Mietek A. Brdys

Dynamic neural controllers for induction motor

The paper reports application of recently developed adaptive control techniques based on neural networks to the induction motor control. This case study represents one of the more difficult control problems due to the complex, nonlinear, and time-varying dynamics of the motor and unavailability of full-state measurements. A partial solution is first presented based on a single input-single output (SISO) algorithm employing static multilayer perceptron (MLP) networks. A novel technique is subsequently described which is based on a recurrent neural network employed as a dynamical model of the plant. Recent stability results for this algorithm are reported. The technique is applied to multiinput-multioutput (MIMO) control of the motor. A simulation study of both methods is presented. It is argued that appropriately structured recurrent neural networks can provide conveniently parameterized dynamic models for many nonlinear systems for use in adaptive control.

Iterative Algorithms for Multilayer Optimizing Control

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Stable indirect adaptive control based on discrete-time T--S fuzzy model

This paper presents an indirect adaptive fuzzy control scheme for uncertain nonlinear asymptotically stable plants. A discrete-time T-S fuzzy input-output model is employed to approximate the unknown plant dynamics. The T-S fuzzy model is fed with its own states, which are indeed its past outputs, rather than the measurements from the plants. Entirely based on this model, a feedback linearization control law is designed by using the model structure, model parameters and model states and then applied to control the model and the plant. Premise and consequent parameters of the model are updated on-line by gradient descent algorithm and recursive least square estimation method using plant output measurements. Stability analysis shows that if there the model structure is accurate the adaptive controller achieves the bounded tracking error and boundedness of all the closed-loop signals. In the ideal case where the signals are persistently exciting during the model parameter adaptation a perfect asymptotic tracking is ensured. The effectiveness of the method is verified by simulation application to SISO and MIMO example plants.

Grid Implementation of a Parallel Multiobjective Genetic Algorithm for Optimized Allocation of Chlorination Stations in Drinking Water Distribution Systems: Chojnice Case Study

Solving multiobjective optimization problems requires suitable algorithms to find a satisfactory approximation of a globally optimal Pareto front. Furthermore, it is a computationally demanding task. In this paper, the grid implementation of a distributed multiobjective genetic algorithm is presented. The distributed version of the algorithm is based on the island algorithm with forgetting island elitism used instead of a genetic data exchange. The algorithm is applied to the allocation of booster stations in a drinking water distribution system. First, a multiobjective formulation of the allocation problem is further enhanced in order to handle multiple water demand scenarios and to integrate controller design into the allocation problem formulation. Next, the new grid-based algorithm is applied to a case study system. The results are compared with a nondistributed version of the algorithm.

Feedback control of water quality

Drinking water distribution networks (DWDN) are complex, large-scale systems designed to supply clean water to industrial and domestic users. To reduce the risk of human exposure to pathogens, drinking water is required to contain a small disinfectant residual. The most common disinfectant used in DWDN is chlorine because it is inexpensive and effectively controls a number of disease-causing organisms. The article formulates the water quality control problem and proposes a design approach based on parameter estimation and adaptive control techniques.

Dissolved oxygen control for activated sludge processes

Controllers for dissolved oxygen reference trajectory tracking for activated sludge processes are proposed and investigated. A nonlinear model predictive controller and a direct reference adaptive controller are investigated. Both the nutrient and the phosphorous removal from a wastewater by its biological treatment using an activated sludge technology are considered. An approach to the controller design utilises a structure of the dissolved oxygen dynamics and its two time scales: fast and slow. The predictive controllers offer good tracking performance and robustness. The direct model reference adaptive controller is much simpler to implement. However, it is more difficult to compromise between tracking accuracy and rate of change and magnitudes of the control actions. The controllers are validated by simulation using real data sets and an ASM2d model of the biological reactor.

Hierarchical model predictive control of integrated quality and quantity in drinking water distribution systems

An integrated approach to control of quantity and quality in water supply and distribution systems is proposed. The integrated control consists in optimising the operational cost, meeting a demand on water of desired quality and maintaining the system constraints. This constrained optimising control problem is complex due to nonlinearities, large dimension, output constraints, mixed-integer structure of the variables involved, at least two time scales in the system dynamics and an uncertainty. A sub-optimal two-level hierarchical control structure is proposed that allows incorporating the desired controller functions and yet making the synthesis of these functions possible. The algorithms for implementing the functionalities are proposed and discussed. Detail design of the lower level controller is presented and investigated. The controller performance is validated by simulation.

ROBUST MODEL PREDICTIVE CONTROL UNDER OUTPUT CONSTRAINTS

Abstract Control of a linear time-varying uncertain dynamical system with delayed inputs is considered. The model parameters, disturbance inputs and model structure errors are unknown but bounded, and the parameter value can abruptly change. The objective is to keep the system output within prescribed limits regardless of the uncertainty scenario. A model predictive type of controller is designed that utilises a set bounded model of the uncertainty and employs safety zones modifying the original constraints so that the control input feasibility can be guaranteed. The controller is applied to quality control in a benchmark Drinking Water Distribution System, and its performance is validated by simulation.

Monitoring of Chlorine Concentration in Drinking Water Distribution Systems Using an Interval Estimator

Monitoring of Chlorine Concentration in Drinking Water Distribution Systems Using an Interval Estimator This paper describes the design of an interval observer for the estimation of unmeasured quality state variables in drinking water distribution systems. The estimator utilizes a set bounded model of uncertainty to produce robust interval bounds on the estimated state variables of the water quality. The bounds are generated by solving two differential equations. Hence the numerical efficiency is sufficient for on-line monitoring of the water quality. The observer is applied to an exemplary water network and its performance is validated by simulations.

APPLICATION OF FUZZY MODEL PREDICTIVE CONTROL TO THE DISSOLVED OXYGEN CONCENTRATION TRACKING IN AN ACTIVATED SLUDGE PROCESS

Abstract Maintaining desired concentration of the dissolved oxygen (DO) in an activated sludge process is crucial for feasible and efficient operation of a wastewater treatment plant. The dissolved oxygen dynamics is nonlinear and of high dimension. The available models involve many parameters that are very difficult to estimate. Utilising the dynamics structure and its multiple time scale a simplified nonlinear SISO model was recently adopted with a disturbance inputs that can be efficiently and sufficiently accurately predicted over short time period. Based on this model a nonlinear model predictive controller was designed showing good performance. However necessity of solving a nonliner optimisation task during the controller operation limits its performance under large and fast changes of disturbances or reference trajectories. In the paper a fuzzy Takagi - Sugeno type model of the nonlinear dynamics is produced based on its local linearisations. The recently proposed fuzzy predictive control strategy is then applied to obtain a nonlinear fuzzy predictive controller. The controller is tested and validated on physical data sets showing substantial savings in computing time with negligible loss on its performance.