Estimation-based Control Research Articles

A torque estimator-based control strategy for oil-well drill-string torsional vibrations active damping including an auto-tuning algorithm

Considerable compliance of the lengthy oil-well drill string combined with low tool inertia and stick–slip friction between the tool and the rock bed causes notable undesired torsional vibrations of the drill-string electrical drive. In order to attenuate the torsional vibrations and, thus, improve both the quality and the productivity of the drilling process, an automatically tuned active damping control strategy based on drill-string torque estimation is proposed in the paper. The core of the strategy includes a proportional-integral (PI) controller extended with estimator-based drill-string torque feedback loop. Furthermore, an appropriate algorithm that prevents drill-string back-spinning caused by the limited braking power of the power converter is presented. Finally, an auto-tuning algorithm is proposed, which is built around an adaptive Kalman filter-based estimator of drill-string drive natural frequencies. The drill-string control strategy is verified experimentally using a drill string hardware-in-the-loop setup under the laboratory conditions, as well as on an actual oil-drilling rig.

Asymptotic statistical analysis for model-based control design strategies

In this paper, we generalize existing fundamental limitations on the accuracy of the estimation of dynamic models. In addition, we study the large sample statistical behavior of different estimation-based controller design strategies. In particular, fundamental limitations on the closed-loop performance using a controller obtained by Virtual Reference Feedback Tuning (VRFT) are studied. We also extend our results to more general estimation-based control design strategies. We present numerical examples to show the application of our results.

Control Strategies for an Automotive Suspension with a MR Damper

AbstractA semi-active control system of an automotive suspension with an Magneto-Rheological (MR) damper as a key element is considered. Given its hysteretical and nonlinear behavior, the inclusion of a MR damper model in a controller synthesis is presented. Two controllers are proposed from different approaches: LPV control and a Frequency Estimation-Based (FEB) control. The LPV controller uses a LPV model of a quarter of vehicle based on the MR damper dynamics. The FEB controller is a model-free controller. These controllers are compared under comfort oriented standards. Simulation results show these controllers as new alternatives with excellent response for comfort and road holding (improving the comfort between 10–20). %.

Sensorless Control of Electric Motors with Kalman Filters: Applications to Robotic and Industrial Systems

The paper studies sensorless control for DC and induction motors, using Kalman Filtering techniques. First the case of a DC motor is considered and Kalman Filter-based control is implemented. Next the nonlinear model of a field-oriented induction motor is examined and the motor's angular velocity is estimated by an Extended Kalman Filter which processes measurements of the rotor's angle. Sensorless control of the induction motor is again implemented through feedback of the estimated state vector. Additionally, a state estimation-based control loop is implemented using the Unscented Kalman Filter. Moreover, state estimation-based control is developed for the induction motor model using a nonlinear flatness-based controller and the state estimation that is provided by the Extended Kalman Filter. Unlike field oriented control, in the latter approach there is no assumption about decoupling between the rotor speed dynamics and the magnetic flux dynamics. The efficiency of the Kalman Filter-based control schemes, for both the DC and induction motor models, is evaluated through simulation experiments.

Feedback control of unstable steady states of flow past a flat plate using reduced-order estimators

We present an estimator-based control design procedure for flow control, using reduced-order models of the governing equations linearized about a possibly unstable steady state. The reduced-order models are obtained using an approximate balanced truncation method that retains the most controllable and observable modes of the system. The original method is valid only for stable linear systems, and in this paper, we present an extension to unstable linear systems. The dynamics on the unstable subspace are represented by projecting the original equations onto the global unstable eigenmodes, assumed to be small in number. A snapshot-based algorithm is developed, using approximate balanced truncation, for obtaining a reduced-order model of the dynamics on the stable subspace.The proposed algorithm is used to study feedback control of two-dimensional flow over a flat plate at a low Reynolds number and at large angles of attack, where the natural flow is vortex shedding, though there also exists an unstable steady state. For control design, we derive reduced-order models valid in the neighbourhood of this unstable steady state. The actuation is modelled as a localized body force near the trailing edge of the flat plate, and the sensors are two velocity measurements in the near wake of the plate. A reduced-order Kalman filter is developed based on these models and is shown to accurately reconstruct the flow field from the sensor measurements, and the resulting estimator-based control is shown to stabilize the unstable steady state. For small perturbations of the steady state, the model accurately predicts the response of the full simulation. Furthermore, the resulting controller is even able to suppress the stable periodic vortex shedding, where the nonlinear effects are strong, thus implying a large domain of attraction of the stabilized steady state.

Modeling and Control of Needles With Torsional Friction

A flexible needle can be accurately steered by robotically controlling the bevel tip orientation as the needle is inserted into tissue. Friction between the long, flexible needle shaft and the tissue can cause a significant discrepancy between the orientation of the needle tip and the orientation of the base where the needle angle is controlled. Our experiments show that several common phantom tissues used in needle steering experiments impart substantial friction forces to the needle shaft, resulting in a lag of more than 45 ( degrees ) for a 10 cm insertion depth in some phantoms; clinical studies report torques large enough to cause similar errors during needle insertions. Such angle discrepancies will result in poor performance or failure of path planners and image-guided controllers, since the needles used in percutaneous procedures are too small for state-of-the-art imaging to accurately measure the tip angle. To compensate for the angle discrepancy, we develop an estimator using a mechanics-based model of the rotational dynamics of a needle being inserted into tissue. Compared to controllers that assume a rigid needle in a frictionless environment, our estimator-based controller improves the tip angle convergence time by nearly 50% and reduces the path deviation of the needle by 70%.

Synthesis of state estimator-based control algorithms to apply on a distillation column

This work concerns the development of different discrete-time multivariable non-linear globally linearising control (GLC) algorithms for a binary distillation column (BDC). The proposed algorithms make the closed-loop system linear in an input/output (I/O) sense. The GLC control strategy is constructed with a transformer, also known as I/O linearising state feedback law, which accounts for process non-linearities; an estimator, which estimates the required unmeasured states and a quadratic dynamic matrix controller (QDMC) that is employed as an external controller around the globally linearised system. Three different GLC structures in conjunction with QDMC have been designed based on the availability of state information of the example distillation process. The proposed controllers have been compared in terms of set point tracking and disturbance rejection performance via numerical simulations. The study also includes the effects of parametric uncertainties on the controller responses.

Long-term estimation-based burst control algorithm in OBS networks

By taking advantage of statistical multiplexing gain in the burst level, optical burst switching (OBS) technology enables optical Internet to handle huge volume of data in an efficient manner without requiring optical buffers in the optical domain. However, when congestion builds up in the optical network core, large amount of data might be lost. In this article, we propose an efficient optical burst control algorithm that operates based on the awareness of future burst traffic condition to eliminate the effect of congestion reaction delay. The proposed algorithm takes advantage of multiple statistics to improve the estimation accuracy.Through performance evaluation, it is verified that the proposed algorithm proactively controls inbound burst traffic so that the OBS network can stay in a stable traffic condition while keeping the network throughput high.

State estimation-based control of a coal gasifier

On-line state estimation techniques provide a means of inferring real-time values for key process variables that cannot themselves be measured directly. Such state estimates can then form a basis for improved process control. Accordingly, Kalman filtering (KF) is applied to a non-linear coal gasifier system operated initially under a conventional feedback control strategy. Key unmeasured disturbances to gasifier operation arise in discharge pressure and coal feed ‘quality’. The fact that by treating both of these variables as unknown process parameters, they can be included in an augmented state and parameter vector and estimated reliably by KF is shown. A proportional feedforward control strategy, acting on these disturbance estimates and linked into the existing conventional feedback control system, is then shown to enhance system performance to both step and sinusoidal discharge pressure changes and to steps of up to 18% around normal coal feed quality. No significant performance difference relative to conventional feedback control is shown in ramping the downstream-generated power output. An important future outcome may be enhanced operation and control of upstream coal preparation plant based on the capability demonstrated here to obtain reliable real-time estimates of coal quality by KF.

Estimator-based control of reactive distillation system: Application of an extended Kalman filtering

This paper demonstrates that a state estimator can be successfully designed and implemented in a feedback control system of reactive distillation. The work of the state estimator is to provide the state compositions that are required to be used in the controller for necessary action. The control performance of a system that relies on the state estimator is examined and compared to a system that takes direct measurement from the process assuming the availability of a perfect online analyzer. It is found that the estimator-based system is robust against a moderate measurement errors and erroneous initial conditions. If the state estimator is designed from a highly erroneous process model, noisy measurements and approximate initial conditions, the use of estimator together with an online analyzer (for easily measured states) is recommended to achieve an effective control of a reactive distillation system.

Development and application of linear process model in estimation and control of reactive distillation

This paper presents a comprehensive formulation of a linearized state space process model for a generic two-reactant-two-product reactive distillation system. The development of the model requires the knowledge of the desired steady state design data, including liquid holdups and composition profiles. The application of the developed linear process model in composition estimation and control of the system is demonstrated. The effect of controller tuning on the performance of the estimator-based control system is explored. It is shown that effective controller tuning is necessary for a robust performance of a closed-loop reactive distillation system that relies on a state estimator. A robust linear state estimator can be developed and implemented in a feedback control system of a reactive distillation when the process can be approximated by a linear model.

Improving surface roughness in turning using optimal control of tool's radial position

Vibration suppression of the tool results in improved surface texture, dimensional accuracy and enhanced productivity in machining operations. Vibrations of the machine-tool structure, as well as the tool-workpiece interaction are the main contributors to the tool vibration. In light of this, abating the tool vibration can be approached by isolating the tool from the machine structure in the presence of a random process representing the tool-workpiece interaction. An active tool holder capable of isolating the cutting tool from the vibration of the machine-tool structure is constructed. Vibration isolation is accomplished by means of a Kalman estimator-based control strategy, a high bandwidth magnetostrictive actuator, and two accelerometers. The proposed control technique focuses on lowering the transmitted force to the tool that is subject to the machine structure vibration (base excitation) in presence of cutting process disturbance. The important aspect of this control strategy is that, while it is designed based on a full order model of the plant, its implementation is reduced to the realization of a second order estimator irrespective of the order of the plant model. Machining experiments showed that an average of 25% improvement in surface roughness of the workpiece has been achieved using the proposed technique.

AN ESTIMATOR-BASED SLIDING-MODE CONTROL FOR MANEUVERING A FLEXIBLE SPACECRAFT

An estimator-based sliding-mode controller (ESMC) is discussed for a linear stochastic system with a known disturbance and is utilized in a flexible spacecraft for the reduction of residual vibration while allowing natural deflection during operation. By converting the tracking problem into a regulator problem, the ESMC minimizes the expected value of the guadratic objective function composed of errors which always remain in the intersection of sliding hypersurfaces. For the numerical evaluation to take place in a flexible with a flexible spacecraft, a large slewing maneuver strategy is devised, with a tracking model for the nominal trajectory. A start-coast-stop strategy for an economical maneuver is employed in conjunction with the input shaping technique. The performance and efficacy of the proposed control scheme are illustrated with a comparison of different maneuvering strategies.

Run-by-run estimation-based control of meniscus coating

This paper describes the application of process control techniques to the meniscus coating process. Meniscus coating may be used to deposit polymers used in integrated circuits and electronics packages. Three control schemes are presented and each employs Kalman filtering to identify process coefficients. The three schemes differ according to the process model and amount of process information that is available. The dominating process parameters are applicator speed and material viscosity which affect the output of film thickness. The first scheme addresses the case where the least amount of information is known; viscosity is not measured. Identification and control is applied to a meniscus coater that does not have hardware to sense viscosity. In the second control scheme, viscosity sensors are used. However, limitations of the process model and the nature of the process produce controller lag in the presence of a shift in the process. This lag is undesirable in a manufacturing setting, therefore the third scheme is introduced which improves upon the second by reducing lag. The third scheme employs curves fitted to a history of process data to reinitialize the coefficient estimates when there is a shift in the process.

Direct Method of Constructing H 2-Suboptimal Controllers: Discrete-Time Systems

For discrete-time systems, an H 2-suboptimal control problem is defined and analyzed. Then, an algorithm called H 2-suboptimal state feedback gain sequence (Algorithm A1) is developed. Rather than utilizing a perturbation method, which is numerically stiff and computationally prohibitive, Algorithm A1 utilizes a direct eigenvalue assignment method to come up with a sequence of H 2-suboptimal state feedback gains. Also, although the sequence of H 2-suboptimal state feedback gains constructed by Algorithm A1 depends on a parameter ɛ, the construction procedure itself does not require explicitly the value of the parameter ɛ. Next, attention is focused on constructing a sequence of H 2-suboptimal estimator-based measurement feedback controllers. Three different estimator structures (prediction, current, and reduced-order estimators) are considered. For a given H 2-suboptimal state feedback gain, a sequence of estimator gains for any of the three estimators considered can be constructed by merely dualizing Algorithm A1. The direct method of constructing H 2-suboptimal controllers developed here has a number of advantages over the perturbation method, e.g., it has the ability to design all three types of estimator-based controllers while still maintaining throughout the design the computational simplicity of it. This paper is the discrete-time version of Ref. 1. There are some conceptual similarities as well as fundamental differences between the H 2-suboptimal control problems for continuous-time and discrete-time systems. The fundamental differences arise mainly from the fact that, in contrast to continuous-time systems, for discrete-time systems the infimum of the H 2-norm over the class of strictly proper controllers is in general different from the infimum of the H 2-norm over the class of proper controllers.

Automotive engine diagnosis and control via nonlinear estimation

The aim of this article is to explore a possible approach to the problem of designing control and diagnostic strategies for future generations of automotive engines. The work described focuses on the use of physical models to estimate unmeasured or unmeasurable variables and parameters, to be used for control and diagnostic purposes. We introduce the mean value engine model used, present a conceptual strategy for combined control and diagnosis focusing mainly on the problem of air fuel ratio control, review basic concepts related to estimation in nonlinear systems, and propose various forms of the estimators that serve different objectives. We illustrate estimation problems in the context of a simplified engine model, assuming both linear and nonlinear measurement of oxygen concentration in the exhaust. Some simulation and experimental results related to estimation-based control and diagnosis are shown.

Video rate control using a radial basis function estimator for constant bit-rate MPEG coders

Compressed video is a source of bursty traffic in communication networks whose data rate needs to be controlled within the available channel capacity, particularly, when it is transmitted via a fixed rate channel. Since the video rate is nonstationary and bursty at large-scene variations in a statistical sense, we propose a feed-forward, estimator-based rate control scheme associated with spatio-temporal activity features (STAF) for MPEG video encoders. This information is used to estimate the video rate of input picture frames. The estimated video rate enables the future buffer occupancy to be calculated and permits the encoder to adapt the quantisation step size to limit the increase or decrease in video rate due to dramatic scene variation. The current and future occupancies are used in a nonlinear quantiser control scheme to determine an appropriate quantisation step size depending on them. The novelty of this technique is that the nonlinear prediction and the nonlinear quantiser control are combined to achieve effective feed-forward video rate control, particularly, for realistic video containing various scene variations. In this paper, we highlight the innovative structure of the scheme and evaluate the performance of rate control algorithms with heuristic, linear and nonlinear rate estimators in the framework of the MPEG2 test model 5 video encoder. The performance measures are the occupancy of a two-frame delay buffer and peak signal-to-noise ratio (PSNR) for video quality.

On the observer-based structure of covariance controllers

The problem of finding the set of all stabilizing full-order controllers is equivalent to a problem of finding all matrices which can be assigned to the closed-loop system as a state covariance. The necessary and sufficient conditions for a given matrix to be assignable as a covariance are given, and all controllers (of plant order) which assign a specific covariance are parametrized explicitly. The structure of covariance controllers is shown for the first time to be observer-based (state estimator plus estimated-state feedback). The ‘central’ state estimator of the covariance controller is shown to be the Kalman filter. Unlike the traditional estimator-based controller, the separation principle does not hold, but one can design a controller by assigning the estimation error covariance and the plant state covariance simultaneously.

Optimal servodesign of the SISO estimator-based control system

An optimal servodesign for single-input—single-output systems by the frequency-domain approach is suggested. The servomechanism is estimator-based controlled with feedforward of the reference input, which provides an additional design freedom for the improvement (optimization) of the transient response. The cost function to be minimized is the integral-squared tracking error. The suggested design procedure requires the residue evaluations and n + 1 dimensional matrix operations for an nth-order plant. It theoretically provides a noniterative approach to the exact solution of the optimal design problem without any iterative approximation algorithm.

Evaluation paradigms for deductive databases: from systolic to as-you-please: Ulrich Guentzer, Werner Kiessling and Rudolf Bayer Institut fuer Informatik, Technische Universitaet Muenchen, Maerz 1986

In the first paper, a model predictive control (MPC) balancing strategy as the superior control is developed based on a universal equalization plant with balancing efficiency. Herein, two typical active equalization topologies, i.e., bidirectional adjacent, and bus-based, are considered to design the low-level controller, and the bidirectional flyback converter is selected as an individual cell equalizer (ICE). First, the extended Kalman filter (EKF) state-of-charge (SOC) estimation is presented to develop a unified ICE model. Then, an adaptive estimation-based control is proposed to track the optimal balancing reference current yielding from the superior control MPC formulation under the charge/discharge disturbance and parameters uncertainties. The current/voltage adaptive sliding-mode tracking control of the ICE is derived through the defined Lyapunov function, and the results are compared with a PI controller. Combined with the MPC balancing strategy, the EKF and adaptive-based controls are utilized to perform a balancing task for a 5-series battery cells module, which reduced the unbalanced SOC from 13% to 1%. To conclude, with the proposed superior control MPC strategy yielding optimal current targets, the low-level adaptive estimation-based controller for the ICE can effectively balance the inconsistent series-connected cells in typical active equalization systems by taking disturbance and uncertainty into consideration.