82 research outputs found
Extremum Seeking-based Iterative Learning Linear MPC
In this work we study the problem of adaptive MPC for linear time-invariant
uncertain models. We assume linear models with parametric uncertainties, and
propose an iterative multi-variable extremum seeking (MES)-based learning MPC
algorithm to learn on-line the uncertain parameters and update the MPC model.
We show the effectiveness of this algorithm on a DC servo motor control
example.Comment: To appear at the IEEE MSC 201
Explicit model predictive control accuracy analysis
Model Predictive Control (MPC) can efficiently control constrained systems in
real-time applications. MPC feedback law for a linear system with linear
inequality constraints can be explicitly computed off-line, which results in an
off-line partition of the state space into non-overlapped convex regions, with
affine control laws associated to each region of the partition. An actual
implementation of this explicit MPC in low cost micro-controllers requires the
data to be "quantized", i.e. represented with a small number of memory bits. An
aggressive quantization decreases the number of bits and the controller
manufacturing costs, and may increase the speed of the controller, but reduces
accuracy of the control input computation. We derive upper bounds for the
absolute error in the control depending on the number of quantization bits and
system parameters. The bounds can be used to determine how many quantization
bits are needed in order to guarantee a specific level of accuracy in the
control input.Comment: 6 pages, 7 figures. Accepted to IEEE CDC 201
Tailored Presolve Techniques in Branch-and-Bound Method for Fast Mixed-Integer Optimal Control Applications
Mixed-integer model predictive control (MI-MPC) can be a powerful tool for
modeling hybrid control systems. In case of a linear-quadratic objective in
combination with linear or piecewise-linear system dynamics and inequality
constraints, MI-MPC needs to solve a mixed-integer quadratic program (MIQP) at
each sampling time step. This paper presents a collection of block-sparse
presolve techniques to efficiently remove decision variables, and to remove or
tighten inequality constraints, tailored to mixed-integer optimal control
problems (MIOCP). In addition, we describe a novel heuristic approach based on
an iterative presolve algorithm to compute a feasible but possibly suboptimal
MIQP solution. We present benchmarking results for a C code implementation of
the proposed BB-ASIPM solver, including a branch-and-bound (B&B) method with
the proposed tailored presolve techniques and an active-set based interior
point method (ASIPM), compared against multiple state-of-the-art MIQP solvers
on a case study of motion planning with obstacle avoidance constraints.
Finally, we demonstrate the computational performance of the BB-ASIPM solver on
the dSPACE Scalexio real-time embedded hardware using a second case study of
stabilization for an underactuated cart-pole with soft contacts.Comment: 27 pages, 7 figures, 2 tables, submitted to journal of Optimal
Control Applications and Method
Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning
We develop a real-time feasible mixed-integer programming-based decision
making (MIP-DM) system for automated driving. Using a linear vehicle model in a
road-aligned coordinate frame, the lane change constraints, collision avoidance
and traffic rules can be formulated as mixed-integer inequalities, resulting in
a mixed-integer quadratic program (MIQP). The proposed MIP-DM simultaneously
performs maneuver selection and trajectory generation by solving the MIQP at
each sampling time instant. While solving MIQPs in real time has been
considered intractable in the past, we show that our recently developed solver
BB-ASIPM is capable of solving MIP-DM problems on embedded hardware in real
time. The performance of this approach is illustrated in simulations in various
scenarios including merging points and traffic intersections, and
hardware-in-the-loop simulations on dSPACE Scalexio and MicroAutoBox-III.
Finally, we present results from hardware experiments on small-scale automated
vehicles.Comment: 14 pages, 11 figures, 3 tables, submitted to IEEE Transactions on
Control Systems Technolog
Control Over a Network: Using Actuation Buffers to Reduce Transmission Frequency
We consider a discrete time linear feedback control system with additive noise where the control signals are sent across a network from the controller to the actuators. Due to network considerations it is desired to reduce the transmission frequency of the control signals. We show that by including a finite sequence of predicted control signals in each communication packet the frequency of transmission can be reduced by transmitting only when the previously sent sequence has run out, although as a consequence the closed loop error will increase. We introduce a communication protocol, which we call Input Difference Transmission Scheme (IDTS), that transmits control packets when the difference between newly computed control values and the predicted control sequence previously transmitted is larger than a certain threshold. This threshold is a design parameter and we show how the closed loop behavior varies with this threshold. Simulation results are provided to augment the theory
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