A5040508398- Advanced Control Systems Optimization
- Fault Detection and Control Systems
- Control Systems and Identification
- Adaptive Control of Nonlinear Systems
- Experimental Learning in Engineering
- Process Optimization and Integration
- Iterative Learning Control Systems
- Advanced Control Systems Design
- Mechatronics Education and Applications
- Fuel Cells and Related Materials
- Innovative Teaching Methods
- Distributed Control Multi-Agent Systems
- Piezoelectric Actuators and Control
- Engineering Education and Pedagogy
- Problem and Project Based Learning
- Glycogen Storage Diseases and Myoclonus
- Topology Optimization in Engineering
- Ophthalmology and Eye Disorders
- Sensorless Control of Electric Motors
- Lower Extremity Biomechanics and Pathologies
- Botulinum Toxin and Related Neurological Disorders
- Teleoperation and Haptic Systems
- Online and Blended Learning
- Innovative Teaching and Learning Methods
- Magnetic Properties of Alloys
Universidad de Sevilla
2012-2024
University of Valparaíso
2022
University of Stuttgart
2013
This paper presents a novel tracking predictive controller for constrained nonlinear systems capable to deal with sudden and large variations of piece-wise constant setpoint signal. The uncertain nature the may lead stability feasibility issues if regulation based on stabilizing terminal constraint is used. model presented in this extends MPC linear more complex case systems. key idea addition an artificial reference as new decision variable. considered cost function penalizes deviation...
Highlights Sheepherders express pride in their resilience, skill, and responsibility for flock's livelihood. face challenges meeting basic needs like food, clothing, medical care. Isolation environmental hardships take a toll on sheepherders' mental health. Challenging conditions affect the working living of sheepherders open range. Abstract. western United States often work remote areas small campers, have long periods isolation, challenging conditions, including extreme weather predator...
This paper deals with the tracking problem for constrained nonlinear systems using a model predictive control (MPC) law. MPC provides law suitable regulating linear and to given target steady state. However, when operating point changes, feasibility of controller may be lost fails track reference. In this paper, novel changing constant references is presented. The main characteristics are: (i) considering an artificial state as decision variable, (ii) minimizing cost that penalizes error...
This paper presents an experimental, Arduino based, low cost self-balancing robot developed at the University of Seville for control education. The main idea is that students can learn electronics, computer programming, modeling, and signal processing by means construction this robot. resulting model a multivariable unstable nonlinear system with non-minimum phase zero. Experimental results obtained are included to demonstrate possibilities prototype.
In this paper, we propose a novel robust model predictive controller for tracking periodic signals linear systems subject to bounded additive uncertainties based on nominal predictions and constraint tightening. The proposed joins optimal trajectory planning control in single optimization problem guarantees that the perturbed closed-loop system converges asymptotically neighborhood of an reachable while robustly satisfying constraints. addition, maintains recursive feasibility even presence...
This paper presents a novel formulation of robust output feedback model predictive controller to track piecewise constant references (output RMPCT). The real plant is assumed be modelled as linear system with additive bounded uncertainties on the states. Under mild assumptions, proposed MPC can steer uncertain in an admissible evolution any steady state, that is, under change set point. consists stable state estimator and recently developed robustly stabilizing, tube based, control for...
Abstract Model predictive control (MPC) is one of the few techniques which able to handle constraints on both state and input plant. The admissible evolution asymptotic convergence closed-loop system ensured by means suitable choice terminal cost constraint. However, most existing results MPC are designed for a regulation problem. If desired steady-state changes, controller must be redesigned guarantee feasibility optimisation problem, as well stability. Recently, novel has been proposed...
This article presents a sparse, low-memory footprint optimization algorithm for the implementation of model predictive control (MPC) tracking formulation in embedded systems. MPC has several advantages over standard formulations, such as an increased domain attraction and guaranteed recursive feasibility even event sudden reference change. However, this comes at expense addition small amount decision variables to MPC's problem that complicates structure its matrices. We propose sparse...
This paper shows how risk management can be applied to schedule the operation of combined heat and power plants in order consider process uncertainties. The main innovative point is consideration mitigation actions reduce exposure identified risks. Model predictive control used select strategic plan actions.
This paper presents a novel formulation of robust model predictive controller (RMPCT) to track piecewise constant references. The real plant is assumed be modelled as linear system with additive bounded uncertainties on the states. Under mild assumptions, proposed MPC can steer uncertain in an admissible evolution any steady state, that is, under change set point. allows us reject disturbances compensating effect then, changing setpoint. Feasibility for setpoint achieved by adding artificial...
In this paper, a novel model predictive control (MPC) formulation has been proposed to solve tracking problems, considering generalized offset cost function. Sufficient conditions on function are given ensure the local optimality property. This allows consider as target operation points, states which may be not equilibrium points of linear systems. case, it is proved in paper that law steers system an admissible steady state (different target) optimal with relation Therefore, controller for...
This paper presents a new robust MPC formulation based on nominal predictions that improves other popular formulations such as Chisci et al. (2001); Mayne (2005). As in (2001), the proposed controller ensures feasibility by means of tighter constraints, however, condition terminal constraint has been notably relaxed, leading to less conservative controllers. Another remarkable property is calculation (an approximation of) minimum positively invariant set not required, which makes its design...
Many popular approaches in the field of robust model predictive control (MPC) are based on nominal predictions. This paper presents a novel formulation this class controller with proven input-to-state stability and constraint satisfaction. Its advantages are: (i) design its main ingredients tractable for medium to large-sized systems, (ii) terminal set does not need be respect all possible system uncertainties, but only reduced that can made arbitrarily small, thus facilitating...