DECENTRALIZED ROBUST NONLINEAR MODEL PREDICTIVE CONTROLLER FOR UNMANNED AERIAL SYSTEMS

The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1 A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2 A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3 An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4 A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible

Distributed interpolatory algorithms for set membership estimation

This work addresses the distributed estimation problem in a set membership framework. The agents of a network collect measurements which are affected by bounded errors, thus implying that the unknown parameters to be estimated belong to a suitable feasible set. Two distributed algorithms are considered, based on projections of the estimate of each agent onto its local feasible set. The main contribution of the paper is to show that such algorithms are asymptotic interpolatory estimators, i.e. they converge to an element of the global feasible set, under the assumption that the feasible set associated to each measurement is convex. The proposed techniques are demonstrated on a distributed linear regression estimation problem

Corner-based estimation of tire forces and vehicle velocities robust to road conditions

The final publication is available at Elsevier via https://doi.org/10.1016/j.conengprac.2017.01.009 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Recent developments in vehicle stability control and active safety systems have led to an interest in reliable vehicle state estimation on various road conditions. This paper presents a novel method for tire force and velocity estimation at each corner to monitor tire capacities individually. This is entailed for more demanding advanced vehicle stability systems and especially in full autonomous driving in harsh maneuvers. By integrating the lumped LuGre tire model and the vehicle kinematics, it is shown that the proposed corner-based estimator does not require knowledge of the road friction and is robust to model uncertainties. The stability of the time-varying longitudinal and lateral velocity estimators is explored. The proposed method is experimentally validated in several maneuvers on different road surface frictions. The experimental results confirm the accuracy and robustness of the state estimators.Automotive Partnership Canada, Ontario Research Fund, General Motors Co

Full Vehicle State Estimation Using a Holistic Corner-based Approach

Vehicles' active safety systems use different sensors, vehicle states, and actuators, along with an advanced control algorithm, to assist drivers and to maintain the dynamics of a vehicle within a desired safe range in case of instability in vehicle motion. Therefore, recent developments in such vehicle stability control and autonomous driving systems have led to substantial interest in reliable road angle and vehicle states (tire forces and vehicle velocities) estimation. Advances in applications of sensor technologies, sensor fusion, and cooperative estimation in intelligent transportation systems facilitate reliable and robust estimation of vehicle states and road angles. In this direction, developing a flexible and reliable estimation structure at a reasonable cost to operate the available sensor data for the proper functioning of active safety systems in current vehicles is a preeminent objective of the car manufacturers in dealing with the technological changes in the automotive industry. This thesis presents a novel generic integrated tire force and velocity estimation system at each corner to monitor tire capacities and slip condition individually and to address road uncertainty issues in the current model-based vehicle state estimators. Tire force estimators are developed using computationally efficient nonlinear and Kalman-based observers and common measurements in production vehicles. The stability and performance of the time-varying estimators are explored and it is shown that the developed integrated structure is robust to model uncertainties including tire properties, inflation pressure, and effective rolling radius, does not need tire parameters and road friction information, and can transfer from one car to another. The main challenges for velocity estimation are the lack of knowledge of road friction in the model-based methods and accumulated error in kinematic-based approaches. To tackle these issues, the lumped LuGre tire model is integrated with the vehicle kinematics in this research. It is shown that the proposed generic corner-based estimator reduces the number of required tire parameters significantly and does not require knowledge of the road friction. The stability and performance of the time-varying velocity estimators are studied and the sensitivity of the observers' stability to the model parameter changes is discussed. The proposed velocity estimators are validated in simulations and road experiments with two vehicles in several maneuvers with various driveline configurations on roads with different friction conditions. The simulation and experimental results substantiate the accuracy and robustness of the state estimators for even harsh maneuvers on surfaces with varying friction. A corner-based lateral state estimation is also developed for conventional cars application independent of the wheel torques. This approach utilizes variable weighted axles' estimates and high slip detection modules to deal with uncertainties associated with longitudinal forces in large steering. Therefore, the output of the lateral estimator is not altered by the longitudinal force effect and its performance is not compromised. A method for road classification is also investigated utilizing the vehicle lateral response in diverse maneuvers. Moreover, the designed estimation structure is shown to work with various driveline configurations such as front, rear, or all-wheel drive and can be easily reconfigured to operate with different vehicles and control systems' actuator configurations such as differential braking, torque vectoring, or their combinations on the front or rear axles. This research has resulted in two US pending patents on vehicle speed estimation and sensor fault diagnosis and successful transfer of these patents to industry

VLSI Design of Trusted Virtual Sensors

This work presents a Very Large Scale Integration (VLSI) design of trusted virtual sensors providing a minimum unitary cost and very good figures of size, speed and power consumption. The sensed variable is estimated by a virtual sensor based on a configurable and programmable PieceWise-Affine hyper-Rectangular (PWAR) model. An algorithm is presented to find the best values of the programmable parameters given a set of (empirical or simulated) input-output data. The VLSI design of the trusted virtual sensor uses the fast authenticated encryption algorithm, AEGIS, to ensure the integrity of the provided virtual measurement and to encrypt it, and a Physical Unclonable Function (PUF) based on a Static Random Access Memory (SRAM) to ensure the integrity of the sensor itself. Implementation results of a prototype designed in a 90-nm Complementary Metal Oxide Semiconductor (CMOS) technology show that the active silicon area of the trusted virtual sensor is 0.86 mm 2 and its power consumption when trusted sensing at 50 MHz is 7.12 mW. The maximum operation frequency is 85 MHz, which allows response times lower than 0.25 μ s. As application example, the designed prototype was programmed to estimate the yaw rate in a vehicle, obtaining root mean square errors lower than 1.1%. Experimental results of the employed PUF show the robustness of the trusted sensing against aging and variations of the operation conditions, namely, temperature and power supply voltage (final value as well as ramp-up time)Ministerio de Economía, Industria y Competitividad TEC2014-57971-RConsejo Superior de Investigaciones Científicas 201750E01

L1 adaptive control flight testing and extension to nonlinear reference systems with unmatched uncertainty

Building upon prior research efforts deploying L1 adaptive control in remotely piloted aerospace applications, this dissertation presents the progression of in-flight evaluation of L1 adaptive control to manned flight testing on Calspan’s variable stability Learjet and to an augmentation of an autonomous trajectory planner on a multirotor aircraft. These efforts ultimately led to the development of a new L1 adaptive controller for a class of control-affine nonlinear reference systems subject to time-varying, state-dependent matched and unmatched uncertainties. The L1 adaptive controller for the Learjet flight tests was designed as stability augmentation system, modifying the pilot's stick-to-surface commands, and was evaluated in a series of flying and handling qualities tests. The results of the Learjet flight tests demonstrated the ability of the L1 adaptive controller to recover desired flying qualities and safe, consistent handling qualities in the presence of off-nominal dynamics, some of which had severe flying qualities deficiencies and aggressive tendencies toward adverse pilot-aircraft interaction, and simulated aircraft failures. A modification of the Learjet control law was implemented, with a nonlinear reference system and estimation of both matched and unmatched uncertainties, for a multirotor aircraft as an augmentation of a geometric trajectory-tracking baseline controller, tracking a reference trajectory generated by a model predictive path integral trajectory planner. Simulation results demonstrated that, with the L1 augmentation, the vehicle was able to navigate a complex environment in the presence of uncertainty and external disturbances. The new L1 adaptive controller provides a theoretical foundation for the L1 augmentation in the multirotor application, and may be applicable to tilt-rotor, tilt-wing, and split-propulsion vertical takeoff and landing aircraft proliferating in the urban air mobility sector. The theory is based on incremental stability for robust trajectory tracking and uses a piecewise-constant adaptive law. It proposes a feedforward compensator (in the form of an embedded linear parameter-varying system), synthesized for the variational dynamics of the system using linear matrix inequality-based robust control methods to minimize the peak-to-peak gain from unmatched uncertainty to the system state. A realization of the feedforward compensator in the ambient space can be directly applied to the nonlinear system. Analysis of the closed-loop system provides an incremental stability guarantee and bounds the transient and steady-state trajectory-tracking error

Research on Stability Control Based on the Wheel Speed Difference for the AT Vehicles

This paper utilizes a linear two-degree-of-freedom vehicle model to calculate the nominal value of the vehicle’s nondrive-wheel speed difference and investigates methods of estimating the yaw acceleration and sideslip angular speed. A vehicular dynamic stability control system utilizing this nondrive-wheel speed difference is then developed, which can effectively improve a vehicle’s dynamic stability at a very low cost. Vehicle cornering processes on roads of different frictions and with different vehicle speeds are explored via simulation, with speed control being applied when vehicle speed is high enough to make the vehicle unstable. Driving simulator tests of vehicle cornering capacity on roads of different friction coefficients are also conducted