Proceedings of the American Control Conference

Neural network applications to aircraft automatic landing control based on linearized inverse aircraft model are presented. Conventional automatic landing systems can provide a smooth landing which is essential to the comfort of passengers. However, these systems work only within a specified operational safety envelope. When the conditions are beyond the envelope, such as turbulence or wind shear, they often cannot be used. The objective of this study is to investigate the use of neural networks with linearized inverse aircraft model in automatic landing systems and to make these systems more intelligent. Current flight control law is adopted in the intelligent controller design. Tracking performance and robustness are demonstrated through software simulations. This paper presents five different neural network controllers to improve the performance of conventional automatic landing systems based on the linearized inverse aircraft model. Simulation results show that the neural network controller can successfully expand the safety envelope to include more hostile environments such as severe turbulence.

This paper presents a new approach to trajectory optimization for autonomous fixed-wing aerial vehicles performing large-scale maneuvers. The main result is a planner which designs nearly minimum time planar trajectories to a goal, constrained by no-fly zones and the vehicle's maximum speed and turning rate. Mixed-Integer Linear Programming (MILP) is used for the optimization, and is well suited to trajectory optimization because it can incorporate logical constraints, such as no-fly zone avoidance, and continuous constraints, such as aircraft dynamics. MILP is applied over a receding planning horizon to reduce the computational effort of the planner and to incorporate feedback. In this approach, MILP is used to plan short trajectories that extend towards the goal, but do not necessarily reach it. The cost function accounts for decisions beyond the planning horizon by estimating the time to reach the goal from the plan's end point. This time is estimated by searching a graph representation of the environment. This approach is shown to avoid entrapment behind obstacles, to yield near-optimal performance when comparison with the minimum arrival time found using a fixed horizon controller is possible, and to work consistently on large trajectory optimization problems that are intractable for the fixed horizon controller.

Variable headway Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) based on a sliding mode method are considered in a unified model-based approach. An analysis of robust stability and performance for the closed-loop systems corresponding to different implementations has been carried out. Explicit relationships between distance headway and control design parameters and preceding vehicle speed and acceleration are provided. Teja Software is used for simulation and real-time C/C++ code generation. In simulation, a full vehicle dynamic model is used. It also simulates different maneuvers and several possible approximations in the implementation.

In an internal combustion engine, whenever complete combustion can be achieved, indicated torque production is directly related to fuel rate. This premise is exploited to extrapolate a public-domain, homogeneous charge, spark ignition engine model to the stratified charge regime. The extrapolation is based on published, functional forms for indicated torque and friction torque in a direct-injection, spark ignition engine. The extrapolated, control-oriented model is "validated" through comparisons to both published results and expected behaviors. The dynamic model is illustrated through step responses in fuel and throttle flow rate.

The problem of establishing less conservative performance bounds relative to a classical technique is investigated for a class of semi-active control problems with nonlinear actuator dynamics and discontinuous feedback control. The performance problem is formulated as a linear matrix inequality problem and it is shown by way of a 3D example that this approach provides several orders of magnitude performance bound improvement over that obtainable by a classical technique. However, the LMI bound is still one order of magnitude larger than that provided by simulation. It is interesting to note that the LMI bound is the same order of magnitude as that provided by the L/sub 1/-system norm of the linear system that results from the case where the valve on the semi-active element remains open on the whole space (equivalent to a passive damper). Even with the large improvement over the classical technique, the LMI bound did not differentiate between various competing Lyapunov type controllers for the 3D example. The LMI method is only used here to establish an upper bound on the system performance. The control design here is based on the steepest descent Lyapunov formulation, not on the LMI treatment.

Investigates the possibility of computing position estimates for passenger cars based only on signals available in a production car. Two new dead reckoning approaches are compared to the well known rear axle based algorithm. One method combines the wheel revolution data calculated on the basis of raw ABS-signal measurements with the steering angle measurement. The other model based algorithm computes the position estimates using velocity and also steering angle measurement. For this approach the well known tricycle model has been extended by an object-oriented front axle model. Both newly proposed methods perform superior to the simple rear axle based algorithm and provide position data precise enough for control purposes. This was shown by experiments using a real car in connection with a real-time signal processing environment.

A state estimator design is described for discrete time systems having observably intermittent measurements. A stationary Markov process is used to model probabilistic measurement losses. The stationarity of the Markov process suggests an analogous stationary estimator design related to the Markov states. A precomputable time-varying state estimator is proposed as an alternative to Kalman's optimal time-varying estimation scheme applied to a discrete linear system with Markovian intermittent measurements. An iterative scheme to find optimal precomputed estimators is given. The results here naturally extend to Markovian jump linear systems.

The paper presents some results on global exponential stability of linear time invariant systems with different time scales. Once fast and slow subsystems are derived, and controllers synthesized using Reaching Law Approach, global closed loop stability is derived for the whole system using Lyapunov methods.

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Most microelectromechanical systems are based on electromagnetic or electrostatic actuation forces. It is well known that linear controllers based on linearized models have a stable actuation range of one third of the nominal gap, while at larger displacements the electrostatic force dominates resulting in the electrodes pulling together. For Hammerstein systems with quadratic input nonlinearity we propose a nonlinear controller that guarantees stability and bounded disturbance rejection. For a single-sided electromagnetic oscillator we use this controller to achieve tracking. For a double-sided electromagnetic oscillator, we propose a control algorithm that achieves the desired performance while guaranteeing that the electromagnetic plates never pull together. These nonlinear controllers are robust since their stabilization and disturbance rejection properties do not require knowledge of the inertia, damping and stiffness of the plant.