FILTERED-DYNAMIC-INVERSION CONTROL FOR UNKNOWN MINIMUM-PHASE SYSTEMS WITH UNKNOWN RELATIVE DEGREE

We present filtered-dynamic-inversion (FDI) control for unknown linear time-invariant systems that are multi-input multi-output and minimum phase with unknown-but-bounded relative degree. This FDI controller requires limited model information, specifically, knowledge of an upper bound on the relative degree and knowledge of the first nonzero Markov parameter. The FDI controller is a single-parameter high-parameter-stabilizing controller that is robust to uncertainty in the relative degree. We characterize the stability of the closed-loop system. We present numerical examples, where the FDI controller is implemented in feedback with mathematical and physical systems. The numerical examples demonstrate that the FDI controller for unknown relative degree is effective for stabilization, command following, and disturbance rejection. We demonstrate that for a sufficiently large parameter, the average power of the closed-loop performance is arbitrarily small

Analysis and design of robust stabilizing modified repetitive control systems

In control system practice, high precision tracking or attenuation for periodic signals is an important issue. Repetitive control is known as an e.ective approach for such control problems. The internal model principle shows that the repetitive control system which contains a periodic generator in the closed-loop can achieve zero steady-state error for reference input or completely attenuate disturbance. Due to its simple structure and high control precision, repetitive control has been widely applied in many systems. To improve existing results on repetitive control theory, this thesis presents theoretical results in analysis and design repetitive control system. The main work and innovations are listed as follows: We propose a design method of robust stabilizing modi.ed repetitive controllers for multiple-input/multiple-output plants with uncertainties. The parameterization of all robust stabilizing modi.ed repetitive controllers for multiple-input/multiple-output plant with uncertainty is obtained by employing H∞ control theory based on the Riccati equation. The robust stabilizing controller contains free parameters that are designed to achieve desirable control characteristic. In addition, the bandwidth of low-pass .lter has been analyzed. In order to simplify the design process and avoid the wrong results obtained by graphical method, the robust stability conditions are converted to LMIs-constraint conditions by employing the delay-dependent bounded real lemma. When the free parameters of the parameterization of all robust stabiliz-ing controllers is adequately chosen, then the controller works as robust stabilizing modi.ed repetitive controller. For a time-varying periodic disturbances, we give an design method of an opti-mal robust stabilizing modi.ed repetitive controller for a strictly proper plant with time-varying uncertainties. A modi.ed repetitive controller with time-varying delay structure, inserted by a low-pass .lter and an adjustable parameter, is developed for this class of system. Two linear matrix inequalities LMIs-based robust stability con-ditions of the closed-loop system with time-varying state delay are derived for .xed parameters. One is a delay-dependent robust stability condition that is derived based on the free-weight matrix. The other robust stability condition is obtained based on the H∞ control problem by introducing a linear unitary operator. To obtain the desired controller, the design problems are converted to two LMI-constrained opti-mization problems by reformulating the LMIs given in the robust stability conditions. The validity of the proposed method is verified through a numerical example.学位記番号：工博甲46

Stabilization of systems with asynchronous sensors and controllers

We study the stabilization of networked control systems with asynchronous sensors and controllers. Offsets between the sensor and controller clocks are unknown and modeled as parametric uncertainty. First we consider multi-input linear systems and provide a sufficient condition for the existence of linear time-invariant controllers that are capable of stabilizing the closed-loop system for every clock offset in a given range of admissible values. For first-order systems, we next obtain the maximum length of the offset range for which the system can be stabilized by a single controller. Finally, this bound is compared with the offset bounds that would be allowed if we restricted our attention to static output feedback controllers.Comment: 32 pages, 6 figures. This paper was partially presented at the 2015 American Control Conference, July 1-3, 2015, the US

Input to State Stability of Bipedal Walking Robots: Application to DURUS

Bipedal robots are a prime example of systems which exhibit highly nonlinear dynamics, underactuation, and undergo complex dissipative impacts. This paper discusses methods used to overcome a wide variety of uncertainties, with the end result being stable bipedal walking. The principal contribution of this paper is to establish sufficiency conditions for yielding input to state stable (ISS) hybrid periodic orbits, i.e., stable walking gaits under model-based and phase-based uncertainties. In particular, it will be shown formally that exponential input to state stabilization (e-ISS) of the continuous dynamics, and hybrid invariance conditions are enough to realize stable walking in the 23-DOF bipedal robot DURUS. This main result will be supported through successful and sustained walking of the bipedal robot DURUS in a laboratory environment.Comment: 16 pages, 10 figure

Optimal and Robust Design of Integrated Control and Diagnostic Modules

The problem of designing an integrated control and diagnostic module is considered. The four degree of freedom controller is recast into a general framework wherein results from optimal and robust control theory can be easily implemented. For the case of an H2 objective, it is shown that the optimal control-diagnostic module involves constructing an optimal controller, closing the loop with this controller, and then designing an optimal diagnostic module for the closed loop. When uncertain plants are involved, this two-step method does not lead to reasonable diagnostics, and the control and diagnostic modules must be synthesized simultaneously. An example shows how this design can be accomplished with available methods