7 research outputs found
Modelling and control design of river systems
Get PDF© 2011 Dr. Mathias Fui Lin FooFarming consumes a large amount of water usage and it is reported that large portion of this water is wasted through inefficient water distribution from river to farms. More efficient water distribution and preservation of environmental demands can be achieved through better control and decision support systems. In order to design better control and decision support systems, a river model is required. This model needs to be able to capture the relevant river dynamics and easy to be used for control design.
Traditionally, the Saint Venant equations have been used to model river systems. These equations are nonlinear hyperbolic partial differential equation (PDE) and are solved numerically using Preissmann scheme. The simulated Saint Venant equations are compared against operational data from the Broken River, and it is found that the Saint Venant equations are accurate in representing the river systems. Through further study, it is found that a single segmentation, i.e. treating the river as one long stretch with uniform geometry is sufficiently accurate for representation of the river for the purpose of control design. For the representation of meandering river, the Saint Venant equations are as accurate a two-dimensional flow model. The nonlinearities in the Saint Venant equations are also investigated. From the nonlinearity test, it is found that the Saint Venant equations are approximately linear within an operating region.
The Saint Venant equations are difficult to use for control design. An alternative model is therefore sought. Based on the operational data from the Broken River, simple time delay model (TDM) and integrator delay model (IDM) are proposed and estimated using system identification procedures. These models are found to be accurate in capturing the relevant dynamics of the river system. Furthermore, they are easy to use for control design. It is found that the time delay varies with the flow and hence controllers must be robust to variations in the time delay. A comparison between both TDM and IDM and the Saint Venant equations shows that they are as accurate as the Saint Venant equations within the operating range. The TDM and IDM are desirable as they are easier to be used for control design and decision support system.
The TDM and IDM are used to design Model Predictive Control (MPC) to control the river system. The choice of using MPC is motivated by the fact that MPC handles constraints very well. Despite that, tuning the weights in the MPC cost function is not trivial. The methods of reverse engineering are used to obtain these weights. Building on the results of existing method of reverse engineering used in the literatures, two additional methods are developed. In addition, the design of MPC from scratch is also considered. A realistic year long simulations using both MPCs on the Broken River is carried out. The MPCs are compared with the current manual operation and a decentralised control configuration. The results show that with MPCs, significant water savings, improvement of water delivery service to the irrigators and the environmental demands satisfaction are achieved
Nonlinear Compensator Design For Bilinear Systems
No full textThere are many processes, which inherit the behaviour of a bilinear system. Bilinear systems can be considered as a variation of linear system due to their many similarities with the latter. Despite being treated as a variation of linear systems, the effect of the nonlinearities is still distinctive. Hence, there arises a need to compensate for these nonlinearities. This leads to the non linear compensator design issues for bilinear systems
Kernel design for estimation of resonant systems: A case study on vehicle suspension
No full textThis paper considers the kernel design for impulse response estimation of resonant systems,
exemplified through a vehicle suspension system. The identification of resonant systems is highly
challenging due to their long impulse responses and dynamics that may be close to the limit of
stability. These issues have not been investigated in the existing literature on kernel design. A
novel incremental approach for the design of multiple-kernels is proposed, where the kernels are
added sequentially in a structured manner, with checks to ensure that they match practical ex
pectations. The proposed method possesses the advantages of simplicity, low computational
complexity, good numerical conditioning and high feasibility in practical applications. A detailed
case study on a vehicle suspension system is presented for the theoretical case and experimental
cases with two different grades of road roughness leading to different resonant behavior. It was
found that the proposed technique resulted in simple multiple-kernels comprising a combination
of Kautz kernels that outperformed the current state-of-the-art diagonal-correlated and single
Kautz kernels, recording the highest output fit and the lowest uncertainty under real-world
conditions on four separate experimental datasets. Additionally, the incremental approach suc
cessfully captured the multiple resonances in the system. Comparison with multiple-kernels of the
same structure but optimized using a competing technique showed that the proposed method has
an increasing edge as the number of resonances increases. The findings from this work are sig
nificant for identifying systems with multiple resonances, such as for structural health monitoring
and the development of digital twins
Road classification using built-in self-scaling method of Bayesian regression
Get PDFThis paper proposes the use of the built-in self-scaling (BS) method for ISO classification of road roughness. The technique employs the transfer function between the vehicle body acceleration as input and the suspension travel as output. This transfer function has a nonzero dc gain, which is important for application of the BS method. Frequency response magnitude patterns corresponding to this transfer function are estimated via Bayesian regression, capitalizing on the inherent properties of the BS method where the prior dc gain is incorporated into the formulation. This strategy leads to high classification accuracy. The proposed approach requires only low-cost sensors. It possesses a short detection time of 0.5s and a short training time of 5s for each road class. The method is model-free and does not require recalibration when the load carried by the vehicle changes. Additionally, it is capable of handling varying vehicle velocity and is effective for both passive and active suspensions. A laboratory-scale experiment shows that the proposed technique increases the percentage of correct classification by an average of 34% in the case of constant road profiles, compared with a state-of-the-art method using augmented Kalman filtering. A corresponding value of 24% is achieved for a varying road profile. The significant improvement in the accuracy of road classification is impactful as it will enable controller design for suspension systems to be enhanced resulting in more comfortable ride and higher vehicle
stability
Pseudorandom maximum length signal design with bias compensation least squares estimation for system identification
No full textThe effect of input and output noise towards the identification of the best linear approximation of a system is investigated. This leads to the problem of errors-in-variables (EIV). The effectiveness of one particular EIV method, namely the bias compensation least squares estimation method, is analyzed, with simulations carried out on a first order bilinear system. It is shown that the use of perturbation signals with carefully selected harmonic properties can lead to significant improvements in the estimation of the best linear approximation of the system. In particular, a spectrum that is sparser but having a larger magnitude at the nonzero harmonics is found to be more robust towards the effect of noise
Modelling of Direction-dependent Systems using Bilinear Models
No full textThe modelling of first and second order direction-dependent systems using bilinear models is considered, for perturbation using periodic signals. Equivalence between the two systems can be obtained for first order systems under binary perturbation. In most other cases, a close match can be obtained. The relationship between the parameters of a direction-dependent system and those of a bilinear system is investigated
Optimisation of a static nonlinear compensator for bilinear systems
No full textA static nonlinear compensator is proposed to reduce the effects of nonlinear distortion in bilinear systems. Optimisation of the compensator using a maximum length ternary signal is discussed. Since the signal contains only odd harmonic components, the optimisation is reduced to a minimisation of the ratio of the even to odd order components at the system output. The performance of the compensator is evaluated for both first order and second order bilinear systems. A significant reduction of nonlinear distortion is obtained using the compensator. It is also shown that the tuning of the proposed compensator is robust in the presence of noise
