Browsing by Subject "Control allocation"
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Item Open Access Adaptive control allocation for constrained systems(Elsevier, 2020-06) Tohidi, Seyed Shahabaldin; Yıldız, Yıldıray; Kolmanovsky, I.This paper proposes an adaptive control allocation approach for uncertain over-actuated systems with actuator saturation. The proposed control allocation method does not require uncertainty estimation or persistency of excitation. Actuator constraints are respected by employing the projection algorithm. The stability analysis is provided for two different cases: when ideal adaptive parameters are inside and when they are outside of the projection boundary which is chosen consistently with the actuator saturation limits. Simulation results for the Aerodata Model in Research Environment (ADMIRE), which is used as an example of an over-actuated aircraft system with actuator saturation, demonstrate the effectiveness of the proposed method.Item Open Access Adaptive control allocation for over-actuated systems with actuator saturation(Elsevier B.V., 2017) Tohidi, Seyed Shahabaldin; Yıldız, Yıldıray; Kolmanovsky, IlyaThis paper proposes an adaptive control allocation approach for over-actuated systems with actuator saturation. The methodology can tolerate actuator loss of effectiveness without utilizing the control input matrix estimation, eliminating the need for persistence of excitation. Closed loop reference model adaptive controller is used for identifying adaptive parameters, which provides improved performance without introducing undesired oscillations. The modular design of the proposed control allocation method improves the flexibility to develop the outer loop controller and the control allocation strategy separately. The ADMIRE model is used as an over-actuated system, to demonstrate the effectiveness of the proposed method using simulation results.Item Open Access Adaptive control of cyberphysical human systems(Bilkent University, 2021-08) Tohidi, Seyed ShahabaldinThis dissertation focuses on the control of cyberphysical human systems in the presence of actuators’ redundancy and constraints. A novel adaptive control tech-nique is proposed to allocate control signals among redundant actuators in the presence of uncertainty and actuator saturation. The proposed method does not require any uncertainty identification or persistency of excitation assumption. The stability of the proposed method is guaranteed using Lyapunov stability analysis. In addition, a modified projection operator that can be implemented to the adaptive control allocation is proposed. This operator enables the allo-cator to handle both magnitude and rate limits of actuators. A novel sliding mode controller with time-varying sliding surface is designed to complement the adaptive allocator and guarantee stability and reference tracking in the presence of uncertainty and actuator saturation. This controller is robust to both adap-tive control allocation error and external disturbance. Furthermore, an adaptive human model is proposed to mimic the human control response in the presence of uncertainty. The proposed structure is based on the model reference adaptive control, and the adaptive laws are obtained using the Lyapunov-Krasovskii stabil-ity criteria. To validate this model, an experimental setup is employed to collect data and a statistical analysis is conducted to measure the predictive power of the pilot model. Finally, the stability limits of a human-in-the-loop closed loop control system, where the plant to be controlled has redundant actuators with uncertain dynamics, are demonstrated. Various human models with and without time delays are investigated. Simulation results are provided to demonstrate the effectiveness of the proposed methods in each chapter.Item Open Access A control allocation technique to recover from driver-induced oscillations(Bilkent University, 2019-12) Sarwar, AyeshaThe focus of this thesis is the study of driver induced oscillations. When the rear tires of a car are force saturated due to aggressive driver behaviour, high velocity on a slippery road, sudden steering action or heavy braking, rear end of the car tends to lose traction on the road and starts skidding. At the same time, the driver, having the direct steering control over the front two tires, feels a time delay at the response of the rear tires to the steering actions. The delay between the driver's action and the vehicle's response may eventually instigate the swinging of the rear end of the vehicle, which is generally referred to as \fishtailing". In the literature, few studies exist regarding fishtailing motion and in those studies, the theoretical background of this motion is not studied in detail. In this thesis, fishtailing motion dynamics are investigated in detail by employing a non linear vehicle model, and this motion is recreated for a specific vehicle configuration. Then, a control allocation technique is presented to recover from this driverinduced fishtailing motion. The proposed control allocation method helps the vehicle recover from these undesired oscillations by minimizing the phase shift between the commanded and realized forces and moments. The simulation results demonstrate that using the proposed method, it is possible to make the vehicle recover from driver induced oscillations even at high velocities where conventional approaches fail. For the cases of actuator failure, for example loss of tire inflation pressure, an adaptive version of the control allocation is also proposed that can stabilize the vehicle even when the actuator effectiveness is reduced significantly.Item Open Access Development of a fault-tolerant model predictive controller for vehicle lateral stability(Bilkent University, 2023-08-01) Köysüren, Muhammed KemalRecently, there has been an increased interest in the automotive industry using scaled test vehicles to test the real-time performance of modeling and control algorithms. A scaled prototype, specifically developed and instrumented for enhancing vehicle lateral stability, offers distinct advantages in terms of cost reduction and the ability to repeat tests under various vehicle maneuvering scenarios rapidly. First, the mechatronic design of a 1:8 scaled electric vehicle with 4-wheel-drive and 4-wheel-independent-steering was done and the prototype vehicle was built. Plant model parameters such as the cornering coefficients of the tires are estimated using various methods such as traditional neural network training, a Physics Informed Deep Learning (PIDL) algorithm, and Pacejka’s tire modeling procedure. Secondly, a fault-tolerant reconfigurable model predictive controller (MPC) is proposed to enhance reference tracking for four-wheel-drive and four-wheel-steering vehicles under concurrent steering actuator faults. The method detects, isolates, and estimates fault magnitudes, which inform adjustments to the MPC formula. Performance validation is conducted through obstacle avoidance maneuvers with a control-oriented vehicle model and real-time applicability tests with a Processor-in-the-Loop system using a high-fidelity vehicle model. The test results confirm the proposed algorithm’s superior performance over the conventional MPC. Lastly, a computationally efficient two-path optimal control allocation method is proposed to reduce controller block execution time in vehicle ECU. High-fidelity results prove the computational cost reduction of the proposed algorithm over the conventional allocation method.Item Open Access Handling actuator magnitude and rate saturation in uncertain over-actuated systems: a modified projection algorithm approach(Taylor and Francis, 2020-09-24) Tohidi, Seyed Shahabaldin; Yıldız, YıldırayThis paper proposes a projection algorithm which can be employed to bound actuator signals, in terms of both magnitude and rate, for uncertain systems with redundant actuators. The investigated closed-loop control system is assumed to contain an adaptive control allocator to distribute the total control input among actuators. Although conventional control allocation methods can handle actuator rate and magnitude constraints, they cannot consider actuator uncertainty. On the other hand, adaptive allocators manage uncertainty and actuator magnitude limits. The proposed projection algorithm enables adaptive control allocators to handle both magnitude and rate saturation constraints. A mathematically rigorous analysis is provided to show that with the help of the proposed projection algorithm, the performance of the adaptive control allocator can be guaranteed, in terms of error bounds. Simulation results are presented, where the Aero-Data Model In Research Environment (ADMIRE) is used to demonstrate the effectiveness of the proposed methodItem Open Access Time-varying sliding mode controller for over-actuated systems with constrained and uncertain actuators in flightcontrol applications(John Wiley and Sons, Ltd, 2022-10-29) Yildiray, Yildiz; Kolmanovsky, Ilya; Tohidi, Seyed ShahabaldinOne solution to the problem of distributing the control action among redundant actuators with uncertain dynamics is employing an adaptive control allocator. This paper proposes a sliding mode controller which exploits a time-varying sliding surface to complement adaptive control allocation in the presence of actuator saturation. The proposed approach does not require error augmentation for tracking desired references, which diminishes the computational burden. Aerodata Model in Research Environment, which is an over-actuated aircraft model, is adopted to demonstrate the efficacy of the proposed controller in simulation studies.Item Open Access Trajectory control of a quadrotor using a control allocation approach(IEEE, 2017) Zaki, H.; Ünel, M.; Yıldız, YıldırayA quadrotor is an underactuated unmanned aerial vehicle with four inputs to control the dynamics. Trajectory control of a quadrotor is a challenging task and usually tackled in a hierarchical framework where desired/reference attitude angles are analytically determined from the desired command signals, i.e. virtual controls, that control the positional dynamics of the quadrotor and the desired yaw angle is set to some constant value. Although this method is relatively straightforward, it may produce large and nonsmooth reference angles which must be saturated and low-pass filtered. In this work, we show that the determination of desired attitude angles from virtual controls can be viewed as a control allocation problem and it can be solved numerically using nonlinear optimization where certain magnitude and rate constraints can be imposed on the desired attitude angles and the yaw angle need not be constant. Simulation results for both analytical and numerical methods have been presented and compared. Results for constrained optimization show that the flight performance is quite satisfactory.