Implementation of an extended prediction self-adaptive controller using LabVIEW (TM)

The implementation of the Extended Prediction Self-Adaptive Controller is presented in this paper. It employs LabVIEWTM graphical programming of industrial equipment and it is suitable for controlling fast processes. Three different systems are used for implementing the control algorithm. The research regarding the controller design using graphical programming demonstrates that a single advanced control application can run on Windows, real time operating systems and FPGA targets without requiring significant program modifications. The most appropriate device may be selected according to the required processing time of the control signal and of the application. A relevant case study is used to exemplify the procedure

Design and Certification of Industrial Predictive Controllers

Three decades have passed since milestone publications by several industrial and academic researchers spawned a flurry of research and commercial, industrial activities on model predictive control (MPC). The improvement in efficiency of the on-line optimization part of MPC led to its adoption in mechanical and mechatronic systems from process control and petrochemical applications. However, the massive strides made by the academic community in guaranteeing stability through state-space MPC have not always been directly applicable in an industrial setting. This thesis is concerned with design and a posteriori certification of feasibility/stability of input-output MPC controllers for industrial applications without terminal conditions (i.e. terminal penalty, terminal constraint, terminal control). MPC controllers which differ in their modelling and prediction method are categorized into three major groups, and a general equivalence between these forms is established. Then an overview on robust set invariance is given as it plays a fundamental role in our analysis of the constrained control systems. These tools are used to give new tuning guidelines as well as a posteriori tests for guaranteeing feasibility of the suboptimal or optimal predictive control law without terminal conditions, which is fundamental towards stability of the closed loop. Next, penalty adaptation is used as a systematic procedure to derive asymptotic stability without any terminal conditions and without using set invariance or Lyapunov arguments. This analysis however is restricted to repetitive systems with input constraints. Then, predictive control without terminal conditions is considered for nonlinear and distributed systems. The invariance tools are extended to switching nonlinear systems, a proof of convergence is given for the iterative nonlinear MPC (NMPC), and a guarantee on overall cost decrease is developed for distributed NMPC, all without terminal conditions. Reference generation and parameter adaptation are shown to be effective mechanisms for NMPC and distributed NMPC (DNMPC) under changing environmental conditions. This is demonstrated on two benchmark test-cases i.e. the wet-clutch and hydrostatic drivetrain, respectively. Terminal conditions in essence are difficult to compute, may compromise performance and are not used in the industry. The main contribution of the thesis is a systematic development and analysis of MPC without terminal conditions for linear, nonlinear and distributed systems.This work was supported within the framework of the LeCoPro project (grant nr. 80032) of the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen)

A low computational cost, prioritized, multi-objective optimization procedure for predictive control towards cyber physical systems

Cyber physical systems consist of heterogeneous elements with multiple dynamic features. Consequently, multiple objectives in the optimality of the overall system may be relevant at various times or during certain context conditions. Low cost, efficient implementations of such multi-objective optimization procedures are necessary when dealing with complex systems with interactions. This work proposes a sequential implementation of a multi-objective optimization procedure suitable for industrial settings and cyber physical systems with strong interaction dynamics. The methodology is used in the context of an Extended Prediction self-adaptive Control (EPSAC) strategy with prioritized objectives. The analysis indicates that the proposed algorithm is significantly lighter in terms of computational time. The combination with an input-output formulation for predictive control makes these algorithms suitable for implementation with standardized process control units. Three simulation examples from different application fields indicate the relevance and feasibility of the proposed algorithm

Economic differences between cumulative and episodic reduction of sediment from cropland

This study compares measures for reducing cumulative sediment loads from cropland with measures for reducing sediment loads from extreme storms. The issue is whether the optimal means of controlling cumulative loads are very different from the optimal controls for storm event loads. Differences are described in terms of costs and management practices. The analysis entailed developing a storm-event simulation model analogous to the SEDEC sedimentation economics model. The analogue model was used to identify the respective optimal cropland management strategies for various extreme storm conditions. These strategies were then analyzed using the annual average SEDEC, and the optimal strategies from SEDEC were analyzed for their storm-event properties. The comparisons permit conclusions concerning the relative effectiveness of management strategies for achieving cumulative sediment goals versus storm-event load goals. Data for a 223 study site in the Highland-Silver Lake Watershed in Southwestern Illinois were analyzed using this approach. The study produced four main conclusions. First, control costs for episodic sediment loads were consistently higher than the costs for proportionate reductions in annual average loads. Furthermore, strategies for reducing cumulative loads generally achieve less than proportionate reductions in cumulative loads. Second, the highest control costs were generally for the most extreme storms. Third, contour cultivation is a key element of efficient management strategies for row crops. Finally, where a permanent grass crop is grown adjacent to the stream, there is generally little more to be gained by changing upslope management practices. This suggests that grass strips along streams would greatly reduce the need to modify farming practices elsewhere in order to limit sedimentation.U.S. Department of the InteriorU.S. Geological SurveyOpe

Increasing the Reliability of Power and Communication Networks via Robust Optimization

Uncertainty plays an increasingly significant role in the planning and operation of complex networked infrastructure. The inclusion of variable renewable energy in power systems makes ensuring basic grid requirements such as transmission line constraints and the power balance between supply and demand more involved. Likewise, data traffic in communication networks varies greatly with user preferences and service availability, and with communication networks carrying more traffic than ever due to the surge in network-enabled devices, coping with the highly variable data flows between server and end-users becomes more crucial for the network's overall stability. Within this context, we propose in this thesis new adaptable methods for optimizing flows in power and communication systems that explicitly consider the growing variability in these systems to guarantee optimal operation with a flexible degree of reliability. The proposed methods use a robust optimization framework, making constraints dependent on uncertain factors tractable by replacing originally stochastic conditions with deterministic counterparts. The primary benefit of robust methods is that they ensure the system is feasible for any values of the uncertain variables within a given continuous set of possible realizations. This, however, can lead to excessively conservative solutions. Therefore, we also investigate how to reduce the conservativeness of the proposed algorithms. This thesis focuses on two classes of problems in power and communication systems, flow control and the placement of flow-controlling devices. In power systems, flow control refers to actions that induce changes in the power carried by transmission lines to minimize or maximize a specific objective value while considering the electrical grid's physical constraints. Some examples of power flow control actions are the change of switching equipment's state, regulation of generators' set points, and the management of the so-called Flexible AC Transmission Systems (FACTS) devices. For the last two action types, we propose a robust approach to optimize the corresponding control policies. As for communication networks, (data) flow control is implemented at each router in the network. These routers define the path and the rate data is forwarded using routing tables. We show that it is possible to robustly design policies to adapt these routing tables that optimize the data flows in the network depending on the instantaneous rate of the system's exogenous inputs. For both flow problems, we employ a robust optimization framework where affine-linear functions parametrize the flow control policies. The parametrized policies can be efficiently computed via linear or quadratic programming, depending on the system's constraints. Furthermore, we consider the placement problems in the form of FACTS placement and the embedding of virtual networks in an existing communication network to improve the reliability of the network systems. Both problems are formulated as robust Mixed-Integer Linear Programs (MILP). However, because finding provable optimal solutions in large networks is computationally challenging, we also develop approximate algorithms that can yield near-optimal results while being several times faster to solve than the original MILP. In the proposed robust framework, the flow control and the placement of controlling-devices problems are solved together to take into account the coupling effects of the two optimization measures. We demonstrate the proposed methodology in a series of use cases in power and communication systems. We also consider applications in Smart Grids, where communication and electric networks are closely interlinked. E.g., communication infrastructure enables real-time monitoring of the status of power grids and sending timely control signals to devices controlling the electric flow. Due to the increasing number of renewable energy resources, Smart Grids must adapt to fast changes in operating conditions while meeting application-dependent reliability requirements. The robust optimization methods introduced in this thesis can thus use the synergy between flexible power and communication systems to provide secure and efficient Smart Grid operation

Incorporation of the Generalized Tsk Models in Model Predictive Control

The generalized TSK (GTSK) modeling approach is proved to provide accurate model prediction and to alleviate the computational burden. The scope of this study is to incorporate the GTSK models in the nonlinear model predictive control (NMPC) to improve the overall performance and reliability of NMPC. A novel global optimization method, the Leapfrogging technique, is also used to further improve the NMPC's computational efficiency. Another innovation, the "sawtooth" pattern is used as input signal to generate the GTSK model. The experiments and tests are conducted on a nonlinear process simulation system, in which the NMPC control algorithm was embedded. The virtual process in this simulator is fourth-order-plus-dead-time (FOPDT) process with a nonlinear gain and the environmental effect (noise and disturbance). The controlled process is subject to both soft and hard constraints - soft on both the controlled and the auxiliary variable, and hard on both the limits and rate of change of the manipulated variable. The NMPC performance is evaluated via several simulation experiments, which involved constraint handling, interactions and process nonlinearity. The use of a GTSK model and Leapfrogging as an optimizer were demonstrated as effective for nonlinear model predictive control. The nonlinear model is firstly developed by using GTSK approach. The prediction accuracy of the GTSK model was illustrated and quantified by a comparison with SOPDT model. The GTSK model was much better. The performance of GTSK MPC controller is evaluated via seven sets of dynamic control simulation. The controller showed desirable performance for disturbance rejection, set point tracking, constraint handling, and comprehensive environmental effect handling.School of Chemical Engineerin