Hans P. Geering

Control of an SCR catalytic converter system for a mobile heavy-duty application

Selective catalytic reduction (SCR) is well known for exhaust gas aftertreatment in power plant applications. In the near future, it will be introduced in Europe in mobile heavy-duty diesel applications. Stringent specifications and the dynamic operation mode of such applications demand advanced control strategies. Such a control strategy is presented here. It incorporates a model-based feedforward controller (FFC) which handles the known dynamics of the plant. It further contains a feedback controller (FBC) to compensate for disturbances and slowly varying parameters. The realization of the feedback control loop is possible only due to the use of a nitrogen oxide (NO/sub x/) sensor which is cross-sensitive against ammonia (NH/sub 3/), and the implementation of a procedure separating the detection of NO/sub x/ and NH/sub 3/.

Efficient Nonlinear Actuator Fault Detection and Isolation System for Unmanned Aerial Vehicles

In this paper, three main limitations of the classical implementation of the multiple-model adaptive-estimation method to isolate faults based on predefined fault hypotheses are highlighted. The first concerns the number of filters that must be designed to span the range of possible fault scenarios, which must be limited due to computational load. The second limitation appears when an actuator is locked at an arbitrary nonzero position that biases the residuals of the Kalman filters, leading to inaccurate fault detection and state estimation. Third, most of the implementations of a multiple-model adaptive-estimation method only work efficiently around predefined operating conditions. This paper presents a nonlinear actuator-fault detection and isolation system, which properly works over the entire operating envelope of an aircraft. Locked-in-place and floating actuator faults can be handled. The robustness of the fault detection and isolation system is enhanced by the usage of auxiliary excitation signals. The fault detection and isolation system is also capable of handling two simultaneous actuator failures with no increase of the computational load. The complete system was demonstrated in simulation with a nonlinear model of a model aircraft in moderate to severe wind conditions.

Control of a Urea SCR Catalytic Converter System for a Mobile Heavy Duty Diesel Engine

An advanced controller for a urea SCR (Selective Catalytic Reduction) catalytic converter system for a mobile heavy-duty diesel engine is presented. The after-treatment system is composed of the injecting device for urea solution and a single SCR catalytic converter. The control strategy consists of three parts: A primary feedforward controller, a surface coverage observer, and a feedback controller. A nitrogen oxide (NO x ) gas sensor with non-negligible cross-sensitivity to ammonia (NH 3 ) is used for a good feedback control performance. The control strategy is validated with ESC and ETC cycles: While the average NH 3 slip is kept below 10 ppm, the emission of NO x is reduced by 82%.

Observability analysis for target maneuver estimation via bearing-only and bearing-rate-only measurements

The implementation of modern guidance laws requires the design of tracking filters that provide information about the target maneuver. Because of the nonlinearity of the tracking problem, the stability of extended Kalman filters in tracking applications involving bearing-( rate-) only observations is highly dependent on the intercept geometry. This is due mainly to the lack of observability in the absence of range measurements. In this paper, the observability of target maneuvers via bearing-( rate-) only measurements is discussed. Intercept scenarios that result in the loss of observability are identified. They play an important role for both the initialization of tracking filters and the design of missile guidance laws based on bearing-( rate-) only measurements.

Model-reference adaptive A/F-ratio control in an SI engine based on Kalman-filtering techniques

In this article a model-reference adaptive air/fuel-ratio (A/F-ratio) control technique for SI engines is investigated. The technique is based on the principle of indirect model-reference adaptive control and involves explicit system parameter identification. Since the relevant system dynamics exhibit nonlinearities an extended Kalman-filter is used for parameter identification purposes. The filter is applied online and persistency of excitation is established by introducing supplementary perturbation signals. The resulting parameter estimates are used to update both the controller parameters and the parameters of a state observer providing the nonmeasurable amount of fuel trapped in the cylinders after each intake stroke. This quantity is used for feedback yielding increased control bandwidth. In a series of experimental tests the proposed controller proves to be a highly efficient tool to compensate for the wall-wetting dynamics.

Robust helicopter position control at hover

Position control for a radio controlled helicopter at hover is carried out using H/sub 2/ and H/sub /spl infin// methods. Various problems arising in real-world tests are stated and solved by different means: prefilters are used to achieve step responses without overshoot. For plants with more outputs than inputs; a special H/sub /spl infin// design scheme is used. The implicit inversion of the plant in the H/sub /spl infin// controller can cause problems. This inversion can be avoided by a new scheme for weighting the sensitivity.

Stochastic Model Predictive Control And Portfolio Optimization

This paper proposes a solution method for the discrete-time long-term dynamic portfolio optimization problem with state and asset allocation constraints. We use the ideas of Model Predictive Control (MPC) to solve the constrained stochastic control problem. MPC is a solution technique which was developed to solve constrained optimal control problems for deterministic control applications. MPC solves the optimal control problem with a receding horizon where a series of consecutive open-loop optimal control problems is solved.The aim of this paper is to develop an MPC approach to the problem of long-term portfolio optimization when the expected returns of the risky assets are modeled using a factor model based on stochastic Gaussian processes. We prove that MPC is a suboptimal control strategy for stochastic systems which uses the new information advantageously and thus is better than the pure optimal open-loop control. For the open-loop optimal control optimization, we derive the conditional portfolio distribution and the corresponding conditional portfolio mean and variance. The mean and the variance depend on future decision about the asset allocation.For the dynamic portfolio optimization problem, we consider constraints on the asset allocation as well as probabilistic constraints on the attainable values of the portfolio wealth. We discuss two different objectives, a classical mean–variance objective and the objective to maximize the probability of exceeding a predetermined value of the portfolio. The dynamic portfolio optimization problem is stated, and the solution via MPC is explained in detail. The results are then illustrated in a case study.

Extended Multiple Model Adaptive Estimation for the Detection of Sensor and Actuator Faults

A combination of the multiple model adaptive estimation method (MMAE) with an extended Kalman filter (EKF) is considered. The ability of the MMAE method to detect faults based on a predefined hypothesis and the parameter-estimating ability of an EKF results in a very efficient fault detection approach. This so-called extended multiple model adaptive estimation method (EMMAE) has been investigated on a nonlinear model of an aircraft. The results show that it is a very capable method to detect faults of various types.

Continuous-time optimal control theory for cost functionals including discrete state penalty terms

In this short paper, continuous-time optimal control problems are treated, where the cost functional includes additional discrete state penalty terms K_{j}(x(t_{j})) at specified times t_{j}(j = 1,..., N - 1) in the interior of the time interval [t_{0}, t_{N}] of the problem. Necessary conditions for a control to be optimal are given in the form of a minimum principle. The costate is necessarily discontinuous at the times t_{j}(j = 1,..., N - 1) . Consequently, the optimal control is discontinuous at t j , in general. A numerical example is given.

Test bench for rotorcraft hover control

This paper describes an indoor stand for a computer-controlled model helicopter. This stand was built to verify modern multivariable controller algorithms in real-world tests rather than in simulations. A commercial radio-controlled helicopter (not a scale model) is mounted on a mechanical structure allowing 6-degree of freedom flight conditions in a 2-m cube. The symmetrical geometry of the frame reduces its physical influence on the rotorcraft to be equivalent to a concentrated mass near the center of gravity. Including the dynamics of the driving motor and some unsteady aspects of the aerodynamics, an unstable 18th order mathematical model results. In addition, the radio controller causes a significant time delay. For this system, several control algorithms have been applied [channelwise proportional differential (PD) controller used for system identification, Linear Quadratic Gaussian/Loop Transfer Recovery (LQG/ LTR) approaches, modal controller]. The quality of the test results and some aspects of design problems are described and compared in the second part of the paper.