114 research outputs found
Stabilizing Stochastic Predictive Control under Bernoulli Dropouts
This article presents tractable and recursively feasible optimization-based
controllers for stochastic linear systems with bounded controls. The stochastic
noise in the plant is assumed to be additive, zero mean and fourth moment
bounded, and the control values transmitted over an erasure channel. Three
different transmission protocols are proposed having different requirements on
the storage and computational facilities available at the actuator. We optimize
a suitable stochastic cost function accounting for the effects of both the
stochastic noise and the packet dropouts over affine saturated disturbance
feedback policies. The proposed controllers ensure mean square boundedness of
the states in closed-loop for all positive values of control bounds and any
non-zero probability of successful transmission over a noisy control channel
Sparse and Constrained Stochastic Predictive Control for Networked Systems
This article presents a novel class of control policies for networked control
of Lyapunov-stable linear systems with bounded inputs. The control channel is
assumed to have i.i.d. Bernoulli packet dropouts and the system is assumed to
be affected by additive stochastic noise. Our proposed class of policies is
affine in the past dropouts and saturated values of the past disturbances. We
further consider a regularization term in a quadratic performance index to
promote sparsity in control. We demonstrate how to augment the underlying
optimization problem with a constant negative drift constraint to ensure
mean-square boundedness of the closed-loop states, yielding a convex quadratic
program to be solved periodically online. The states of the closed-loop plant
under the receding horizon implementation of the proposed class of policies are
mean square bounded for any positive bound on the control and any non-zero
probability of successful transmission
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
Stabilizing Gain Selection of Networked Variable Gain Controller to Maximize Robustness Using Particle Swarm Optimization
Networked Control Systems (NCSs) are often associated with problems like
random data losses which might lead to system instability. This paper proposes
a method based on the use of variable controller gains to achieve maximum
parametric robustness of the plant controlled over a network. Stability using
variable controller gains under data loss conditions is analyzed using a
suitable Linear Matrix Inequality (LMI) formulation. Also, a Particle Swarm
Optimization (PSO) based technique is used to maximize parametric robustness of
the plant.Comment: 6 pages, 6 figure
Output feedback stable stochastic predictive control with hard control constraints
We present a stochastic predictive controller for discrete time linear time
invariant systems under incomplete state information. Our approach is based on
a suitable choice of control policies, stability constraints, and employment of
a Kalman filter to estimate the states of the system from incomplete and
corrupt observations. We demonstrate that this approach yields a
computationally tractable problem that should be solved online periodically,
and that the resulting closed loop system is mean-square bounded for any
positive bound on the control actions. Our results allow one to tackle the
largest class of linear time invariant systems known to be amenable to
stochastic stabilization under bounded control actions via output feedback
stochastic predictive control
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