Direct Adaptive Control for Stability and Command Augmentation System of an Air-Breathing Hypersonic Vehicle

In this paper we explore a Direct Adaptive Control scheme for stabilizing a non-linear, physics based model of the longitudinal dynamics for an air breathing hypersonic vehicle. The model, derived from first principles, captures the complex interactions between the propulsion system, aerodynamics, and structural dynamics. The linearized aircraft dynamics show unstable and non-minimum phase behavior. It also shows a strong short period coupling with the fuselage-bending mode. The value added by direct adaptive control and the theoretical requirements for stable convergent operation is displayed. One of the main benefits of the Directive Adaptive Control is that it can be implemented knowing very little detail about the plant. The implementation uses only measured output feedback to accomplish the adaptation. A stability analysis is conducted on the linearized plant to understand the complex aero-propulsion and structural interactions. The multivariable system possesses certain characteristics beneficial to the adaptive control scheme; we discuss these advantages and ideas for future work

A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version

NASA Aircraft Controls Research, 1983

The workshop consisted of 24 technical presentations on various aspects of aircraft controls, ranging from the theoretical development of control laws to the evaluation of new controls technology in flight test vehicles. A special report on the status of foreign aircraft technology and a panel session with seven representatives from organizations which use aircraft controls technology were also included. The controls research needs and opportunities for the future as well as the role envisioned for NASA in that research were addressed. Input from the panel and response to the workshop presentations will be used by NASA in developing future programs

Model Predictive Controller Weight Tuning and Real-Time Learning-Based Weight Selection

A variety of control systems with specific goals are designed and utilized in every vehicle system. Optimal performance of each of these control systems is essential to keep the vehicle in a safe and desirable driving condition. A model predictive controller (MPC) is a type of control system that employs an internal model of the system being controlled to predict its future behavior and determine the optimal control actions to achieve desired outcomes. The controller works by continuously updating its predictions based on the current state of the system and using an optimization algorithm to calculate the best control actions while satisfying any constraints on the system. In each MPC controller, there is an objective function with a set of weights. These weights can directly affect the response of the system. The appropriate selection of weights results in the generation of an effective control action, which reduces tracking errors to a minimum. In the conventional MPC controllers, the focus is solely on optimizing the control actions, and weight values remain fixed or scheduled for different ranges of system operations. Therefore, the effects of real-time selection of optimum weights in the controller performance are overlooked. This research aims to improve the performance of MPC control systems by developing a weight tuning and real-time weight selection scheme that considers the dynamic system's state. The proposed approach is applied to the vehicle stability control under a variety of environmental and/or driving conditions. The weight tuning is performed by using the prediction model of the vehicle and the Bayesian optimization (BO) technique. The weight selection is carried out in real-time by learning the adjusted weights through Gaussian process regression (GPR). These are two main modules developed to be used for selecting and tuning the weights of an MPC controller. Hence, in addition to optimizing control actions through the MPC controller's optimization problem, the weights of the MPC controller are also assessed and adjusted to achieve the highest level of optimality in the vehicle control system. Furthermore, an authentication process is proposed to evaluate the tuned weights after being selected in the tests. This way, unnecessary increases or decreases in the weights stored in the weight selection dataset can be avoided. To further enhance the model predictions, a blending-based multiple model approach is utilized. In this approach, instead of considering a fixed prediction model with invariant parameters, a combination of finite number of models with different parameters are considered. Based on the prediction error of each model, a weighted sum of matrices of these models are utilized both in the MPC controller and weight tuning modules. To verify the proposed methodology, MATLAB/Simulink and CarSim co-simulations as well as experimental tests are carried out. Comparing the vehicle responses with and without the proposed weight tuning and real-time weight selection approach strongly corroborates the proposed technique in enhancing the controller performance. The capability of the proposed multiple model technique in improving the weight tuning has been demonstrated in the simulations and experimental results

Integration of anti-lock braking system and regenerative braking for hybrid/electric vehicles

Vehicle electrification aims at improving energy efficiency and reducing pollutant emissions which creates an opportunity to use the electric machines (EM) as Regenerative Braking System (RBS) to support the friction brake system. Anti-lock Braking System (ABS) is part of the active safety systems that help drivers to stop safely during panic braking while ensuring the vehicle’s stability and steerability. Nevertheless, the RBS is deactivated at a safe (low) deceleration threshold in favour of ABS. This safety margin results in significantly less energy recuperation than what would be possible if both RBS and ABS were able to operate simultaneously. Vehicle energy efficiency can be improved by integrating RBS and friction brakes to enable more frequent energy recuperation activations, especially during high deceleration demands. The main aim of this doctoral research is to design and implement new wheel slip control with torque blending strategies for various vehicle topologies using four, two and one EM. The integration between the two braking actuators will improve the braking performance and energy efficiency of the vehicle. It also enables ABS by pure EM in certain situations where the regenerative brake torque is sufficient. A novelmethod for integrating the wheel slip control and torque blending is developed using Nonlinear Model Predictive Control (NMPC). The method is well known for the optimal performance and enforcement of critical control and state constraints. A linear MPC strategy is also developed for comparison purpose. A pragmatic brake torque blending algorithm using Daisy-Chain with sliding mode slip control is also developed based on a pre-defined energy recuperation priority. Simulation using high fidelity model using co-simulation in Matlab/Simulink and CarMaker is used to validate the developed strategies. Different test patterns are used to evaluate the controllers’ performance which includes longitudinal and lateral motions of the vehicle. Comparison analysis is done for the proposed strategies for each case. The capability for real-time implementation of the MPC controllers is assessed in simulation testing using dSPACE hardware

Optimal fault-tolerant flight control for aircraft with actuation impairments

Current trends towards greater complexity and automation are leaving modern technological systems increasingly vulnerable to faults. Without proper action, a minor error may lead to devastating consequences. In flight control, where the controllability and dynamic stability of the aircraft primarily rely on the control surfaces and engine thrust, faults in these effectors result in a higher extent of risk for these aspects. Moreover, the operation of automatic flight control would be suddenly disturbed. To address this problem, different methodologies of designing optimal flight controllers are presented in this thesis. For multiple-input multiple-output (MIMO) systems, the feedback optimal control is a prominent technique that solves a multi-objective cost function, which includes, for instance, tracking requirements and control energy minimisation. The first proposed method is based on a linear quadratic regulator (LQR) control law augmented with a fault-compensation scheme. This fault-tolerant system handles the situation in an adaptive way by solving the optimisation cost function and considering fault information, while assuming an effective fault detection system is available. The developed scheme was tested in a six-degrees-of-freedom nonlinear environment to validate the linear-based controller. Results showed that this fault tolerant control (FTC) strategy managed to handle high magnitudes of the actuator’s loss of effciency faults. Although the rise time of aircraft response became slower, overshoot and settling errors were minimised, and the stability of the aircraft was maintained. Another FTC approach has been developed utilising the features of controller robustness against the system parametric uncertainties, without the need for reconfiguration or adaptation. Two types of control laws were established under this scheme, the H∞ and µ-synthesis controllers. Both were tested in a nonlinear environment for three points in the flight envelope: ascending, cruising, and descending. The H∞ controller maintained the requirements in the intact case; while in fault, it yielded non-robust high-frequency control surface deflections. The µ-synthesis, on the other hand, managed to handle the constraints of the system and accommodate faults reaching 30% loss of effciency in actuation. The final approach is based on the control allocation technique. It considers the tracking requirements and the constraints of the actuators in the design process. To accommodate lock-in-place faults, a new control effort redistribution scheme was proposed using the fuzzy logic technique, assuming faults are provided by a fault detection system. The results of simulation testing on a Boeing 747 multi-effector model showed that the system managed to handle these faults and maintain good tracking and stability performance, with some acceptable degradation in particular fault scenarios. The limitations of the controller to handle a high degree of faults were also presented

Control strategies of series active variable geometry suspension for cars

This thesis develops control strategies of a new type of active suspension for high performance cars, through vehicle modelling, controller design and application, and simulation validation. The basic disciplines related to automotive suspensions are first reviewed and are followed by a brief explanation of the new Series Active Variable Geometry Suspension (SAVGS) concept which has been proposed prior to the work in this thesis. As part of the control synthesis, recent studies in suspension control approaches are intensively reviewed to identify the most suitable control approach for the single-link variant of the SAVGS. The modelling process of the high-fidelity multi-body quarter- and full- vehicle models, and the modelling of the linearised models used throughout this project are given in detail. The design of the controllers uses the linearised models, while the performance of the closed loop system is investigated by implementing the controllers to the nonlinear models. The main body of this thesis elaborates on the process of synthesising H∞ control schemes for quarter-car to full-car control. Starting by using the quarter-car single-link variant of the SAVGS, an H∞ -controlled scheme is successfully constructed, which provides optimal road disturbance and external force rejection to improve comfort and road holding in the context of high frequency dynamics. This control technique is then extended to the more complex full-car SAVGS and its control by considering the pitching and rolling motions in the context of high frequency dynamics as additional objectives. To improve the level of robustness to single-link rotations and remove the geometry nonlinearity away from the equilibrium position, an updated approach of the full-car SAVGS H∞ -controlled scheme is then developed based on a new linear equivalent hand-derived full-car model. Finally, an overall SAVGS control framework is developed, which operates by blending together the updated H∞ controller and an attitude controller, to tackle the comfort and road holding in the high frequency vehicle dynamics and chassis attitude motions in the low frequency vehicle dynamics simultaneously. In all cases, cascade inner position controllers developed prior to the work in this thesis are employed at each corner of the vehicle and combined with the control systems developed in this thesis, to ensure that none of the physical or design limitations of the actuator are violated under any circumstances.Open Acces

Multiple-Model Robust Adaptive Vehicle Motion Control

An improvement in active safety control systems has become necessary to assist drivers in unfavorable driving conditions. In these conditions, the dynamic of the vehicle shows rather different respond to driver command. Since available sensor technologies and estimation methods are insufficient, uncertain nonlinear tire characteristics and road condition may not be correctly figured out. Thus, the controller cannot provide the appropriate feedback input to vehicle, which may result in deterioration of controller performance and even in loss of vehicle control. These problems have led many researchers to new active vehicle stability controllers which make vehicle robust against critical driving conditions like harsh maneuvers in which tires show uncertain nonlinear behaviour and/or the tire-road friction coefficient is uncertain and low. In this research, the studied vehicle has active front steering system for driver steer correction and in-wheel electric motors in all wheels to generate torque vector at vehicle center of gravity. To address robustness against uncertain nonlinear characteristics of tire and road condition, new blending based multiple-model adaptive schemes utilizing gradient and recursive least squares (RLS) methods are proposed for a faster system identification. To this end, the uncertain nonlinear dynamics of vehicle motion is addressed as a multiple-input multiple-output (MIMO) linear system with polytopic parameter uncertainties. These polytopic uncertainties denote uncertain variation in tire longitudinal and lateral force capacity due to nonlinear tire characteristics and road condition. In the proposed multiple-model approach, a set of fixed linear parametric identifi cation models are designed in advance, based on the known bounds of polytopic parameter set. The proposed adaptive schemes continuously generates a weighting vector for blending the identifi cation model to achieve the true model (operation condition) of the vehicle. Furthermore, the proposed adaptive schemes are generalized for MIMO systems with polytopic parameter uncertainties. The asymptotic stability of the proposed adaptive identifi cation schemes for linear MIMO systems is studied in detail. Later, the proposed blending based adaptive identi fication schemes are used to develop Linear Quadratic (LQ) based multiple-model adaptive control (MMAC) scheme for MIMO systems with polytopic parameter uncertainties. To this end, for each identi fication model, an optimal LQ controller is computed on-line for the corresponding model in advance, which saves computation power during operation. The generated control inputs from the set of LQ controllers is being blended on-line using weighting vector continuously updated by the proposed adaptive identifi cation schemes. The stability analysis of the proposed LQ based optimal MMAC scheme is provided. The developed LQ based optimal MMAC scheme has been applied to motion control of the vehicle. The simulation application to uncertain lateral single-track vehicle dynamics is presented in Simulink environment. The performances of the proposed LQ based MMAC utilizing RLS and gradient based methods have been compared to each other and an LQ controller which is designed using the same performance matrices and fixed nominal values of the uncertain parameters. The results validated the stability and effectiveness of the proposed LQ based MMAC algorithm and demonstrate that the proposed adaptive LQ control schemes outperform over the LQ control scheme for tracking tasks. In the next step, we addressed the constraints on actuation systems for a model predictive control (MPC) based MMAC design. To determine the constraints on torque vectoring at vehicle center of gravity (CG), we have used the min/max values of torque and torque rate at each corner, and the vehicle kinematic structure information. The MPC problem has been redefi ned as a constrained quadratic programming (QP) problem which is solved in real-time via interior-point algorithm by an embedded QP solver using MATLAB each time step. The solution of the designed MPC based MMAC provides total steering angle and desired torque vector at vehicle CG which is optimally distributed to each corner based on holistic corner control (HCC) principle. For validation of the designed MPC based MMAC scheme, several critical driving scenarios has been simulated using a high- fidelity vehicle simulation environment CarSim/Simulink. The performance of the proposed MPC based MMAC has been compared to an MPC controller which is designed for a wet road condition using the same tuning parameters in objective function design. The results validated the stability and effectiveness of the proposed MPC based MMAC algorithm and demonstrate that the proposed adaptive control scheme outperform over an MPC controller with fixed parameter values for tracking tasks

Aeronautical Engineering: A continuing bibliography with indexes (supplement 207)

This bibliography lists 484 reports, articles and other documents introduced into the NASA scientific and technical information system in November 1986