Adaptive feedforward compensation algorithms for active vibration control with mechanical coupling

Adaptive feedforward broadband vibration (or noise) compensation is currently used when a correlated measurement with the disturbance (an image of the disturbance) is available. However in most of the systems there is a ”positive” mechanical feedback coupling between the compensator system and the measurement of the image of the disturbance. This may lead to the instability of the system. The paper proposes new algorithms taking into account this coupling effect and provides the corresponding analysis. The algorithms have been applied to an active vibration control (AVC) system and real time results are presented. A theoretical and experimental comparison with some existing algorithms is also provided.     

An integrated approach to active model adaptation and on-line dynamic optimisation of batch processes

In the application of on-line, dynamic process optimisation, adaptive estimation of the system states and parameters is usually needed to minimise the unavoidable model-process mismatch. This work presents an integrated approach to optimal model adaptation and dynamic optimisation, with specific focus on batch processes. An active approach is proposed whereby the input variables are designed so as to maximise the information content of the data for optimal model adaptation. Then, this active adaptation method is combined with the objective of process performance to form a multi-objective optimisation problem. This integrative approach is in contrast to the traditional adaptation method, where only the process performance is considered and adaptation is passively carried out by using the data as is. Two strategies for solving the multi-objective problem are investigated: weighted average and constrained optimisation, and the latter is recommended for the ease in determining the balance between these two objectives. The proposed methodology is demonstrated on a simulated semi-batch fermentation process.     

Identification of a rice drying model with an improved sequential optimal design of experiments

Getting relevant parameter estimation of a non-linear model is often a hard task from both an experimental and numerical point of view. The objective of optimally designed experiments procedure is to diminish the experimental effort needed such that the identification is within acceptable confidence ranges. After each experiment, the next experiment is optimally designed, taking into account all past experimental results. It allows quality information to be extracted from the experimental data with less experimental time and resource consumption.In this paper, we present an original approach and implementation of the classical A-, D- and E-optimality on the estimation of 5 unknown (transfer related) coefficients in a compartmental model used to describe the convective drying of rice. The originality of our method is that it uses reparameterization of both parameter and protocol vectors which permits to avoid using a global optimization algorithm. The presented method is implemented in Matlab as a Toolbox and fully tested on a pilot plant. The case study (drying of rice) is typical in the field of process engineering: the dynamic model is strongly non-linear in its parameters and cannot be analytically solved. In addition, the specific technical constrains (inertias, limits, etc.) on the pilot are explicitly taken into account for improved experimental feasibility.In this drying application, three experiments with non-constant drying conditions are shown to be quite as effective as a two-factor three-level grid of nine experiments at constant conditions, with only one third of the experimental effort.     

A tuner that accelerates parameters

We propose a tuner, suitable for adaptive control and (in its discrete-time version) adaptive filtering applications, that sets the second derivative of the parameter estimates rather than the first derivative as is done in the overwhelming majority of the literature.     

Integrated direct/indirect adaptive robust contouring control of a biaxial gantry with accurate parameter estimations

This paper presents an integrated direct/indirect adaptive robust contouring controller (DIARC) for an industrial biaxial high-speed gantry that achieves not only excellent contouring performance but also accurate parameter estimations for secondary purposes such as machine health monitoring and prognosis. Contouring control problem is first formulated in a task coordinate frame. A physical model-based indirect-type parameter estimation algorithm is then developed to obtain accurate on-line estimates of unknown model parameters. A DIARC controller possessing dynamic-compensation-like fast adaptation is subsequently constructed to preserve the excellent transient and steady-state contouring performance of the direct adaptive robust controller (DARC) designs. The proposed DIARC along with previously developed DARC contouring controllers are implemented on a high-speed industrial biaxial gantry to test their achievable performance in practice. Comparative experimental results verify the improved contouring performance and the accurate physical parameter estimates of the proposed DIARC algorithm.     

Optimal experimental design for combinatorial problems

We discuss two experimental designs and show how to use them to evaluate difficult empirical combinatorial problems. We restrict our analysis here to the knapsack problem but comment more generally on the use of computational testing to analyze the performances of algorithms.Corresponding author. Part of this work was carried out while the author was visiting the IOE Department at University of Michigan and the CS department at Columbia University.     

Nonlinear control design for excitation controller and power system stabilizer

The paper focuses on the design of excitation controller and power system stabilizer (PSS) control schemes to enhance voltage regulation and transient stability. To design the excitation controller, the nonlinear models of the synchronous generator and electrical system are suitably arranged to give an input-output nonlinear model, which is subsequently made linear by a compensating law. The PSS control scheme is designed according to a new approach, based on the inverse system theory, which guarantees satisfactory swing damping by reducing the mutual influence between transient stability and voltage regulation. The results obtained by numerical simulations in different operating conditions confirm the effectiveness of the proposed design, also in the presence of model parameter inaccuracy.     

基于自适应观测器控制系统的快速故障调节

针对基于自适应观测器故障调节设计中的两个难点,即系统满足严格正实(SPR)条件与故障估计的准确性和快速性,首先引入适当的坐标变换,有效地放松了严格SPR条件,适用于一大类控制系统的故障诊断;其次,针对变换后的系统,提出一种快速故障估计的设计方法,明显改善了故障估计的性能;再次,基于故障估计值修正控制律以补偿故障所带来的影响,使故障调节后的系统稳定;最后通过仿真实验验证了该方法的有效性.     

Pessimistic cost-sensitive active learning of decision trees for profit maximizing targeting campaigns

In business applications such as direct marketing, decision-makers are required to choose the action which best maximizes a utility function. Cost-sensitive learning methods can help them achieve this goal. In this paper, we introduce Pessimistic Active Learning (PAL). PAL employs a novel pessimistic measure, which relies on confidence intervals and is used to balance the exploration/exploitation trade-off. In order to acquire an initial sample of labeled data, PAL applies orthogonal arrays of fractional factorial design. PAL was tested on ten datasets using a decision tree inducer. A comparison of these results to those of other methods indicates PAL’s superiority.     

Robot control and parameter estimation with only joint position measurements

Most adaptive control schemes for rigid robots assume velocities measurements to be available. Although it is possible to measure velocities by using tachometers, this increases costs and the signals delivered may be contaminated with noise. Since the use of encoders allows to read joint position pretty accurately, it is desirable to estimate joint velocities through an observer. This paper presents an adaptive scheme designed in conjunction with a linear observer. Boundedness of the estimated parameters and uniform ultimate boundedness for the tracking and observation errors are guaranteed. Experimental results are included to support the developed theory.     

Reinforcement learning-based shared control for walking-aid robot and its experimental verification

A walking-aid robot is an assistive device for enabling safe, stable and efficient locomotion in elderly or disabled individuals. In this paper, we propose a reinforcement learning-based shared control (RLSC) algorithm for intelligent walking-aid robot to address existing control problems in cooperative walking-aid robot system. Firstly, the intelligent walking-aid robot and the human walking intention estimation algorithm are introduced. Due to the limited physical and cognitive capabilities of elderly and disabled people, robot control input assistance is provided to maintain tactile comfort and a sense of stability. Then, considering the robot’s ability to autonomously adapt to different user operation habits and motor abilities, the RLSC algorithm is proposed. By dynamically adjusting user control weight according to different user control efficiencies and walking environments, the robot can improve the user’s degree of comfort when using the device and automatically adapting to user’s behaviour. Finally, the effectiveness of our algorithm is verified by experiments in a specified environment.     

Improving vehicle performance using adaptive control techniques

This paper reviews a collaborative research programme aimed at improving vehicle performance using adaptive control techniques. Initially the design of active suspension systems is considered, and the benefits of using a non-linear controller model with an adaptive control scheme are discussed. Adaptive schemes for active roll control are then considered, and the merits of incorporating a Smith predictor to accommodate for system delays are high-lighted. Preliminary research in adaptive cruise control and collision avoidance is discussed and plans for further developments are outlined. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, 19–21 January 1998     

Optimal input design for parameter estimation in a single and double tank system through direct control of parametric output sensitivities

In this paper the traditional and well-known problem of optimal input design for parameter estimation is considered. In particular, the focus is on input design for the estimation of the flow exponent present in Bernoulli's law. The theory will be applied to a water tank system with a controlled inflow and free outflow. The problem is formulated as follows: Given the model structure (f, g), which is assumed to be affine in the input, and the specific parameter of interest (θ), find a feedback law that maximizes the sensitivity of the model output to the parameter under different flow conditions in the water tank. The input design problem is solved analytically. The solution to this problem is used to estimate the parameter of interest with a minimal variance. Real-world experimental results are presented and compared with theoretical solutions.     

Stability and operating constraints of adaptive LMS-based feedback control

The filtered-X LMS algorithm has enjoyed widespread usage in both adaptive feedforward and feedback controller architectures. For feedforward controller designs the filtered-X LMS algorithm has been shown to exhibit unstable divergence for plant estimation errors in excess of ±90°. Typical implementations of this algorithm in adaptive feedback controllers such as filtered-U and filtered-E have previously been assumed to conform to these same identification constraints. Here we present two instability mechanisms that can arise in filtered-E control that violate the 90° error assumption: feedback loop instabilities and LMS algorithm divergence. Analysis of the adaptive feedback system indicates that the conventionally interpreted plant estimation error can be arbitrarily small yet induce algorithm divergence; while other cases may have very large estimation errors and feedback loops cause controller instability. These analytical observations are supported by simulations. The implications of the actual plant estimation error, calculated here for the filtered-E controller, are extended to practical constraints placed on applications including filtered-U, on-line system identification, and self-excited system control.     

New filters for internal model control design

In this paper we study the design of a new two-degree-of-freedom filter for the internal model control (IMC) method. The new filter alleviates some disadvantages of the standard IMC filter when the IMC method is applied to unstable plants that do not have non-minimum-phase zeros. We show that by employing the new filter, the resulting system has a flatter frequency response, better stability robustness, and little overshoot in the step response. Furthermore, one of its design parameters can be related directly to the closed-loop bandwidth and the other parameter can be used to control the recovery time after an overshoot has occurred in the step response. These features are important in the application of the IMC method to a new approach of adaptive robust control. Examples are given in the paper to illustrate the new filter design.     

Robust finite frequency range iterative learning control design and experimental verification

Iterative learning control is an application for two-dimensional control systems analysis where it is possible to simultaneously address error convergence and transient response specifications but there is a requirement to enforce frequency attenuation of the error between the output and reference over the complete spectrum. In common with other control algorithm design methods, this can be a very difficult specification to meet but often the control of physical/industrial systems is only required over a finite frequency range. This paper uses the generalized Kalman–Yakubovich–Popov lemma to develop a two-dimensional systems based iterative learning control law design algorithm where frequency attenuation is only imposed over a finite frequency range to be determined from knowledge of the application and its operation. An extension to robust control law design in the presence of norm-bounded uncertainty is also given and its applicability relative to alternative settings for design discussed. The resulting designs are experimentally tested on a gantry robot used for the same purpose with other iterative learning control algorithms.     

Sensor selection for model-based real-time optimization: relating design of experiments and design cost

Real-time optimization systems have become a common tool, in the continuous manufacturing industries, for improving process performance. Typically, these are on-line, steady-state, model-based optimization systems, whose effectiveness depends on a large number of design decisions. The work presented here addresses one of these design decisions and proposes a systematic approach to the selection of sensors to be used by the RTO system. This paper develops a sensor system selection metric based on a trade-off between two approaches to the design of experiments, which is shown to be consistent with the design cost approach of Forbes and Marlin [Computers Chem Eng 20 (1996) 7/7]. The resulting design metric is incorporated into a systematic procedure for RTO sensor selection problem. Finally, the proposed RTO sensor selection procedure is illustrated with a case study using the Williams–Otto [AIEE Trans 79 (1960), 458] plant.