490 research outputs found
MPC for tracking of piece-wise constant referente for constrained linear systems
16th IFAC World Congress. Praga (República Checa) 03/07/2005Model predictive control (MPC) is one of the few techniques which is able to handle with constraints on both state and input of the plant. The admissible evolution and asymptotically convergence of the closed loop system is ensured by means of a suitable choice of the terminal cost and terminal contraint. However, most of the existing results on MPC are designed for a regulation problem. If the desired steady state changes, the MPC controller must be redesigned to guarantee the feasibility of the optimization problem, the admissible evolution as well as the asymptotic stability. In this paper a novel formulation of the MPC is proposed to track varying references. This controller ensures the feasibility of the optimization problem, constraint satisfaction and asymptotic evolution of the system to any admissible steady-state. Hence, the proposed MPC controller ensures the offset free tracking of any sequence of piece-wise constant admissible set points. Moreover this controller requires the solution of a single QP at each sample time, it is not a switching controller and improves the performance of the closed loop system
Predictive Second Order Sliding Control of Constrained Linear Systems with Application to Automotive Control Systems
This paper presents a new predictive second order sliding controller (PSSC)
formulation for setpoint tracking of constrained linear systems. The PSSC
scheme is developed by combining the concepts of model predictive control (MPC)
and second order discrete sliding mode control. In order to guarantee the
feasibility of the PSSC during setpoint changes, a virtual reference variable
is added to the PSSC cost function to calculate the closest admissible set
point. The states of the system are then driven asymptotically to this
admissible setpoint by the control action of the PSSC. The performance of the
proposed PSSC is evaluated for an advanced automotive engine case study, where
a high fidelity physics-based model of a reactivity controlled compression
ignition (RCCI) engine is utilized to serve as the virtual test-bed for the
simulations. Considering the hard physical constraints on the RCCI engine
states and control inputs, simultaneous tracking of engine load and optimal
combustion phasing is a challenging objective to achieve. The simulation
results of testing the proposed PSSC on the high fidelity RCCI model show that
the developed predictive controller is able to track desired engine load and
combustion phasing setpoints, with minimum steady state error, and no
overshoot. Moreover, the simulation results confirm the robust tracking
performance of the PSSC during transient operations, in the presence of engine
cyclic variability.Comment: 6 pages, 5 figures, 2018 American Control Conferance (ACC), June
27-29, 2018, Milwaukee, WI, USA. [Accepted in Jan. 2018
Reference tracking stochastic model predictive control over unreliable channels and bounded control actions
A stochastic model predictive control framework over unreliable Bernoulli
communication channels, in the presence of unbounded process noise and under
bounded control inputs, is presented for tracking a reference signal. The data
losses in the control channel are compensated by a carefully designed
transmission protocol, and that of the sensor channel by a dropout compensator.
A class of saturated, disturbance feedback policies is proposed for control in
the presence of noisy dropout compensation. A reference governor is employed to
generate trackable reference trajectories and stability constraints are
employed to ensure mean-square boundedness of the reference tracking error. The
overall approach yields a computationally tractable quadratic program, which
can be iteratively solved online
Surviving disturbances: A predictive control framework with guaranteed safety
Rejecting all disturbances is an extravagant hope in safety-critical control systems, hence surviving them where possible is a sensible objective a controller can deliver. In order to build a theoretical framework starting from surviving all disturbances but taking the appropriate opportunity to reject them, a sufficient condition on surviving disturbances is first established by exploring the relation among steady sets of state, input, and disturbance, followed by an output reachability condition on rejecting disturbances. A new robust safety-critical model prediction control (MPC) framework is then developed by embedding the quartet of pseudo steady input, output, state, and disturbance (IOSD) into the optimisation. Unlike most existing tracking MPC setups, a new and unique formulation is adopted by taking the pseudo steady disturbance as an optimisation decision variable, rather than directly driven by the disturbance estimate. This new setup is able to decouple estimation error dynamics, significantly contributing to the guarantee of recursive feasibility, even if the disturbance or its estimate changes rapidly. Moreover, towards optimal coexistence with disturbances, offset-free tracking of a compromised reference can be achieved, if rejecting the disturbance conflicts with safety-critical specifications. Finally, the benefits of the proposed method have been demonstrated by both numerical simulations and experiments on aerial physical interaction
Fixed-Point Constrained Model Predictive Control of Spacecraft Attitude
The paper develops a Model Predictive Controller for constrained control of
spacecraft attitude with reaction wheel actuators. The controller exploits a
special formulation of the cost with the reference governor like term, a low
complexity addition of integral action to guarantee offset-free tracking of
attitude set points, and an online optimization algorithm for the solution of
the Quadratic Programming problem which is tailored to run in fixed-point
arithmetic. Simulations on a nonlinear spacecraft model demonstrate that the
MPC controller achieves good tracking performance while satisfying reaction
wheel torque constraints. The controller also has low computational complexity
and is suitable for implementation in spacecrafts with fixed-point processors
Modular model predictive control upon an existing controller
The availability of predictions of future system inputs has motivated research into preview control to improve set-point tracking and disturbance rejection beyond that achievable via conventional feedback control. The design of preview controllers, typically based upon model predictive control (MPC) for its constraint handling properties, is often performed in a monolithic nature, coupling the feedback and feed-forward problems. This can create problems, such as: (i) an additional feedback loop is introduced by MPC, which alters the closed-loop dynamics of the existing feedback compensator, potentially resulting in a deterioration of the nominal sensitivities and robustness properties of an existing closed-loop and (ii) the default preview action from MPC can be poor, degrading the original feedback control performance. In our previous work, the former problem is addressed by presenting a modular MPC design on top of a given output-feedback controller, which retains the nominal closed-loop robustness and frequency-domain properties of the latter, despite the addition of the preview design. In this paper, we address the second problem; the preview compensator design in the modular MPC formulation. Specifically, we derive the key conditions that ensure, under a given closed-loop tuning, the preview compensator within the modular MPC formulation is systematic and well-designed in a sense that the preview control actions complement the existing feedback control law rather than opposing it. In addition, we also derive some important results, showing that the modular MPC can be implemented in a cascade over any given linear controllers and the proposed conditions hold, regardless of the observer design for the modular MPC. The key benefit of the modular MPC is that the preview control with constraint handling can be implemented without replacing the existing feedback controller. This is illustrated through some numerical examples
Nonlinear MPC for Tracking for a Class of Non-Convex Admissible Output Sets
This paper presents an extension to the nonlinear Model Predictive Control
for Tracking scheme able to guarantee convergence even in cases of non-convex
output admissible sets. This is achieved by incorporating a convexifying
homeomorphism in the optimization problem, allowing it to be solved in the
convex space. A novel class of non-convex sets is also defined for which a
systematic procedure to construct a convexifying homeomorphism is provided.
This homeomorphism is then embedded in the Model Predictive Control
optimization problem in such a way that the homeomorphism is no longer required
in closed form. Finally, the effectiveness of the proposed method is showcased
through an illustrative example
Set-Point Tracking MPC with Avoidance Features
This work proposes a finite-horizon optimal control strategy to solve the
tracking problem while providing avoidance features to the closed-loop system.
Inspired by the set-point tracking model predictive control (MPC) framework,
the central idea of including artificial variables into the optimal control
problem is considered. This approach allows us to add avoidance features into
the set-point tracking MPC strategy without losing the properties of an
enlarged domain of attraction and feasibility insurances in the face of any
changing reference. Besides, the artificial variables are considered together
with an avoidance cost functional to establish the basis of the strategy,
maintaining the recursive feasibility property in the presence of a previously
unknown number of regions to be avoided. It is shown that the closed-loop
system is recursively feasible and input-to-state-stable under the mild
assumption that the avoidance cost is uniformly bounded over time. Finally, two
numerical examples illustrate the controller behavior
Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics
Stability and safety are critical properties for successful deployment of
automatic control systems. As a motivating example, consider autonomous mobile
robot navigation in a complex environment. A control design that generalizes to
different operational conditions requires a model of the system dynamics,
robustness to modeling errors, and satisfaction of safety \NEWZL{constraints},
such as collision avoidance. This paper develops a neural ordinary differential
equation network to learn the dynamics of a Hamiltonian system from trajectory
data. The learned Hamiltonian model is used to synthesize an energy-shaping
passivity-based controller and analyze its \emph{robustness} to uncertainty in
the learned model and its \emph{safety} with respect to constraints imposed by
the environment. Given a desired reference path for the system, we extend our
design using a virtual reference governor to achieve tracking control. The
governor state serves as a regulation point that moves along the reference path
adaptively, balancing the system energy level, model uncertainty bounds, and
distance to safety violation to guarantee robustness and safety. Our
Hamiltonian dynamics learning and tracking control techniques are demonstrated
on \Revised{simulated hexarotor and quadrotor robots} navigating in cluttered
3D environments
- …