Computational burden reduction in Min-Max MPC

Min–max model predictive control (MMMPC) is one of the strategies used to control plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the complex numerical optimization problem that has to be solved at every sampling time. This paper shows how to overcome this by transforming the original problem into a reduced min–max problem whose solution is much simpler. In this way, the range of processes to which MMMPC can be applied is considerably broadened. Proofs based on the properties of the cost function and simulation examples are given in the paper

Adaptive Disturbance Rejection Using ARMARKOV/Toeplitz Models

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57792/1/RaviARMARKOVAdaptiveTCST2000.pd

Yet Another Tutorial of Disturbance Observer: Robust Stabilization and Recovery of Nominal Performance

This paper presents a tutorial-style review on the recent results about the disturbance observer (DOB) in view of robust stabilization and recovery of the nominal performance. The analysis is based on the case when the bandwidth of Q-filter is large, and it is explained in a pedagogical manner that, even in the presence of plant uncertainties and disturbances, the behavior of real uncertain plant can be made almost similar to that of disturbance-free nominal system both in the transient and in the steady-state. The conventional DOB is interpreted in a new perspective, and its restrictions and extensions are discussed

Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach

We introduce a real-time, constrained, nonlinear Model Predictive Control for the motion planning of legged robots. The proposed approach uses a constrained optimal control algorithm known as SLQ. We improve the efficiency of this algorithm by introducing a multi-processing scheme for estimating value function in its backward pass. This pass has been often calculated as a single process. This parallel SLQ algorithm can optimize longer time horizons without proportional increase in its computation time. Thus, our MPC algorithm can generate optimized trajectories for the next few phases of the motion within only a few milliseconds. This outperforms the state of the art by at least one order of magnitude. The performance of the approach is validated on a quadruped robot for generating dynamic gaits such as trotting.Comment: 8 page

Modified Active Disturbance Rejection Predictive Control: A fixed-order state-space formulation for SISO systems

[EN] This paper presents a novel control strategy that provides active disturbance rejection predictive control on constrained systems with no nominal identified model. The proposed loop relaxes the modelling requirement to a fixed discrete-time state¿space realisation of a first-order plus integrator plant despite the nature of the controlled process. A third-order discrete Extended State Observer (ESO) estimates the model mismatch and assumed plant states. Moreover, the constraints handling is tackled by incorporating the compensation term related to the total perturbation in the definition of the optimisation problem constraints. The proposal merges in a new way state¿space Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) into an architecture suitable for the servo-regulatory operation of linear and non-linear systems, as shown through validation examples.This work has been supported by MCIN/AEI/10.13039/501100011033 [Project PID2020-120087GB-C21] , MCIN/AEI/10.13039/501100011033 [Project PID2020-119468O-I00] , the Generalitat Valenciana regional government, Spain [Project CIAICO/2021/064] , and the Ministry of Science, Technology and Innovation of Colombia [scholarship programme 885] .Martínez-Carvajal, BV.; Sanchís Saez, J.; Garcia-Nieto, S.; Martínez Iranzo, MA. (2023). Modified Active Disturbance Rejection Predictive Control: A fixed-order state-space formulation for SISO systems. ISA Transactions. 142:148-163. https://doi.org/10.1016/j.isatra.2023.08.01114816314

Development of Aeroservoelastic Analytical Models and Gust Load Alleviation Control Laws of a SensorCraft Wind-Tunnel Model Using Measured Data

Aeroservoelastic (ASE) analytical models of a SensorCraft wind-tunnel model are generated using measured data. The data was acquired during the ASE wind-tunnel test of the HiLDA (High Lift-to-Drag Active) Wing model, tested in the NASA Langley Transonic Dynamics Tunnel (TDT) in late 2004. Two time-domain system identification techniques are applied to the development of the ASE analytical models: impulse response (IR) method and the Generalized Predictive Control (GPC) method. Using measured control surface inputs (frequency sweeps) and associated sensor responses, the IR method is used to extract corresponding input/output impulse response pairs. These impulse responses are then transformed into state-space models for use in ASE analyses. Similarly, the GPC method transforms measured random control surface inputs and associated sensor responses into an AutoRegressive with eXogenous input (ARX) model. The ARX model is then used to develop the gust load alleviation (GLA) control law. For the IR method, comparison of measured with simulated responses are presented to investigate the accuracy of the ASE analytical models developed. For the GPC method, comparison of simulated open-loop and closed-loop (GLA) time histories are presented

Application of strategies of advanced control under the active disturbance rejection control, to produce lipids from microalgae

40 páginasEn esta investigación se diseñaron estrategias de control avanzado bajo el enfoque del rechazo activo de perturbaciones (ADRC, Active Disturbance Rejection Control) para incrementar la producción de biomasa en cultivos de microalgas. Para lo anterior, desde el punto de vista del control, esta investigación se planeó en dos etapas: control y optimización. La primera etapa resultó en tres diseños diferentes de controladores: dos estrategias ADRC asistida por observador y un control libre de modelo (MFC, Model-Free Control). En cada caso, el objetivo fue garantizar el seguimiento de la señal de referencia. En la segunda etapa, se realizaron dos diseños de estrategias de optimización con el fin de incrementar la producción de biomasa, una fuera de línea y una en línea. Al comparar, a nivel de simulación, estas estrategias con otras propuestas ya existentes, se encontró que: 1) las estrategias ADRC asistidas por observador tienen poca dependencia del modelo, permitiendo trabajar con un modelo aproximado que solo requiere conocer el orden del sistema y la ganancia de entrada; 2) la optimización fuera de línea aunque logra maximizar la producción de biomasa requiere conocer el modelo y 3) la propuesta que combina MFC con la optimización en línea, puede actuar sobre cualquier cultivo de microalgas ya que no necesita de un modelo. Todas las propuestas son robustas frente a perturbaciones permitiendo incrementar la producción de biomasa cuando se hace uso de una estrategia de optimización.Doctorado en BiocienciasDoctor en Biociencia

Distributed Random Convex Programming via Constraints Consensus

This paper discusses distributed approaches for the solution of random convex programs (RCP). RCPs are convex optimization problems with a (usually large) number N of randomly extracted constraints; they arise in several applicative areas, especially in the context of decision under uncertainty, see [2],[3]. We here consider a setup in which instances of the random constraints (the scenario) are not held by a single centralized processing unit, but are distributed among different nodes of a network. Each node "sees" only a small subset of the constraints, and may communicate with neighbors. The objective is to make all nodes converge to the same solution as the centralized RCP problem. To this end, we develop two distributed algorithms that are variants of the constraints consensus algorithm [4],[5]: the active constraints consensus (ACC) algorithm, and the vertex constraints consensus (VCC) algorithm. We show that the ACC algorithm computes the overall optimal solution in finite time, and with almost surely bounded communication at each iteration. The VCC algorithm is instead tailored for the special case in which the constraint functions are convex also w.r.t. the uncertain parameters, and it computes the solution in a number of iterations bounded by the diameter of the communication graph. We further devise a variant of the VCC algorithm, namely quantized vertex constraints consensus (qVCC), to cope with the case in which communication bandwidth among processors is bounded. We discuss several applications of the proposed distributed techniques, including estimation, classification, and random model predictive control, and we present a numerical analysis of the performance of the proposed methods. As a complementary numerical result, we show that the parallel computation of the scenario solution using ACC algorithm significantly outperforms its centralized equivalent