1,198 research outputs found
Robust economic model predictive control: recursive feasibility, stability and average performance
This thesis is mainly concerned with designing algorithms for Economic Model Predictive Control (EMPC), and analysis of its resulting recursive feasibility, stability and asymptotic average performance.
In particular, firstly, in order to extend and unify the formulation and analysis of economic model predictive control for general optimal operation regimes, including steady-state or periodic operation, we propose the novel concept of a “control storage function” and introduce upper and lower bounds to the best asymptotic average performance for nonlinear control systems based on suitable notions of dissipativity and controlled dissipativity. As a special case, when the optimal operation is periodic, we present a new approach to formulate terminal cost functions.
Secondly, aiming at designing a robust EMPC controller for nonlinear systems with general optimal regimes of operation, we present a tube-based robust EMPC algorithm for discrete-time nonlinear systems that are perturbed by disturbance inputs. The proposed algorithm minimizes a modified economic objective function, which considers the worst cost within a tube around the solution of the associated nominal system. Recursive feasibility and an a-priori upper bound to the closed-loop asymptotic average performance are ensured. Thanks to the use of dissipativity of the nominal system with a suitable supply rate, the closed-loop system under the proposed controller is shown to be asymptotically stable, in the sense that it is driven to an optimal robust invariant set.
Thirdly, for the purpose of combining robust EMPC design with a state observer in a single pure economic optimization problem, we consider homothetic tube-based EMPC synthesis for constrained linear discrete time systems. Since, in practical systems, full state measurement is seldom available, the proposed method integrates a moving horizon estimator to achieve closed-loop stability and constraint satisfaction despite system disturbances and output measurement noise. In contrast to existing approaches, the worst cost within a single homothetic tube around the solution of the associated nominal system is minimized, which at the same time tightens the bound on the set of potential states compatible with past output and input data. We show that the designed optimization problem is recursively feasible and adoption of homothetic tubes leads to less conservative economic performance bounds. In addition, the use of strict dissipativity of the nominal system guarantees asymptotic stability of the resulting closed-loop system.
Finally, to deal with the unknown nonzero mean disturbance and the presence of plant-model error, we propose a novel economic MPC algorithm aiming at achieving optimal steady-state performance despite the presence of plant-model mismatch or unmeasured nonzero mean disturbances. According to the offset-free formulation, the system's state is augmented with disturbances and transformed into a new coordinate framework. Based on the new variables, the proposed controller integrates a moving horizon estimator to determine a solution of the nominal system surrounded by a set of potential states compatible with past input and output measurements. The worst cost within a single homothetic tube around the nominal solution is chosen as the economic objective function which is minimized to provide a tightened upper bound for the accumulated real cost within the prediction horizon window. Thanks to the combined use of the nominal system and homothetic tube, the designed optimization problem is recursively feasible and less conservative economic performance bounds are achieved.Open Acces
Trust-aware Safe Control for Autonomous Navigation: Estimation of System-to-human Trust for Trust-adaptive Control Barrier Functions
A trust-aware safe control system for autonomous navigation in the presence
of humans, specifically pedestrians, is presented. The system combines model
predictive control (MPC) with control barrier functions (CBFs) and trust
estimation to ensure safe and reliable navigation in complex environments.
Pedestrian trust values are computed based on features, extracted from camera
sensor images, such as mutual eye contact and smartphone usage. These trust
values are integrated into the MPC controller's CBF constraints, allowing the
autonomous vehicle to make informed decisions considering pedestrian behavior.
Simulations conducted in the CARLA driving simulator demonstrate the
feasibility and effectiveness of the proposed system, showcasing more
conservative behaviour around inattentive pedestrians and vice versa. The
results highlight the practicality of the system in real-world applications,
providing a promising approach to enhance the safety and reliability of
autonomous navigation systems, especially self-driving vehicles
Intrinsic Separation Principles
This paper is about output-feedback control problems for general linear
systems in the presence of given state-, control-, disturbance-, and
measurement error constraints. Because the traditional separation theorem in
stochastic control is inapplicable to such constrained systems, a novel
information-theoretic framework is proposed. It leads to an intrinsic
separation principle that can be used to break the dual control problem for
constrained linear systems into a meta-learning problem that minimizes an
intrinsic information measure and a robust control problem that minimizes an
extrinsic risk measure. The theoretical results in this paper can be applied in
combination with modern polytopic computing methods in order to approximate a
large class of dual control problems by finite-dimensional convex optimization
problems
Advanced Discrete-Time Control Methods for Industrial Applications
This thesis focuses on developing advanced control methods for two industrial
systems in discrete-time aiming to enhance their performance in delivering the
control objectives as well as considering the practical aspects. The first part
addresses wind power dispatch into the electricity network using a battery
energy storage system (BESS). To manage the amount of energy sold to the
electricity market, a novel control scheme is developed based on discrete-time
model predictive control (MPC) to ensure the optimal operation of the BESS in
the presence of practical constraints. The control scheme follows a decision
policy to sell more energy at peak demand times and store it at off-peaks in
compliance with the Australian National Electricity Market rules. The
performance of the control system is assessed under different scenarios using
actual wind farm and electricity price data in simulation environment. The
second part considers the control of overhead crane systems for automatic
operation. To achieve high-speed load transportation with high-precision and
minimum load swings, a new modeling approach is developed based on independent
joint control strategy which considers actuators as the main plant. The
nonlinearities of overhead crane dynamics are treated as disturbances acting on
each actuator. The resulting model enables us to estimate the unknown
parameters of the system including coulomb friction constants. A novel load
swing control is also designed based on passivity-based control to suppress
load swings. Two discrete-time controllers are then developed based on MPC and
state feedback control to track reference trajectories along with a feedforward
control to compensate for disturbances using computed torque control and a
novel disturbance observer. The practical results on an experimental overhead
crane setup demonstrate the high performance of the designed control systems.Comment: PhD Thesis, 230 page
A metaheuristic particle swarm optimization approach to nonlinear model predictive control
This paper commences with a short review on
optimal control for nonlinear systems, emphasizing the Model
Predictive approach for this purpose. It then describes the Particle Swarm Optimization algorithm and how it could be applied
to nonlinear Model Predictive Control. On the basis of these
principles, two novel control approaches are proposed and anal-
ysed. One is based on optimization of a numerically linearized
perturbation model, whilst the other avoids the linearization step
altogether. The controllers are evaluated by simulation of an
inverted pendulum on a cart system. The results are compared
with a numerical linearization technique exploiting conventional
convex optimization methods instead of Particle Swarm Opti-
mization. In both approaches, the proposed Swarm Optimization
controllers exhibit superior performance. The methodology is
then extended to input constrained nonlinear systems, offering a
promising new paradigm for nonlinear optimal control design.peer-reviewe
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