Nobutaka Wada

Model Predictive Control for Linear Parameter Varying Systems Using Parameter Dependent Lyapunov Function

In this paper, we propose a method for synthesizing a model predictive control (MPC) law for linear parameter varying (LPV) systems. The proposed MPC algorithm is reduced to the optimization problem with linear matrix inequality (LMI) constraints that are derived by using the parameter dependent Lyapunov function (PDLF). The proposed control law achieves less conservative results as compared with the existing method. A numerical example is provided to illustrate effectiveness of the proposed method.

An LMI based scheduling algorithm for constrained stabilization problems

We propose a gain-scheduling control algorithm for locally stabilizing a discrete-time linear system with input saturation. The proposed control law has a structure that a high-gain control law and a low-gain control law are interpolated by using a single scheduling parameter. The scheduling parameter is computed on-line by solving a convex optimization problem with LMI constraints.

l/sub 2/-gain analysis of discrete-time systems with saturation nonlinearity using parameter dependent Lyapunov function

In this paper, we propose a method of estimating l/sub 2/-gain of discrete-time linear systems with input saturation. The proposed analysis condition is derived based on the parameter dependent Lyapunov function (PDLF), and it is reduced to a set of linear matrix inequalities (LMIs). We show that the condition is guaranteed to be less conservative than several existing conditions. A numerical example is provided to illustrate effectiveness of the proposed method.

PID gain tuning based on falsification using bandpass filters

A data-driven model-free design method of PID gains has been developed based on unfalsified control for a mixed sensitivity control problem. PID gains can be falsified effectively by this method if many sinusoidal responses are used. However, this requires many experiments. In this paper, we propose a method of generating such data that efficiently falsify PID gains by filtering the input output data with many bandpass filters. By this method, PID gains can be falsified effectively from a step response data of a closed-loop system

Unfalsified parameter space design of PID controllers for nonlinear plants

We present a parameter space design method of PID controllers for a mixed sensitivity control problem of nonlinear plants. The input output data are directly used for falsifying the PID gains without constructing the mathematical model. Falsified regions can be drawn for each input output data on the PID gain plane, and the unfalsified gains are used for control. The success of this method depends on how efficiently the gains can be falsified. We examine this problem by a numerical example of a plant with friction and by a cart positioning control experiment.

Model Predictive Tracking Control for Constrained Linear Systems Using Integrator Resets

In this technical note, we propose a model predictive tracking control algorithm for linear dynamical systems with input constraints. To achieve setpoint tracking, an integrator is inserted into the feedback loop. In the standard control strategy, integral action is used for all the time to remove steady state error. In the proposed control approach, the value of the integrator state is reset at each sampling time to improve tracking control performance until upper bound of the cost becomes sufficiently small. Then, the integral action is used to achieve offset-free tracking. The control algorithm is reduced to a convex optimization problem under linear matrix inequality constraints.

l/sub /spl infin// performance analysis of feedback systems with saturation nonlinearities: an approach based on polytopic representation

In this paper, we show a method of estimating l/sub /spl infin//-norm of an output signal of discrete-time feedback systems with saturation nonlinearities. The analysis condition for estimating l/sub /spl infin//-norm is derived based on the parameter dependent Lyapunov function (PDLF) and is reduced to a condition based on the linear matrix inequality (LMI) representation. Further, we show that the condition is guaranteed to be less conservative than the condition based on the standard sector condition. A numerical example is provided to illustrate effectiveness of the proposed method.

Tracking Control with Saturating Actuators: A Method Based on State-Dependent Gain-Scheduling and Reference Management

Abstract In this paper, we consider tracking control problems in the presence of actuator saturation. We first show a control law that internally stabilizes the closed-loop system and the tracking error converges to zero in the case where a reference signal is generated by a certain dynamics. The control law is based on the recently developed state dependent gain-scheduling algorithm and makes it possible to achieve large region of attraction and fast convergence of tracking error. Then we extend this result to the cases where the reference signal is an arbitrary time-varying signal.

A Scheduling Algorithm for Constrained Control Systems: An Approach Based on a Parameter Dependent Lyapunov Function

We propose a gain-scheduling control algorithm for locally stabilizing a discrete-time linear system with input saturation. The proposed control law has a structure that a high-gain control law and a low-gain control law are interpolated by using a single scheduling parameter. The closed-loop stability is guaranteed by using a parameter dependent Lyapunov function (PDLF). The scheduling parameter is computed on-line by solving a convex optimization problem with LMI constraints.

Path-following control of a mobile robot in the presence of actuator constraints

In this paper, we present a control law for a non-holonomic mobile robot that achieves path following. In the path-following problem, the objective is to control the angular velocity of the robot so that the robot tracks a given reference trajectory. In this paper, we propose a control law that achieves path following in the presence of a constraint on the angular velocity. By applying the proposed control law, the robot can track the reference trajectory even if the distance from the initial position of the robot and the reference trajectory is arbitrary large. Further, we extend the control law so that the linear velocity of the robot becomes small when the robot passes through corners. By using the control algorithm, we can prevent the angular velocity of the robot becoming extremely large when the robot passes through corners. Numerical examples are provided to illustrate the effectiveness of the proposed methods.