Xiaoyin Shao

Bio: Xiaoyin Shao is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Motion control & Control system. The author has an hindex of 8, co-authored 10 publications receiving 440 citations.

A Model-Free Cross-Coupled Control for Position Synchronization of Multi-Axis Motions: Theory and Experiments

Abstract: In this brief, a model-free cross-coupled controller is proposed for position synchronization of multi-axis motions. The position synchronization error of each axis is defined as the differential position error between this axis and its two adjacent axes, which is then coupled with the position error to form a coupled position error. A proportional and derivative (PD)-type synchronization controller with feedback of this coupled position error is proposed and proven to guarantee asymptotic convergence to zero of both position and synchronization errors in a setpoint position control. A setpoint tracking controller is further developed by adding feedforward control terms and a saturation function to the PD synchronization controller. The proposed method is easy to implement in practice since it is model free and the control gains are time-invariant. Experiments are performed to verify effectiveness of the proposed approach

Development of a New Robot Controller Architecture with FPGA-Based IC Design for Improved High-Speed Performance

Abstract: In this paper, a new robot controller architecture is proposed to implement various complex control algorithms for improved high-speed performance. The main thrust of the research is to remove the servo control loop from the digital signal processor (DSP) and implement the high-speed servo loop in a field programmable gate array (FPGA). The main objective of this architecture is to utilize the ultra-high-speed hardwired logic of the FPGA to enhance the overall computational capability and relieve the computational load of the DSP for other tasks. The control algorithm is partitioned into a linear portion and a nonlinear portion. The linear portion with position/velocity feedback represents the major control loop and is implemented in the FPGA. The nonlinear portion acts as dynamic compensation to the linear portion to calculate model-related control gains/parameters, and it is implemented in the DSP. In tandem, with the newly developed control hardware architecture, an FPGA-based motion control integrated circuit (IC) is designed. Experiments are conducted on an industrial robot manipulator to compare the closed-loop performance with this new control architecture and the traditional one, when the same control algorithm is used. The experimental results demonstrate that the proposed new control architecture exhibits much improved motion performance indeed, especially in high-speed motions.

Global Stability of a Saturated Nonlinear PID Controller for Robot Manipulators

Abstract: This paper presents a simple and global stable nonlinear proportional-integral-derivative (N-PID) controller incorporated with a new saturated function design. The new class of saturated function is derived from quasi-natural potential function to shape the position and velocity errors. The coordinate of the saturated function is either in joint space or in Cartesian space, and the function is adjustable by changing the function parameters. The new controller formulation differs from traditional SP-ID control in that a nonlinear velocity (D-) control term is added. The global asymptotic stability of the controlled system is analyzed. Experiments performed on a two-link robot manipulator demonstrate the improved performance of the proposed nonlinear PID controller.

Development of an FPGA-Based Motion Control ASIC for Robotic Manipulators

Abstract: In this paper, a fully digital motion controller is designed by using a new developed FPGA based ASIC. The FPGA based ASIC has functions of closed current loop control, closed position/velocity loop control, incremental encoder logic, PWM modulation, fault/brake logic, velocity estimator, host communication module, UART module and Delta-Sigma analog to digital converter. The hardware system executes quickly in dedicated parallel hardware without timing overhead penalty of a serial processor. The update rates of the current control loop and position/velocity control loop are 120 kHz and 20 kHz, respectively. The new designed ASIC can be incorporated with a general-purpose microcontroller or DSP to provide a simple, compact, low-cost, and effective solution for high-performance motion control. An adaptive control algorithm is implemented in the new designed system in order to control a SCARA robotic manipulator. In order to increase the sampling frequency of the system when using model based control algorithm, the control algorithm is partitioned into a linear portion and a nonlinear portion. The linear portion with position/velocity feedback represents the major control loop and is implemented in the FPGA based ASIC. The nonlinear portion acts as dynamic compensation to the linear portion to perform complex modeling related calculations, and is implemented in the DSP. Experimental results demonstrate that the new proposed system is successful and it exhibits much improved motion performance especially during high-speed motions.

FPGAs in Industrial Control Applications

Abstract: The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.

Distributed leaderless consensus algorithms for networked Euler–Lagrange systems

Abstract: This article proposes and analyses distributed, leaderless, model-independent consensus algorithms for networked Euler–Lagrange systems. We propose a fundamental consensus algorithm, a consensus algorithm accounting for actuator saturation, and a consensus algorithm accounting for unavailability of measurements of generalised coordinate derivatives, for systems modelled by Euler–Lagrange equations. Due to the fact that the closed-loop interconnected Euler–Lagrange equations using these algorithms are non-autonomous, Matrosov's theorem is used for convergence analysis. It is shown that consensus is reached on the generalised coordinates and their derivatives of the networked Euler–Lagrange systems as long as the undirected communication topology is connected. Simulation results show the effectiveness of the proposed algorithms.

Distributed Coordinated Tracking With a Dynamic Leader for Multiple Euler-Lagrange Systems

Abstract: In this note, we study a distributed coordinated tracking problem for multiple networked Euler-Lagrange systems. The objective is for a team of followers modeled by full-actuated Euler-Lagrange equations to track a dynamic leader whose vector of generalized coordinates is time varying under the constraints that the leader is a neighbor of only a subset of the followers and the followers have only local interaction. We consider two cases: i) The leader has a constant vector of generalized coordinate derivatives, and ii) The leader has a varying vector of generalized coordinate derivatives. In the first case, we propose a distributed continuous estimator and an adaptive control law to account for parametric uncertainties. In the second case, we propose a model-independent sliding mode control algorithm. Simulation results on multiple networked two-link revolute joint arms are provided to show the effectiveness of the proposed control algorithms.

Distributed Adaptive Tracking Control for Synchronization of Unknown Networked Lagrangian Systems

Abstract: This paper investigates the cooperative tracking control problem for a group of Lagrangian vehicle systems with directed communication graph topology. All the vehicles can have different dynamics. A design method for a distributed adaptive protocol is given which guarantees that all the networked systems synchronize to the motion of a target system. The dynamics of the networked systems, as well as the target system, are all assumed unknown. A neural network (NN) is used at each node to approximate the distributed dynamics. The resulting protocol consists of a simple decentralized proportional-plus-derivative term and a nonlinear term with distributed adaptive tuning laws at each node. The case with nonconstant NN approximation error is considered. There, a robust term is added to suppress the external disturbances and the approximation errors of the NNs. Simulation examples are included to demonstrate the effectiveness of the proposed algorithms.