Shangtai Jin

Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems

In this paper, a data-driven model-free adaptive control (MFAC) approach is proposed based on a new dynamic linearization technique (DLT) with a novel concept called pseudo-partial derivative for a class of general multiple-input and multiple-output nonlinear discrete-time systems. The DLT includes compact form dynamic linearization, partial form dynamic linearization, and full form dynamic linearization. The main feature of the approach is that the controller design depends only on the measured input/output data of the controlled plant. Analysis and extensive simulations have shown that MFAC guarantees the bounded-input bounded-output stability and the tracking error convergence.

A Data-Driven Iterative Feedback Tuning Approach of ALINEA for Freeway Traffic Ramp Metering With PARAMICS Simulations

In this work, a new iterative feedback tuning approach is proposed to tune ALINEAs controller gain automatically when there is not enough prior information available to select a proper feedback gain of ALINEA. It is a data-driven method and the ALINEA controller is auto-tuned only depending on the input and output data collected from closed-loop experiments. To mimic a real traffic environment, a simulator is built on the PARAMICS platform. The flow-based ALINEA controller is also considered to illustrate the good tuning performance of IFT comprehensively. The effectiveness of the proposed methods is verified through PARAMICS based simulations.

Discrete-time adaptive iterative learning control for permanent magnet linear motor

A discrete-time adaptive iterative learning control approach (DAILC) is presented for improving the permanent magnet linear motor velocity tracking performance. The learning gain can be updated iteratively along the learning axis and pointwisely along the time axis. When the initial states are random and the reference trajectory and disturbance are iteration-varying, the DAILC can achieve the pointwise convergence over a finite time interval asymptotically along the iterative learning axis. The theoretical analysis and simulation results further verify the effectiveness of the proposed approach.

Enhanced Data-Driven Optimal Terminal ILC Using Current Iteration Control Knowledge

In this paper, an enhanced data-driven optimal terminal iterative learning control (E-DDOTILC) is proposed for a class of nonlinear and nonaffine discrete-time systems. A dynamical linearization approach is first developed with iterative operation points to formulate the relationship of system output and input into a linear affine form. Then, an ILC law is constructed with a nonlinear learning gain, which is a function about the system partial derivative with respect to the time-varying control input. In addition, a parameter updating law is designed to estimate the unknown partial derivatives iteratively. The input signals of the proposed E-DDOTILC are time-varying and updated utilizing not only the terminal tracking error of the previous run but also the input signals of the previous time instants in the current iteration. The proposed approach is a data-driven control strategy and only the I/O data are required for the controller design and analysis. The monotonic convergence and effectiveness of the proposed approach is further verified by both the rigorous mathematical analysis and the simulation results.

A data-driven adaptive ILC for a class of nonlinear discrete-time systems with random initial states and iteration-varying target trajectory

Abstract This paper presents a new data-driven adaptive ILC (DDAILC) for a class of nonlinear discrete-time systems by introducing a pointwise dynamical linearization approach in the iteration direction. For the nonlinear systems, which cannot be linearly parameterized, the proposed DDAILC is capable of achieving a perfect performance without requiring any identical conditions exposed both on the initial state and on the reference trajectory. It is a data-driven control approach since only the I/O data is required for the control system design and analysis. The parameter updating law is constructed to estimate the inverse values of the system׳s unknown partial derivatives of the nonlinear system with respect to the control inputs, which are utilized to compute the learning gain of control law further. The control input is updated by using both the information of reference trajectory of the current operation as a feedback term and the input signals in previous operations as a feedforward term. Extension results to MIMO nonlinear discrete-time systems are provided further. Both theoretical analysis and simulation results verify the effectiveness of the proposed data-driven AILC approach.

A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems

A data-driven predictive terminal iterative learning control (DDPTILC) approach is proposed for discrete-time nonlinear systems with terminal tracking tasks, where only the terminal output tracking error instead of entire output trajectory tracking error is available. The proposed DDPTILC scheme consists of an iterative learning control law, an iterative parameter estimation law, and an iterative parameter prediction law. If the partial derivative of the controlled system with respect to control input is bounded, then the proposed control approach guarantees the terminal tracking error convergence. Furthermore, the control performance is improved by using more information of predictive terminal outputs, which are predicted along the iteration axis and used to update the control law and estimation law. Rigorous analysis shows the monotonic convergence and bounded input and bounded output (BIBO) stability of the DDPTILC. In addition, extensive simulations are provided to show the applicability and effectiveness of the proposed approach.

Model Free Adaptive Control for automatic car parking systems

In this paper, a Model Free Adaptive Control (MFAC) scheme is proposed for automatic car parking systems. The scheme consists of a control algorithm, a parameter estimation algorithm and a parameter reset algorithm. The design of the proposed scheme only uses the input and output (I/O) data, and does not involve any model information of the controlled car. Therefore, the MFAC based automatic parking system is applicable for different kinds of car. The simulation comparisons among MFAC scheme and PID control scheme are given for different kinds of car with different parking speed. The simulation results show that the proposed MFAC scheme has smaller tracking errors in the orientation angle of the car, the x axis and y axis.

A Data-Driven Control Design Approach for Freeway Traffic Ramp Metering with Virtual Reference Feedback Tuning

ALINEA is a simple, efficient, and easily implemented ramp metering strategy. Virtual reference feedback tuning (VRFT) is most suitable for many practical systems since it is a “one-shot” data-driven control design methodology. This paper presents an application of VRFT to a ramp metering problem of freeway traffic system. When there is not enough prior knowledge of the controlled system to select a proper parameter of ALINEA, the VRFT approach is used to optimize the ALINEAs parameter by only using a batch of input and output data collected from the freeway traffic system. The extensive simulations are built on both the macroscopic MATLAB platform and the microscopic PARAMICS platform to show the effectiveness and applicability of the proposed data-driven controller tuning approach.

Model-Free based Optimal Iterative Learning Control for a Class of Discrete-Time Nonlinear Systems

A pseudo-partial-derivative based dynamic linearization method is introduced, the method can transform general discrete-time nonlinear model into discrete-time time-varying linear model. Based on this discrete-time time-varying linear model, a novel norm-optimal iterative learning control (NOILC), called model-free based norm-optimal iterative learning control (MFNOILC), is proposed for a class of discrete-time nonlinear systems. Through rigorous analysis, the convergence of the proposed algorithm is proved. The simulation results show the effectiveness of the algorithm.

Terminal ILC design and analysis via a dynamical predictive model

Terminal iterative learning control (TILC) has been developed to track a single desired point at the terminal end of operation interval over iterations. In this paper, the feedback control knowledge of previous time instants is utilized via an equivalent dynamical predictive model to update the input signals for the TILC problem. The proposed scheme consists of a control input updating law with feedback information and a parameter updating law together. The new approach is a data-driven control strategy where the controller design and analysis requires only the measurement I/O data without using any model information of the plant. The effectiveness of the proposed approach is guaranteed by rigorous analysis.