State estimates are needed for optimising model predictive control of nitrogen and phosphorous removal in a wastewater treatment plant due to limited state measurements available. The MPC optimiser to implement an inf...
详细信息
State estimates are needed for optimising model predictive control of nitrogen and phosphorous removal in a wastewater treatment plant due to limited state measurements available. The MPC optimiser to implement an information feedback from the plant uses the estimates. Parameters of a grey box model used by MPC for the output prediction purposes need to be updated as well. The state estimates are then used, as pseudo measurements of the states by the parameter estimation algorithm. Otherwise the joint state and parameter estimation does not provide needed accuracy due to limited measurement programme. The paper applies extended Kaiman filter to the plant model that is based on ASM2d model of the biological reactor. The estimator is tested by simulation on a benchmark producing encouraging results.
Optimising control of wastewater treatment systems (WWTS), allowing for cost savings while fulfilling the effluent discharge limits over long period requires application of advanced control techniques. Model Predictiv...
详细信息
Optimising control of wastewater treatment systems (WWTS), allowing for cost savings while fulfilling the effluent discharge limits over long period requires application of advanced control techniques. Model Predictive control (MPC) is a very suitable control technology for a synthesis such a truly multivariable controller that can handle constraints and accommodate model-based knowledge combined with hard measurements. A hierarchical architecture is used to structure the MPC controller into the functional Supervisory, Optimising and Follow up levels. The Optimising control Level is further decomposed into three layers operating in different time scales: slow, medium and fast. The paper considers MPC controller operating in medium time scale. As it is impossible to efficiently control the plant under all possible influent conditions, three dedicated control strategies were designed. The controller performance was validated by simulation based on Kartuzy WWTS data records. Recently developed mechanism for soft switching between different MPC control strategies was used in the simulations.
Recently, a novel optimality based Repetitive control algorithm was proposed in (Hätönen et al. , 2003). According to the convergence analysis carried out in that paper, the algorithm will result in asymptot...
详细信息
Recently, a novel optimality based Repetitive control algorithm was proposed in (Hätönen et al. , 2003). According to the convergence analysis carried out in that paper, the algorithm will result in asymptotic convergence for an arbitrary discrete-time LTI plant and a T -periodic reference. However, the performance of the algorithm was tested only using simulation studies. In order to rigorously test how the algorithm performs with real systems, in this paper the algorithm is implemented on a non-minimum phase spring-mass-damper system. The results are very satisfactory, because the algorithm results in near perfect tracking with this rather demanding plant type.
In this paper, a new model inverse optimal iterative learning control algorithm is practically implemented on an industrial gantry robot. The algorithm has only one tuning parameter which can be adjusted to provide a ...
详细信息
In this paper, a new model inverse optimal iterative learning control algorithm is practically implemented on an industrial gantry robot. The algorithm has only one tuning parameter which can be adjusted to provide a balance between convergence speed and robustness. Results show that the algorithm is capable of learning the required trajectory in very few iterations. However at this convergence rate the lack of robustness is a major issue. Appropriate use of the tuning parameter is shown to greatly increase the algorithm robustness as demonstrated by tests which successfully reach 600 iterations.
This paper represents a new technique for remapping the distributed object-oriented software methods to target architecture. This technique takes into consideration the advantages of the structure inherit in object-or...
详细信息
Differential linear repetitive processes are a class of continuous-discrete 2D linear systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such syste...
详细信息
Although the need for visible watermarking for copyright notification is apparent, visible watermarking has received much less attention than invisible watermarking. In this paper, we present a general framework for p...
详细信息
Although the need for visible watermarking for copyright notification is apparent, visible watermarking has received much less attention than invisible watermarking. In this paper, we present a general framework for performing visible watermarking based on some concepts of image fusion. Especially, we introduce a new visible watermarking algorithm in which the watermarked coefficients are computed using global as well as local characteristics of both the host and watermark images.
Differential linear repetitive processes are a class of continuous-discrete 2D linear systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such syste...
详细信息
Differential linear repetitive processes are a class of continuous-discrete 2D linear systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such systems is that information propagation in one of the two independent directions only occurs over a finite interval. Applications areas include iterative learning control and iterative solution algorithms for classes of dynamic nonlinear optimal control problems based on the maximum principle. In this paper, we investigate further the structural links between differential linear repetitive processes and a special class of time delay systems. This leads to some significant new controllability and optimal control results for these processes.
Magnetic measurement is a typical inverse problem in Biomedical field. In this kind of problem we always need to locate the positions and moments of one or more magnetic dipoles. Although using the traditional methods...
详细信息
Magnetic measurement is a typical inverse problem in Biomedical field. In this kind of problem we always need to locate the positions and moments of one or more magnetic dipoles. Although using the traditional methods to solve this kind of inverse problem has all kinds of shortcomings, BPNN (Back Propagation Neural Networks) method can be used to solve this typical inverse problem fast enough for real time measurement. In the traditional BPNN method, gradient descent search method is performed for error propagation. In this paper the authors propose a new algorithm that Newton method is performed for error propagation. For the cost function is highly nonconvex in the magnetic measurement problem, the new kind of BPNN can get convergent results quickly and precisely. A simulation result for this method is also presented.
暂无评论