Syed Khusro Saleem

Multiple Model Predictive Flood Control in Regulated River Systems with Uncertain Inflows

This paper presents a novel approach to real time automatic flood control in a managed river network that is subject to uncertain inflows. The proposed approach uses multiple models to represent inflows ranging from low to high flow. Optimal model selection is achieved in a minimum mean square error sense using a bank of Kalman filters to identify the most likely inflow characteristic. There are no a-priori probabilities assigned to the individual models. Model Predictive Control is used for water level controller design. Our Adaptive Multi Model Predictive Control (AMMPC) method is proposed as an alternative to existing techniques that also use multiple inflow models but with a-priori inflow model probabilities, either weighted or equally likely. The performance of the approach is demonstrated using a simulated river-reservoir model as well as using data collected at the Wivenhoe Dam during the 2011 floods in Queensland, Australia.

Irrigation control based on model predictive control (MPC)

This research proposes A THEORETICAL FRAMEWORK based on model predictive control (MPC) for irrigation control to minimize both root zone soil moisture deficit (RZSMD) and irrigation amount under a limited water supply. We (i) investigate means to incorporate direct measurements to MPC (ii) introduce two Robust MPC techniques - Certainty Equivalence control (CE) and Disturbance Affine Feedback Control (DA) - to mitigate the uncertainty of weather forecasts, and (iii) provide conditions to obtain two important theoretical aspects of MPC - feasibility and stability - in the context of irrigation control. Our results show that system identification enables automation while incorporating direct measurements. Both DA and CE minimize RZSMD and irrigation amount under uncertain weather forecasts and always maintain soil moisture above wilting point subject to water availability. The theoretical results are compared against the model AQUACROP, weather data and forecasts from Shepparton, Australia. We also discuss the performance of Robust MPC under different water availability, soil, crop conditions. In general, MPC shows to be a promising tool for irrigation control. MPC is used to minimize both root zone soil moisture deficit and irrigation amount.System identification incorporates direct measurements to MPC enabling automation.Uncertainty in weather forecasts is mitigated using two modified Robust MPC approaches.Optimal operation can be guaranteed through the proposed method.Guaranteed operation above wilting point at all times subject to water availability.

Model Predictive Control for Real-Time Irrigation Scheduling

Abstract Irrigation underpins agricultural productivity. The purpose of irrigation is to match water supply to crop water demand. The effectiveness of irrigation depends on the quality of the timing and duration of watering events, also called irrigation scheduling. Most farmers use heuristic rules to determine irrigation events. This often leads to over-watering which results in lower crop yields and wasted water. By acquiring good estimates of a plants water demand and local weather, it is possible to use optimization theory to compute an irrigation schedule that matches supply and demand thereby improving crop yields. Previous work has focused on scheduling irrigation over long time frames such as seasonal water allocations. Real-time irrigation scheduling, e.g. hourly or daily, has received little attention. Farmers rely on heuristic approaches implemented using simple spreadsheet tools to help them in this task. This approach cannot deal effectively with operational constraints and thereby results in poor performance. In this paper we develop a Model Predictive Control framework for real-time irrigation scheduling. The proposed formulation can take into account common operational constraints, including limitations on water availability as well as practical limits on the maximum or minimum amount of water that should be applied. We use measured climate data coupled with a simulation model to evaluate the proposed algorithm.

Real-Time Optimal Control of River Basin Networks

River basins are key components of water supply grids. River basin operators must handle a complex set of objectives including runoff storage, flood control, supply for consumptive use, hydroelectric power generation, silting management, and maintenance of river basin ecology. At present, operators rely on a combination of simulation and optimization tools to help them make operational decisions. The complexity associated with this approach makes it suitable for long term planning but not daily or hourly operation. The consequence is that between longerterm optimized operation points, river basins are largely operated in open loop. This leads to operational inefficiencies most notably wasted water and poor ecological outcomes. This paper proposes a systematic approach using optimal control based on simple low order models for the real-time operation of entire river basin networks.

Identification of MIMO systems with sparse transfer function coefficients

We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output--MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient for systems with sparse transfer function coefficients since they work on state space models. We propose a two-step algorithm where the first step identifies the system order using the subspace principle in a state space format, while the second step estimates coefficients of the transfer functions via L1-norm convex optimization. The proposed algorithm retains good features of subspace methods with improved noise-robustness for sparse systems.

Electro-optic sensor fusion tracker via a set-theoretic approach

This paper presents a novel, easily implementable set-theoretic solution to a multiple sensor data fusion and tracking problem. A centralized fusion architecture which performs data fusion at the measurement level is described. The algorithm combines 2D image data from two sensors to produce 3D tracks at each scan in the presence of false alarms. Track initiation and maintenance are performed as an integral part of the tracking process.<<ETX>>

Model predictive control of Murray-darling basin networks

River basins are the most significant component in water supply grids and are under increasing pressure from competing demands for fresh water. However, unlike energy grids which are managed very efficiently using closed-loop operation, water grids, and river basins in particular, are largely open-loop systems. One reason is the difficulty associated with developing suitable models and feedback controllers. This paper proposes a systematic approach using model predictive control based on simple low order models for the real-time operation of entire river basin networks.

Real-time optimization of irrigation scheduling in agriculture

A simulation optimization approach is developed for managing irrigation operations in agriculture. The approach combines a highly complex crop growth and yield simulation model with an optimization algorithm to calculate an irrigation sequence that maximizes crop yield. The approach overcomes the limitations of many existing irrigation scheduling optimization methods that rely on simpler but more inaccurate crop growth and yield models to ensure computational tractability. The performance of the approach is demonstrated through simulation.

Minimum volume over-bounding ellipsoids for set-based estimation in target tracking applications

Set-based estimation using ellipsoidal sets is considered. We show that obtaining the minimum volume ellipsoid over-bounding the sum of two ellipsoids in /spl Rfr//sup n/ involves a convex minimisation. We also derive computationally simple tests which represent sufficient conditions for the existence of the minimum volume ellipsoid over-bounding the intersection of two ellipsoids in /spl Rfr//sup n/.

A Fair Irrigation Scheduling Method Prioritizing on the Individual Needs of the Crops and Infrastructure Limitations

Abstract. In many agricultural countries in the world, water is supplied to the crop fields through canal based distribution systems. Fields would have different crop types with varying water demands. When irrigating multiple fields, capacities of the canal and its outlets also need to be considered. This paper develops an integrated scheduling method which addresses both these concerns, in the context of a single canal based farm focusing on short term irrigation. The proposed methodology is a step towards real time irrigation scheduling in response to future weather and crop demands. At the first stage of the method, model predictive control (MPC) calculates the irrigation demand of the individual fields. Then, particle swarm optimization (PSO) optimizes the allocation of irrigation amounts based on these demands and the demands of neighboring fields. Integer zero programming is used for optimal delivery of the suggested allocations, by opening and closing the outlets. Simulations based on the model Aquacrop show that the method is capable of maintaining the soil moisture levels above wilting point at all times while utilizing the limited infrastructure capacities available.