Kok Kiong Tan

Precision motion control with disturbance observer for pulsewidth-modulated-driven permanent-magnet linear motors

In this paper, we address the problem of precision motion control of permanent-magnet linear motors (PMLMs) under the influence of significant disturbances. We establish a mathematical model of a PMLM driven by a sinusoidal pulsewidth-modulated (PWM) amplifier, obtaining it from a describing function analysis of the essentially nonlinear characteristics. The overall model (PWM+PMLM) inevitably inherits uncertainties in the face of load changes, system parameter perturbation, noise, and inherent system nonlinearities, etc., all of which constitute disturbances to the control system that will adversely affect the precision and accuracy. We propose a robust control scheme employing a disturbance observer to address the sensitivity of the control performance to the disturbances. Real-time experimental results are provided to verify and confirm the practical effectiveness of the proposed approach.

Adaptive friction compensation using neural network approximations

We present a new compensation technique for a friction model, which captures problematic friction effects such as Stribeck effects, hysteresis, stick-slip limit cycling, pre-sliding displacement and rising static friction. The proposed control utilizes a PD control structure and an adaptive estimate of the friction force. Specifically, a radial basis function (RBF) is used to compensate the effects of the unknown nonlinearly occurring Stribeck parameter in the friction model. The main analytical result is a stability theorem for the proposed compensator which can achieve regional stability of the closed-loop system. Furthermore, we show that the transient performance of the resulting adaptive system is analytically quantified. To support the theoretical concepts, we present dynamic simulations for the proposed control scheme.

Adaptive Friction Compensation With a Dynamical Friction Model

This paper presents a new compensation technique for dynamic friction. The proposed control utilizes a PD control structure and an adaptive estimation of the friction force based on an observer. Specifically, a nonlinear function is used to compensate the nonlinear effects of the parameters in the friction model. Simulations and experimental results verify the theory and show that the method can significantly improve the tracking performance of the motion control system.

A decentralized control of interconnected systems using neural networks

We develop a decentralized neural-network (NN) controller for a class of large-scale nonlinear systems with the high-order interconnections. The controller is a mixed NN comprised of a conventional NN and a special NN. The conventional NN is used to approximate the unknown nonlinearities in the subsystem, while a special NN is used to counter the high-order interconnections. We prove that this NN structure can achieve a stable controller for the large-scale systems.

A hybrid estimation of distribution algorithm for solving the multi-objective multiple traveling salesman problem

The multi-objective multiple traveling salesman problem (MmTSP) is a generalization of the classical multi-objective traveling salesman problem. In this paper, a formulation of the MmTSP, which considers the weighted sum of the total traveling costs of all salesmen and the highest traveling cost of any single salesman, is proposed. An estimation of distribution algorithm (EDA) based on restricted Boltzmann machine is used for solving the formulated problem. The EDA is developed in the decomposition framework of multi-objective optimization. Due to the limitation of EDAs in generating a wide range of solutions, the EDA is hybridized with the evolutionary gradient search. Simulation studies are carried out to examine the optimization performances of the proposed algorithm on MmTSP with different number of objective functions, salesmen and problem sizes.

Adaptive control of ram velocity for the injection moulding machine

This paper presents the design of an adaptive controller for the control of the ram velocity of an injection moulding machine, based on a physical nonlinear model which describes the filling phase in detail. Polynomial series are first used to simplify the mathematical model such that the system parameters and states are essentially separated. The errors arising from the approximation are reduced with the introduction of certain robustification terms into the control law representative of the residual effects. A sliding surface is subsequently defined and a self-tuning robust controller capable of achieving tight set-point regulation is proposed in which the control gains are tuned by an adaptive algorithm. The convergence property of the adaptive controller is proved. Finally, simulations are provided to evaluate the performance of the proposed control system.

Iterative reference adjustment for high-precision and repetitive motion control applications

A learning control scheme is proposed which is suitable for high-precision and repetitive motion control applications. It comprises of a self-tuning radial basis function (RBF) network operating in parallel with an iterative learning control (ILC) component. Unlike the usual ILC scheme which adapts a feedforward control signal to achieve improved tracking performance over time, the proposed scheme iteratively adjusts the reference signal. The RBF network is employed as a nonlinear function estimator to model the tracking error over a cycle, and this error model is subsequently used implicitly in the iterative adaptation of the reference signal over the next cycle. The ILC component further enhances the tracking performance, particularly over the sections of the trajectory where the RBF network is less adequate in its modeling function. Simulation examples and real-time experimental results are fully furnished to elaborate the various highlights of the proposed method.

A variable structure-augmented adaptive controller for a gyro-mirror line-of-sight stabilization platform

In this paper, we develop a variable structure-augmented adaptive controller applicable to a class of multivariable nonlinear servomechanisms commonly encountered in many industrial applications. For this proposed controller, it is shown in the paper that the proper integration of the adaptive and variable structure methods results in a controller where uniformly stable operation is achieved together with asymptotic tracking of the reference command signals. The proposed controller also assures robust operation in the presence of practical imperfections. In the paper, real-time experimental results in applying the proposed controller to a gyro-mirror line-of-sight stabilization platform are presented to demonstrate its effectiveness. These experiments also serve to verify the analytical results in a prototype real-time application.

Force ripple suppression in iron-core permanent magnet linear motors using an adaptive dither

This paper presents the design and realization of an adaptive dither to reduce the force ripple in an iron-core permanent magnet linear motor (PMLM). A composite control structure is used, consisting of three components: a simple feedforward component, a PID feedback component and an adaptive feedforward compensator (AFC). The first two components are designed based on a dominant linear model of the motor. The AFC generates a dither signal with the motivation to eliminate or suppress the inherent force ripple, thus facilitating smooth precise motion while uncompromising on the maximum force achievable. An analysis is given in the paper to show the parameter convergence. Computer simulations and real-time experimental results verify the effectiveness of the proposed scheme for high precision motion trajectory tracking using the PMLM.

A discrete-time iterative learning algorithm for linear time-varying systems

Abstract In this paper, an iterative learning algorithm (ILC) is presented for a MIMO linear time-varying system. We consider the convergence of the algorithm. A necessary and sufficient condition for the existence of convergent algorithm is stated. Then, we prove that the same condition is sufficient for the robustness of the proposed learning algorithm against state disturbance, output measurement noise, and reinitialization error. Finally, a simulation example is given to illustrate the results.