Gain Scheduled Linear Quadratic Regulator Research Articles

Intelligent momentary assisted control for autonomous emergency braking

Development of control algorithms for enhancing performance in safety-critical systems such as the Autonomous Emergency Braking (AEB) system is an important issue in the emerging field of automated electric vehicles. In this study, we model a safety distance-based hierarchical AEB control system constituted of a high-level Rule-Based Supervisory control module, an intermediate-level switching algorithm and a low-level control module. The Rule Based supervisor determines the required deceleration command that is fed to the low-level control module via the switching algorithm. In the low-level, two wheel slip control algorithms were developed, a Robust Sliding Mode control algorithm with an Artificial Neural Network (ANN) for nonlinear parameter estimation and a Gain-Scheduled Linear Quadratic Regulator. For the needs of this control design, a non-linear dynamic vehicle model was implemented whereas a constant tire-road friction coefficient was considered. The proposed control system was validated in Simulink, assuming a straight-line braking maneuver on a flat dry road. The simulation results demonstrated satisfactory emergency braking performance with full collision avoidance in both proposed control system combinations.

A novel adaptive-rear axles steering controller for an 8 × 8 combat vehicle

Multi-axle vehicles are widely used in several applications such as transportation, industrial, and military field, because of its higher reliability in comparison with conventional two axles vehicles. Despite that, there is a paucity of research studies that consider lateral stability enhancement of these vehicles, especially on rough terrain. This simulation-based research study fills this gap and introduces a new adaptive Active Rear Steering (ARS) controller that improves the lateral stability of an 8x8 combat vehicle for rough-terrain operation. The developed controller is designed utilizing the Integral Sliding Mode Control theory (ISMC) based on Gain-Scheduled Linear Quadratic Regulator (GSLQR). Besides, the GSLQR control gains are optimized by a Genetic Algorithm (GA) toolbox using a new synthesized cost function to ensure asymptotic stability. Furthermore, a new Adaptive-ISMC (AISMC) is introduced by using genetic programming to generate control equations that can replace the developed high-dimension GSLQR gains and facilitate future hardware implementation. The developed controller is evaluated by performing a series of simulation-based Double Lane Change (DLC) maneuvers on several rough terrains. The evaluation is conducted for both high friction and slippery surfaces at high and moderate speed, consequently. The results show high fidelity and robustness of the developed controller in comparison with a previously designed optimal LQR controller.

Hybrid Trajectory Optimization Method and Tracking Guidance for Variable-Sweep Missiles

In this paper, an offline hybrid trajectory optimization approach is proposed for variable-sweep missiles to explore the superiority in the diving phase. Aiming at the maximal terminal velocity with the impact angle constraint, the trajectory optimization model is formulated under multiple constraints, and the aerodynamic analysis in different sweep angles is discussed. Unlike only the attack angle used for the optimization process traditionally, the two-variable optimization scheme on both the attack angle and sweep angle is investigated for variable-sweep missiles. Then, the trajectory optimization problem is transformed into the nonlinear programming problem via a hybrid optimization strategy combining the Gauss pseudospectral method and direct shooting method to obtain the high precision and fast convergence solution. Finally, to verify the feasibility of the optimal trajectory under uncertainties, the tracking guidance law is designed on basis of the gain scheduled linear quadratic regulator control. Numerical simulation results reveal not only of the proposed hybrid optimization strategy but also of the superiority of variable-sweep missiles compared with traditional missiles.

Modeling and Control of Tower Cranes With Elastic Structure

Due to their lightweight construction, large tower cranes are prone to structural vibrations that pose a major challenge for load sway damping control design and are the main reason for the lack of anti-sway systems in most modern tower cranes. Since neglecting the structural elasticity for feedback control design can lead to instability or poor performance, a detailed dynamic model is required that takes the structural elasticity into account. This contribution presents a control-oriented dynamic model of top-slewing tower cranes as a flexible multibody system assuming jib and tower as nonuniform Euler-Bernoulli beams and taking into account the dynamic couplings between steel structure, load motion, system drives, and rope. The distributed deformations are discretized using the Ritz method. For active sway damping, a two-degree-of-freedom control structure is designed consisting of a nonlinear flatness-based feedforward controller and a gain scheduled linear quadratic regulator. Experimental results at a full-scale tower crane validate the model accuracy and demonstrate the effectiveness of the proposed anti-sway control concept.

Gain-scheduled linear quadratic regulator applied to the stabilization of a riderless bicycle

In this article, a gain-scheduling control is designed to achieve the stabilization of a riderless bicycle. The main contribution is the design of a stabilizing control system including integral ac...

Identification and Multivariable Gain-Scheduling Control for Cloud Computing Systems

This paper presents the dynamic modeling and performance control of a Web server hosted on a private cloud. The cloud hosting Web server is a variable capacity system with two control inputs: 1) the number of virtual machines (VMs), which is indicative of the capacity of the cloud, and 2) the admission control used for regulating workload. As the workload and the hosting conditions change frequently, the linear parameter-varying (LPV) framework is well suited to derive the model. For the hosted Web server, we obtain an multiple input multiple output (MIMO) LPV model with performance metrics such as the response time and the throughput, which is then converted to polytopic LPV form using tensor product transformation. Finally, we design a gain scheduled linear quadratic regulator controller for performance guarantees with optimal cost of VMs. The identification, validation, and control experiments are demonstrated on the open source Eucalyptus cloud platform. The HTTP requests are generated using customized synthetic workload generator tool.

Optimal Transport Approach for Probabilistic Robustness Analysis of F-16 Controllers

This paper presents a new framework for controller robustness verification with respect to an F-16 aircraft’s closed-loop performance in longitudinal flight. The state regulation performance of a linear quadratic regulator and a gain-scheduled linear quadratic regulator are compared, where both controllers are applied to nonlinear open-loop dynamics of an F-16, in the presence of stochastic initial condition and parametric uncertainties, as well as actuator disturbance. It is shown that, in the presence of initial condition uncertainties alone, both the linear quadratic regulator and gain-scheduled linear quadratic regulator have comparable immediate and asymptotic performances, but the gain-scheduled linear quadratic regulator exhibits better transient performance at intermediate times. This remains true in the presence of additional actuator disturbance. Also, the gain-scheduled linear quadratic regulator is shown to be more robust than the linear quadratic regulator against parametric uncertainties. The probabilistic framework proposed here leverages transfer-operator-based density computation in exact arithmetic and introduces optimal transport theoretic performance validation and verification for nonlinear dynamical systems. Numerical results from the proposed method are in unison with Monte Carlo simulations.

5kW급 고체 산화물 연료전지 열관리 계통 LQR 상태 궤환 제어기 설계

Solid oxide fuel cell operate at high temperature (). High temperature have an advantage of system efficiency, but a weak durability. In this study, linear state space controller is designed to handle the temperature of solid oxide fuel cell system for proper thermal management. System model is developed under simulink environment with Thermolib. Since the thermally optimal system integration improves efficiency, very complicated thermal integration approach is selected for system integration. It shows that temperature response of fuel cell stack and catalytic burner are operated at severe non-linearity. To control non-linear temperature response of SOFC system, gain scheduled linear quadratic regulator is designed. Results shows that the temperature response of stack and catalytic burner follows the command over whole ranges of operations.

Bounded gain-scheduled LQR satellite control using a tilted wheel

Generation of control torque for highly agile satellite missions is generally achieved with momentum exchange devices, such as reaction wheels and control moment gyros (CMGs) with high slew maneuverability. However, the generation of a high control torque from the respective actuators requires high power and thus a large mass. The work presented here proposes a novel type of control actuator that generates torques in all three principal axes of a rigid satellite using only a spinning wheel and a simple tilt mechanism. This newly proposed actuator has several distinct advantages including less mass and more simplicity than a conventional CMG and no singularities being experienced during nominal wheel operation. A new high performance bounded (HPB) linear quadratic regulator (LQR) control law has been presented that extends classical LQR by providing faster settling times, gain-scheduling the control input weightings to optimize its performance, and has much quicker computation times than classical LQR. This work derives a fundamental mathematical model of the actuator and demonstrates feasibility by providing three degree of freedom high fidelity simulations for the actuator using both classical LQR and HPB LQR.

Electronic stability control for electric vehicle with four in-wheel motors

The electric vehicle with four direct-driven in-wheel motors is an over actuated system. A three-level control strategy of electronic stability control (ESC) is proposed to achieve optimal torque distribution for four in-wheel motors. The first level is a gain-scheduled linear quadratic regulator which is designed to generate the desired yaw moment command for ESC. Control allocation is the second level which is used to distribute the desired longitudinal tire forces according to the yaw moment command while satisfying the driver’s intent for acceleration and deceleration. The associated weighting matrix is designed using the work load ratio at each wheel to prevent saturating the tire. The third level is slip ratio control (SRC) which is employed at each wheel to generate the desired longitudinal tire force based on a combined-slip tire model. Simulation results show that the proposed method can enhance the ESC performance for the test maneuvers. Since the tire model is often unknown for practical implementation, the effectiveness of the SRC is studied using the sine with dwell test. It is found that the SRC is not crucial for achieving performance similar to the proposed method with SRC, if the slip ratio can be maintained in the stable region using traction control system/anti-lock braking system.