to estimate the position and velocity of the two-body wave energy system. The optimal power take-off force is computed, and hence, the maximum output power is achieved. A comparison with a conventional approach reflects the superiority of this framework even under different values of a mooring constant.

Kalmen estimator as a robust solution for output power maximization of wave energy converter

In this paper, the application of linear quadratic Gaussian (LQG) control for a buoy‐type point absorber of a wave energy converter (PA‐WEC) system is investigated. The proposed wave energy conversion is considered as a two‐body system, which is taut‐anchored to the sea floor using three cables. The main goal of this study is to extract the maximum available power from the ocean wave. This is accomplished via determining the optimal value of the force exerted on the power take‐off (PTO) system taking in account the physical constraints on the position and velocity. First, the reduced nonlinear dynamical model of the WEC system is obtained. The nonlinearity in the mooring force is replaced by a linear law to yield the state space linear model of the system. Then, the standard Kalman filter technique is employed to estimate the full states of the system. Based on the LQG control approach, the optimal PTO force is computed at which the maximum output power can be easily harvested. The computational burden is minimized to a great extent by computing the optimal state feedback gains and the Kalman state space model offline. The feasibility of the proposed control approach in extracting the optimal power of the ocean wave is validated via the simulation example even under different values of the mooring constant and without violating the system limitation. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Linear quadratic Gaussian control for adaptive optics and multiconjugate adaptive optics: experimental and numerical analysis

We present a comprehensive analysis of the linear quadratic Gaussian control approach applied to adaptive optics (AO) and multiconjugated AO (MCAO) based on numerical and experimental validations. The structure of the control law is presented and its main properties discussed. We then propose an extended experimental validation of this control law in AO and a simplified MCAO configuration. Performance is compared with end-to-end numerical simulations. Sensitivity of the performance regarding tuning parameters is tested. Finally, extension to full MCAO and laser tomographic AO (LTAO) through numerical simulation is presented and analyzed.

A dynamic sliding-mode controller with fuzzy adaptive tuning for an active suspension system

This paper introduces a controller consisting of two control loops for an active suspension system to improve the passenger comfort and manipulability of vehicles. The inner loop controls the hydraulic actuator to track a desired actuation force, and the outer loop provides the control force according to the external conditions. In order to alleviate the discontinuous jump in the inner loop, which is caused by the rate of change in the control force, a dynamic sliding-mode controller is proposed in the outer loop. A fuzzy adaptive tuning controller is adopted to improve the robustness and stability of the controller. The simulation results indicate that the proposed controller is more effective and robust in the vibration isolation of the vehicle body than is the linear quadratic Gaussian control approach, a type of conventional sliding-mode controller.