Model predictive control techniques for hybrid systems

This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581

Modeling and control of a dynamic information flow tracking system

This thesis introduces and details the effort of modeling and control design of an information tracking system for computer security purposes. It is called Dynamic Information Flow Tracking (DIFT) system. The DIFT system is developed at the Computer Science Department at the University of New Mexico, works by tagging data and tracking it to measure the information flow throughout the system. DIFT can be used for several security applications such as securing sensor networks and honeypot - which is a trap set to detect, deflect, or counteract attempts at unauthorized use of information systems. Existing DIFT systems cannot track address and control dependencies, therefore, their applicability is currently very limited because important information flow dependencies are not tracked for stability reasons. A new approach is taken, aimed at stabilizing DIFT systems and enabling it to detect control dependencies at the assembly-level, through control theory. Modern control has been used to model several cyber-physical, computing, networking, economical... systems. In an effort to model a computing system using control theory, this thesis introduces a general hybrid systems framework to model the flow of information in DIFT when control dependencies are encountered. Information flow in DIFT is represented by a numeric vector called taint vector . The model suggested benefits from the characteristics of hybrid systems and its ability to represent continuous variables and discrete events occurring. The system is stabilized by making sure that the taint vectors represent the true information flow in control dependencies. This problem is solved by designing a PID and model predictive controller which guarantee that system does not over taint, while allowing information to flow properly. The modeling framework is validated by comparing simulations of the hybrid models against. This research provides a new approach to solve the DIFT over-tainting problems through modeling it as a hybrid system and forcing the constraints to be obeyed by the taint values.\u2

Multiple Loop Self-Triggered Model Predictive Control for Network Scheduling and Control

We present an algorithm for controlling and scheduling multiple linear time-invariant processes on a shared bandwidth limited communication network using adaptive sampling intervals. The controller is centralized and computes at every sampling instant not only the new control command for a process, but also decides the time interval to wait until taking the next sample. The approach relies on model predictive control ideas, where the cost function penalizes the state and control effort as well as the time interval until the next sample is taken. The latter is introduced in order to generate an adaptive sampling scheme for the overall system such that the sampling time increases as the norm of the system state goes to zero. The paper presents a method for synthesizing such a predictive controller and gives explicit sufficient conditions for when it is stabilizing. Further explicit conditions are given which guarantee conflict free transmissions on the network. It is shown that the optimization problem may be solved off-line and that the controller can be implemented as a lookup table of state feedback gains. Simulation studies which compare the proposed algorithm to periodic sampling illustrate potential performance gains.Comment: Accepted for publication in IEEE Transactions on Control Systems Technolog

Imprecise dynamic walking with time-projection control

We present a new walking foot-placement controller based on 3LP, a 3D model of bipedal walking that is composed of three pendulums to simulate falling, swing and torso dynamics. Taking advantage of linear equations and closed-form solutions of the 3LP model, our proposed controller projects intermediate states of the biped back to the beginning of the phase for which a discrete LQR controller is designed. After the projection, a proper control policy is generated by this LQR controller and used at the intermediate time. This control paradigm reacts to disturbances immediately and includes rules to account for swing dynamics and leg-retraction. We apply it to a simulated Atlas robot in position-control, always commanded to perform in-place walking. The stance hip joint in our robot keeps the torso upright to let the robot naturally fall, and the swing hip joint tracks the desired footstep location. Combined with simple Center of Pressure (CoP) damping rules in the low-level controller, our foot-placement enables the robot to recover from strong pushes and produce periodic walking gaits when subject to persistent sources of disturbance, externally or internally. These gaits are imprecise, i.e., emergent from asymmetry sources rather than precisely imposing a desired velocity to the robot. Also in extreme conditions, restricting linearity assumptions of the 3LP model are often violated, but the system remains robust in our simulations. An extensive analysis of closed-loop eigenvalues, viable regions and sensitivity to push timings further demonstrate the strengths of our simple controller

Stabilizing control for power converters connected to transmission lines

This paper proposes a switching control strategy for the set-point stabilization of a power converter connected via a transmission line to a resistive load. The strategy employs a Lyapunov function that is directly based on energy considerations of the power converter, as well as of the transmission line described by the telegraph equations. The proposed stabilizing switching control still allows a certain freedom in the choice of the control law, a comparison between a maximum descent strategy and a minimum commutation strategy being discussed on a simple example.

A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control

This paper introduces a family of iterative algorithms for unconstrained nonlinear optimal control. We generalize the well-known iLQR algorithm to different multiple-shooting variants, combining advantages like straight-forward initialization and a closed-loop forward integration. All algorithms have similar computational complexity, i.e. linear complexity in the time horizon, and can be derived in the same computational framework. We compare the full-step variants of our algorithms and present several simulation examples, including a high-dimensional underactuated robot subject to contact switches. Simulation results show that our multiple-shooting algorithms can achieve faster convergence, better local contraction rates and much shorter runtimes than classical iLQR, which makes them a superior choice for nonlinear model predictive control applications.Comment: 8 page

A model predictive controller for robots to follow a virtual leader

SUMMARYIn this paper, we develop a model predictive control (MPC) scheme for robots to follow a virtual leader. The stability of this control scheme is guaranteed by adding a terminal state penalty to the cost function and a terminal state region to the optimization constraints. The terminal state region is found by analyzing the stability. Also a terminal state controller is defined for this control scheme. The terminal state controller is a virtual controller and is never used in the control process. Two virtual leader-following formation models are studied. Simulations on different formation patterns are provided to verify the proposed control strategy.</jats:p