Robert Babuska | Delft University of Technology (original) (raw)

Papers by Robert Babuska

Fuzzy Sets and Systems, 2010

A large class of nonlinear systems can be well approximated by Takagi-Sugeno (TS) fuzzy models, w... more A large class of nonlinear systems can be well approximated by Takagi-Sugeno (TS) fuzzy models, with linear or affine consequents. However, in practical applications, the process under consideration may be affected by unknown inputs, such as disturbances, faults or unmodeled dynamics. In this paper, we consider the problem of simultaneously estimating the state and unknown inputs in TS systems. The inputs considered in this paper are 1) polynomials in time (such as a bias in the model or an unknown ramp input acting on the model) and 2) unmodeled dynamics. The proposed observer is designed based on the known part of the fuzzy model. Conditions on the asymptotic convergence of the observer are presented and the design guarantees an ultimate bound on the error signal. The results are illustrated on a simulation example.

Artificial Intelligence in Medicine, 2001

The results of monitoring respiratory parameters estimated from¯ow±pressure±volume measurements c... more The results of monitoring respiratory parameters estimated from¯ow±pressure±volume measurements can be used to assess patients' pulmonary condition, to detect poor patient± ventilator interaction and consequently to optimize the ventilator settings. A new method is proposed to obtain detailed information about respiratory parameters without interfering with the expiration. By means of fuzzy clustering, the available data set is partitioned into fuzzy subsets that can be well approximated by linear regression models locally. Parameters of these models are then estimated by least-squares techniques. By analyzing the dependence of these local parameters on the location of the model in the¯ow±volume±pressure space, information on patients' pulmonary condition can be gained. The effectiveness of the proposed approaches is demonstrated by analyzing the dependence of the expiratory time constant on the volume in patients with chronic obstructive pulmonary disease (COPD) and patients without COPD. #

IEEE Transactions on Fuzzy Systems, 2004

A simple and effective method for the selection of significant inputs in nonlinear regression mod... more A simple and effective method for the selection of significant inputs in nonlinear regression models is proposed. Given a set of input-output data and an initial superset of potential inputs, the relevant inputs are selected by checking whether after deleting a particular input, the data set is still consistent with the basic property of a function. In order to be able to handle real-valued and noisy data in a sensible manner, fuzzy clustering is first applied. The obtained clusters are compared by using a similarity measure in order to find inconsistencies within the data. Several examples using simulated and real-world data sets are presented to demonstrate the effectiveness of the algorithm.

Abstract: Shock waves are special types of relatively short traffic jams that propagate opposite ... more Abstract: Shock waves are special types of relatively short traffic jams that propagate opposite to the driving direction. These jams increase travel time, air pollution, and negatively impact safety. One way of dealing with shock waves is to impose dynamic speed limits to ...

Control Engineering Practice, 2006

The use of a linear design technique in combination with gain scheduling is the most common syste... more The use of a linear design technique in combination with gain scheduling is the most common systematic approach to the design of nonlinear flight control laws. However, the selection of the operating points and the design of the interpolation scheme remains a time-consuming procedure. In order to reduce the design effort, an automated procedure has been developed and applied to the design of a longitudinal control law in a fly-by-wire flight control system. The number of operating points and their locations are determined automatically by using fuzzy clustering to capture characteristic patterns in the aerodynamic model throughout the flight envelope. This approach also directly provides the interpolation mechanism (membership functions) for the local flight control law parameters. The design procedure has been developed in close cooperation with airframe and control system manufacturers and was evaluated through pilot-in-the-loop flight simulator tests. Experienced test pilots could not detect any significant difference between the conventional flight control laws and those implemented with fuzzy gain scheduling, even though the latter used fewer operating points and was designed in an automated manner, as opposed to the conventional iterative manual procedure.

IEEE Transactions on Fuzzy Systems, 1998

The attitude control of a satellite is often characterized by a limit cycle, caused by measuremen... more The attitude control of a satellite is often characterized by a limit cycle, caused by measurement inaccuracies and noise in the sensor output. In order to reduce the limit cycle, a nonlinear fuzzy controller was applied. The controller was tuned by means of reinforcement learning without using any model of the sensors or the satellite. The reinforcement signal is computed as a fuzzy performance measure using a noncompensatory aggregation of two control subgoals. Convergence of the reinforcement learning scheme is improved by computing the temporal difference error over several time steps and adapting the critic and the controller at a lower sampling rate. The results show that an adaptive fuzzy controller can better cope with the sensor noise and nonlinearities than a standard linear controller.

Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering poin... more Fuzzy Modeling for Control addresses fuzzy modeling from the systems and control engineering point of view. It focuses on the selection of appropriate model structures, on the acquisition of dynamic fuzzy models from process measurements (fuzzy identification), and on the ...

Water Research, 2008

Fluidised bed reactors are used for water softening in water treatment plants. Recent research sh... more Fluidised bed reactors are used for water softening in water treatment plants. Recent research shows that under current operation of reactors the crystallisation of calcium carbonate can be hampered. Until now the operational constraints on the fluidised bed have not been exactly known. Experiments were carried out to investigate the fluidisation behaviour of calcium carbonate pellets in water. The results of the fluidisation experiments are compared to two commonly used modelling approaches of Ergun and Richardon–Zaki. Using the experimental data the models are calibrated. The calibrated Richardson–Zaki model is used to determine operational constraints on pellet size at the bottom of the reactor and water flow through the reactor. The model-based constraints are compared to operational data of the Weesperkarspel full-scale treatment plant of Waternet (The Netherlands). It can be concluded that the current operation of the treatment plant violates the calculated constraints with consequences for effluent quality and corrective maintenance. By using models for determining the operation of the fluidised bed, the softening process can thus be improved.

A semi-mechanistic fuzzy modeling technique is proposed to obtain compact and transparent process... more A semi-mechanistic fuzzy modeling technique is proposed to obtain compact and transparent process models based on small data-sets. Semi-mechanistic models are hybrid models that consist of a white box structure based on mechanistic relationships and black-box substructures to model less defined parts. First, it is shown that certain type of white-box models can be efficiently incorporated into a Takagi-Sugeno fuzzy rule structure. Next, the proposed models are identified from learning data and special attention is paid to transparency and accuracy aspects. The approach is based on a combination of (i) prior knowledge-based model structures, (ii) fuzzy clustering, (iii) orthogonal least-squares, and (iv) the modified Fisher's interclass separability method. For the identification of the semimechanistic fuzzy model, a new fuzzy clustering method is proposed, i.e., clustering is achieved by the simultaneous identification of fuzzy sets defined on some of the scheduling variables and identification of the parameters of the local semimechanistic submodels. Subsequently, model reduction is applied to make the TS models as compact as possible, i.e., the most relevant consequent variables are selected by an orthogonal least squares method, and the modified Fisher's interclass separability criteria is used for selection of relevant antecedent (scheduling) variables. The overall procedure is demonstrated by the development of a semimechanistic model for a biochemical process. Although the results do not carry over directly to other engineering fields, the main ideas and conclusions, will certainly hold for other application areas as well.

In realistic multiagent systems, learning on the basis of complete state information is not feasi... more In realistic multiagent systems, learning on the basis of complete state information is not feasible. We introduce adaptive state focus Q-learning, a class of methods derived from Q-learning that start learning with only the state information that is strictly necessary for a single agent to perform the task, and that monitor the convergence of learning. If lack of convergence is detected, the learner dynamically expands its state space to incorporate more state information (e.g., states of other agents). Learning is faster and takes less resources than if the complete state were considered from the start, while being able to handle situations where agents interfere in pursuing their goals. We illustrate our approach by instantiating a simple version of such a method, and by showing that it outperforms learning with full state information without being hindered by the deficiencies of learning on the basis of a single agent&amp;#x27;s state.

Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for combina... more Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for combinatorial optimization problems. This paper introduces a new type of ACO algorithm that will be used for routing along multiple routes in a network as opposed to optimizing a single route. Contrary to traditional routing algorithms, the Ant Dispersion Routing (ADR) algorithm has the objective of determining recommended routes for every driver in the network, in order to increase network efficiency. We present the framework for the new ADR algorithm, as well as the design of a new cost function that translates the motivations and objectives of the algorithm. The proposed approach is illustrated with a small simulation-based case study for the Singapore Expressway Network.

Multi-agent systems are rapidly finding applications in a variety of domains, including robotics,... more Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, etc. Learning approaches to multi-agent control, many of them based on reinforcement learning (RL), are investigated in complex domains such as teams of mobile robots. However, the application of decentralized RL to low-level control tasks is not as intensively studied. In this paper, we investigate centralized and decentralized RL, emphasizing the challenges and potential advantages of the latter. These are then illustrated on an example: learning to control a two-link rigid manipulator. Some open issues and future research directions in decentralized RL are outlined.

. Reinforcement learning (RL) is a model-free tuning and adaptationmethod for control of dynamic ... more . Reinforcement learning (RL) is a model-free tuning and adaptationmethod for control of dynamic systems. Contrary to supervised learning, based usuallyon gradient descent techniques, RL does not require any model or sensitivity functionof the process. Hence, RL can be applied to systems that are poorly understood,uncertain, nonlinear or for other reasons untractable with conventional methods. Inreinforcement learning, the overall controller performance is evaluated by a scalarmeasure,...

urman, "A fuzzy decision support system for traffic control centers,"

A method is proposed to detect and identify two common classes of actuator faults in nonlinear sy... more A method is proposed to detect and identify two common classes of actuator faults in nonlinear systems. The two fault classes are total and partial actuator faults. This is accomplished by representing the nonlinear system by a Linear Parameter Varying (LPV) model, which is derived from experimental input-output data. The LPV model is used in a Kalman filter to estimate augmented states, which are directly related to the faults. Decision logic has been developed to determine the fault class from the estimated augmented states. The proposed method has been validated on a nonlinear simulation model of a small commercial aircraft.

Soft Computing, 2006

The performance of non-linear identification techniques is often determined by the appropriatenes... more The performance of non-linear identification techniques is often determined by the appropriateness of the selected input variables and the corresponding time lags. High correlation coefficients between candidate input variables in addition to a non-linear relation with the output signal induce the need for an appropriate input selection methodology. This paper proposes a genetic polynomial regression technique to select the significant input variables for the identification of non-linear dynamic systems with multiple inputs. Statistical tools are presented to visualize and to process the results from different selection runs. The evolutionary approach can be used for a wide range of identification techniques and only requires a minimal input and a priori knowledge from the user. The evolutionary selection algorithm has been applied on a real-world example to illustrate its performance. The engine load in a combine harvester is highly variable in time and should be kept below an allowable limit during automatic ground speed control mode. The genetic regression process has been used to select those measurement variables that have a significant impact on the engine load and that will act as measurement variables of a non-linear model-based engine load controller.

Fuzzy logic can in several ways be applied to improve the control of the activated sludge system.... more Fuzzy logic can in several ways be applied to improve the control of the activated sludge system. In the present study, two types of fuzzy logic controllers were developed for intermittent aeration control: a low-level fuzzy controller for DO control and a high-level controller for ...