Abstract:Abstract-The results of a PLC implementation of embedded Model Predictive Control (MPC) for an industrial problem are presented in this paper. The embedded MPC developed is based on the linear MPC module in SEPTIC (Statoil Estimation and Prediction Tool for Identification and Control), and it combines custom ANSI C code generation with problem size reduction methods, embedded real-time considerations, and a primal-dual first-order method that provides a fast and light QP solver obtained from the FiOrdOs code g… Show more
“…Recent results are addressing this important issues from the perspectives of software tools (e.g. [34], [16]), computational efficiency [35], and dependability, e.g. [36].…”
Section: Discussionmentioning
confidence: 99%
“…[14], [15]. Continuing expansion of MPC to new applications is made possible by increasingly faster computer hardware, as well as more efficient and user-friendly software design tools [16], [17], [18], [19]. For example, [20], [21], [22], [23] explore control of a diesel-electric power plant with MPC, and MPC has also been used to realize dynamic control allocation [24], [25].…”
Marine vessels with dynamic positioning capability are typically equipped with many enough thrusters to make them overactuated, and with satellite navigation and other sensors to determine their position, heading and velocity. An automatic control system is tasked with coordinating the thrusters to move the vessel in any desired direction and to counteract the environmental forces. The design of this control system is usually separated into several levels. First, a dynamic positioning (DP) control algorithm calculates the total force and moment of force that the thruster system should produce. Then, a thrust allocation (TA) algorithm coordinates the thrusters so that the resultant force they produce matches the request from the DP control algorithm. Unless significant heuristic modifications are made, the DP control algorithm has limited information about the thruster effects such as saturations and limited rate of rotation of variable-direction thrusters, as well as systemic effects such as singular thruster configurations. The control output produced with this control architecture is therefore not always optimal, and may result in a position loss that would not have occurred with a more sophisticated control algorithm. Recent advances in computer hardware and algorithms make it possible to consider model-predictive control algorithm (MPC) that combines positioning control and thrust allocation into a single algorithm, which theoretically should yield a near-optimal controller output. The presented work explores advantages and disadvantages of using model predictive control compared to the traditional algorithms.
“…Recent results are addressing this important issues from the perspectives of software tools (e.g. [34], [16]), computational efficiency [35], and dependability, e.g. [36].…”
Section: Discussionmentioning
confidence: 99%
“…[14], [15]. Continuing expansion of MPC to new applications is made possible by increasingly faster computer hardware, as well as more efficient and user-friendly software design tools [16], [17], [18], [19]. For example, [20], [21], [22], [23] explore control of a diesel-electric power plant with MPC, and MPC has also been used to realize dynamic control allocation [24], [25].…”
Marine vessels with dynamic positioning capability are typically equipped with many enough thrusters to make them overactuated, and with satellite navigation and other sensors to determine their position, heading and velocity. An automatic control system is tasked with coordinating the thrusters to move the vessel in any desired direction and to counteract the environmental forces. The design of this control system is usually separated into several levels. First, a dynamic positioning (DP) control algorithm calculates the total force and moment of force that the thruster system should produce. Then, a thrust allocation (TA) algorithm coordinates the thrusters so that the resultant force they produce matches the request from the DP control algorithm. Unless significant heuristic modifications are made, the DP control algorithm has limited information about the thruster effects such as saturations and limited rate of rotation of variable-direction thrusters, as well as systemic effects such as singular thruster configurations. The control output produced with this control architecture is therefore not always optimal, and may result in a position loss that would not have occurred with a more sophisticated control algorithm. Recent advances in computer hardware and algorithms make it possible to consider model-predictive control algorithm (MPC) that combines positioning control and thrust allocation into a single algorithm, which theoretically should yield a near-optimal controller output. The presented work explores advantages and disadvantages of using model predictive control compared to the traditional algorithms.
“…A linear MPC problem is generally easier to solve than a NMPC problem, and effective linear solvers like CVXGEN [18] and qpOASES [19] could provide the real-time properties needed. In the literature several linear MPC and linear optimization problem implementations, such as [15,20,21,22,23], are reported with good real-time properties. Another approach would be to use an effective C/C++ library, e.g.…”
Abstract-Advances in power electronics drive systems for variable speed operation has enabled extensive use of such solutions in the propulsion and thruster systems of marine vessels. These solutions however introduce current and voltage distortions that compromises the overall power quality of the onboard electrical system. This paper presents and discusses one approach for generating the harmonic current reference for an active filter based on optimization. Two relevant results are revealed by this study: 1) lower THD values are attained by performing system optimization compared to local compensation of one load, and 2) the lower THD values are achieved with a smaller active filter rating than the one required for local load compensation.
“…This is where the efficiency-boosting achievements of control theory in the field of MPC come to the foreground: these developments allow one to implement better control methods with less resources. Because of the improvements in algorithm efficiency, model predictive control can now be implemented on embedded hardware such as MCUs [7], [8], programmable logic controllers (PLC) [9]- [11], or field programmable gate arrays (FPGA) [4], [12], [13], etc. Efficiency improvements in nominal or deterministic MPC can be divided into two main categories [14].…”
Abstract-This paper presents an efficient real-time implementation of embedded model predictive control, adopted in the context of active vibration control with the objective of minimizing the tip deflection of lightly damped cantilever beams. In particular, we focus on memory and time-efficient explicit solutions to the associated constrained optimal control problem that are easily implementable on low-end embedded hardware. To this end, we exploit the concept of convex lifting and show how it can be used to devise low-complexity, regionless piecewise affine controllers without any loss of optimality and performance. Efficiency of this constructive procedure is quantified via an extensive complexity analysis, evidenced by a successful practical deployment and optimal vibration control performance using a family of 32-bit ARM Cortex-M based microcontroller platforms.
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