Yildiray Yildiz

Adaptive air fuel ratio control for internal combustion engines

This paper treats the control of engine air-to-fuel ratio from the perspective of adaptive control of time-delay systems. High accuracy of engine air-to-fuel ratio control is required to meet stringent emissions regulations. Two adaptive controller designs are considered. The first design is based on feed-forward adaptation while the second design is based on both feedback and feedforward adaptation incorporating the developed adaptive posicast controller. The two adaptive designs are compared with the baseline controller using simulations and vehicle experiments.

Sliding-Mode Neuro-Controller for Uncertain Systems

In this paper, a method that allows for the merger of the good features of sliding-mode control and neural network (NN) design is presented. Design is performed by applying an NN to minimize the cost function that is selected to depend on the distance from the sliding-mode manifold, thus providing that the NN controller enforces sliding-mode motion in a closed-loop system. It has been proven that the selected cost function has no local minima in controller parameter space, so under certain conditions, selection of the NN weights guarantees that the global minimum is reached, and then the sliding-mode conditions are satisfied; thus, closed-loop motion is robust against parameter changes and disturbances. For controller design, the system states and the nominal value of the control input matrix are used. The design for both multiple-input-multiple-output and single-input-single-output systems is discussed. Due to the structure of the (M)ADALINE network used in control calculation, the proposed algorithm can also be interpreted as a sliding-mode-based control parameter adaptation scheme. The controller performance is verified by experimental results

Adaptive posicast controller for time-delay systems with relative degree n * ≤2

In this paper, we present an Adaptive Posicast Controller that deals with parametric uncertainties in linear systems with delays. It is assumed that the plant has no right half plane zeros and the delay is known. The adaptive controller is based on the Smith Predictor and Finite Spectrum Assignment with time-varying parameters adjusted online. A novel Lyapunov-Krasovskii functional is used to show semi-global stability of the closed-loop error equations. The controller is applied to engine fuel-to-air ratio control. The implementation results show that the Adaptive Posicast Controller significantly improves the closed-loop performance when compared to the case with the existing baseline controller.

Spark-Ignition-Engine Idle Speed Control: An Adaptive Control Approach

The paper presents an application of a recently developed adaptive posicast controller (APC) for time-delay systems to the idle speed control (ISC) problem in spark ignition (SI) internal combustion (IC) engines. The objective is to regulate the engine speed to a prescribed set-point in the presence of accessory load torque disturbances such as those due to air conditioning and power steering. The adaptive controller, integrated with the existing proportional spark controller, is used to drive the electronic throttle actuator. We present both simulation and experimental results demonstrating the performance improvement by employing the adaptive controller. We also present the modifications and improvements to the controller structure which were developed during the course of experimentation to solve specific problems. In addition, the potential for the reduction in calibration time and effort which can be achieved with our approach is discussed.

Adaptive Idle Speed Control for Internal Combustion Engines

The paper presents an application of a recently developed adaptive posicast controller for time-delay systems to the idle speed control (ISC) problem in internal combustion (IC) engines. The objective is to regulate the engine speed at a prescribed set-point in the presence of accessory load torque disturbances such as air conditioning and power steering. The adaptive controller, integrated with the existing proportional spark controller, is used to drive the electronic throttle as an actuator. We present both simulation and experimental results which demonstrate the potential of the adaptive controller to improve the performance. In addition, the reduction in calibration time and effort which can be achieved with our approach is discussed.

Cyber-Physical Security: A Game Theory Model of Humans Interacting Over Control Systems

Recent years have seen increased interest in the design and deployment of smart grid devices and control algorithms. Each of these smart communicating devices represents a potential access point for an intruder spurring research into intruder prevention and detection. However, no security measures are complete, and intruding attackers will compromise smart grid devices leading to the attacker and the system operator interacting via the grid and its control systems. The outcome of these machine-mediated human-human interactions will depend on the design of the physical and control systems mediating the interactions. If these outcomes can be predicted via simulation, they can be used as a tool for designing attack-resilient grids and control systems. However, accurate predictions require good models of not just the physical and control systems, but also of the human decision making. In this manuscript, we present an approach to develop such tools, i.e., models of the decisions of the cyber-physical intruder who is attacking the systems and the system operator who is defending it, and demonstrate its usefulness for design.

A control allocation technique to recover from pilot-induced oscillations (capio) due to actuator rate limiting

This paper proposes a control allocation technique that can help pilots recover from pilot induced oscillations (PIO). When actuators are rate-saturated due to aggressive pilot commands, high gain flight control systems or some anomaly in the system, the effective delay in the control loop may increase depending on the nature of the cause. This effective delay increase manifests itself as a phase shift between the commanded and actual system signals and can instigate PIOs. The proposed control allocator reduces the effective time delay by minimizing the phase shift between the commanded and the actual attitude accelerations. Simulation results are reported, which demonstrate phase shift minimization and recovery from PIOs. Conversion of the objective function to be minimized and constraints to a form that is suitable for implementation is given.

Stability properties and cross coupling performance of the control allocation scheme CAPIO

This paper presents a stability analysis and an application of a recently developed algorithm to recover from Pilot Induced Oscillations (CAPIO). When actuators are ratesaturated due to either an aggressive pilot command, high gain of the ight control system or some anomaly in the system, the eective delay in the control loop may increase. This eective delay increase manifests itself as a phase shift between the commanded and actual system signals and can instigate Pilot Induced Oscillations (PIO). CAPIO reduces the eective time delay by minimizing the phase shift between the commanded and the actual attitude accelerations. To establish theoretical results pertinent to CAPIO stability analysis, a scalar case is presented. In addition, we present simulation results for aircraft with cross-coupling which demonstrates the potential of CAPIO serving as an eective PIO handler in adverse conditions.

Neuro sliding mode control of timing belt servo-system

In this paper, the employment of neural networks with sliding mode control in the control of a linear drive with flexible transmission element is described. Linear drives with flexible transmission elements are cheaper and also more efficient than the ones with rigid transmissions like power screw systems. Hence, these devices play an important role in the industry. A neuro-sliding mode controller cascaded with a discrete sliding mode controller is used to control the system. Neuro-sliding mode controller is used in the outer loop and produces reference for the discrete sliding mode controller which serves as a force controller, in the inner loop. The control signal of the neuro-controller is obtained by minimizing an error function which is derived from Lyapunov stability analysis. The controller performance is tested with different loading conditions and different friction torques and the results are presented.

Predicting Pilot Behavior in Medium-Scale Scenarios Using Game Theory and Reinforcement Learning

Effective automation is critical in achieving the capacity and safety goals of the Next Generation Air Traffic System. Unfortunately creating integration and validation tools for such automation is difficult as the interactions between automation and their human counterparts is complex and unpredictable. This validation becomes even more difficult as we integrate wide-reaching technologies that affect the behavior of different decision makers in the system such as pilots, controllers and airlines. While overt short-term behavior changes can be explicitly modeled with traditional agent modeling systems, subtle behavior changes caused by the integration of new technologies may snowball into larger problems and be very hard to detect. To overcome these obstacles, we show how integration of new technologies can be validated by learning behavior models based on goals. In this framework, human participants are not modeled explicitly. Instead, their goals are modeled and through reinforcement learning their actions are predicted. The main advantage to this approach is that modeling is done within the context of the entire system allowing for accurate modeling of all participants as they interact as a whole. In addition such an approach allows for efficient trade studies and feasibility testing on a wide range of automation scenarios. The goal of this paper is to test that such an approach is feasible. To do this we implement this approach using a simple discrete-state learning system on a scenario where 50 aircraft need to self-navigate using Automatic Dependent Surveillance-Broadcast (ADS-B) information. In this scenario, we show how the approach can be used to predict the ability of pilots to adequately balance aircraft separation and fly efficient paths. We present results with several levels of complexity and airspace congestion.