Mohd Ashraf Ahmad

Performance analysis of model-free PID tuning of MIMO systems based on simultaneous perturbation stochastic approximation

Abstract This paper addresses the performance comparison of simultaneous perturbation stochastic approximation (SPSA) based methods for PID tuning of MIMO systems. Four typical SPSA based methods, which are one-measurement SPSA (1SPSA), two-measurement SPSA (2SPSA), Global SPSA (GSPSA) and Adaptive SPSA (ASPSA) are examined. Their performances are evaluated by extensive simulation for several controller design examples, in terms of the stability of the closed-loop system, tracking performance and computation time. In addition, the performance of the SPSA based methods are compared to the other stochastic optimization based approaches. It turns out that the GSPSA based algorithm is the most practical in terms of the stability and the tracking performance.

Composite Fuzzy Logic Control Approach to a Flexible Joint Manipulator

The raised complicatedness of the dynamics of a robot manipulator considering joint elasticity makes conventional model-based control strategies complex and hard to synthesize. This paper presents investigations into the development of hybrid intelligent control schemes for the trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, a collocated proportional-derivative (PD)-type Fuzzy Logic Controller (FLC) is first developed for the tip angular position control of a flexible joint manipulator. This is then extended to incorporate a non-collocated Fuzzy Logic Controller, a non-collocated proportional-integral-derivative (PID) and an input-shaping scheme for the vibration reduction of the flexible joint system. The positive zero-vibration-derivative-derivative (ZVDD) shaper is designed based on the properties of the system. The implementation results of the response of the flexible joint manipulator with the controllers are presented in time and frequency domains. The performances of the hybrid control schemes are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed.

Identification of continuous-time Hammerstein systems by simultaneous perturbation stochastic approximation

We propose an identification method for continuous-time Hammerstein models.The nonlinear subsystem approximation is based on piecewise affine function.We use simultaneous perturbation stochastic approximation algorithm.The proposed method has a good potential in identifying the Hammerstein models. This paper proposes an identification method for Hammerstein systems using simultaneous perturbation stochastic approximation (SPSA). Here, the structure of nonlinear subsystem is assumed to be unknown, while the structure of linear subsystem, such as the system order, is assumed to be available. The main advantage of the SPSA-based method is that it can be applied to identification of Hammerstein systems with less restrictive assumptions. In order to clarify this point, piecewise affine functions with a large number of parameters are adopted to approximate the unknown nonlinear subsystems. Furthermore, the linear subsystems are supposed to be described in continuous-time. Though this class of systems closely reflects the actual systems, there are few methods to identify such models. Hence, the SPSA-based method is utilized to identify the parameters in both linear and nonlinear subsystems simultaneously. The effectiveness of the proposed method is evaluated through several numerical examples. The results demonstrate that the proposed algorithm is useful to obtain accurate models, even for high-dimensional parameter identification.

Model Free Tuning of Variable State of Charge Target of Hybrid Electric Vehicles

Abstract This paper addresses a model free tuning method of the state of charge (SOC) target of a hybrid electric vehicle system. The methodology focuses on the variable SOC target which is governed by a linear equation of the actual vehicle speed. A simultaneous perturbation stochastic approximation (SPSA) based method is used to optimize the variable SOC target. The design method is applied to the JSAE-SICE benchmark problem which is developed using GT-SUITE of Gamma Technologies, Inc. and integrated with Simulink / MATLAB. Experimental results illustrate that the proposed controller can achieve almost 28 % improvements in fuel efficiency and keep 100 % drivers satisfaction.

Model-free wind farm control based on random search

This paper explores a model-free approach based on the random search (RS) algorithm for maximizing wind farms power production. The RS based approach is utilized to find the optimal control parameter of each turbine in maximizing the wind farm total power production. The Horns Rev wind farm model with turbulence interaction between turbines is used to validate the proposed approach. Simulation results demonstrate that the random search approach produces higher total power production as compared to the existing method.

A model-free PID tuning to slosh control using simultaneous perturbation stochastic approximation

This paper addresses an initial study of a model-free PID tuning based on simultaneous perturbation stochastic approximation (SPSA) for liquid slosh control. The SPSA method is used to optimize the PID parameters such that the liquid slosh is minimized. In order to validate our model-free design, a liquid slosh model is considered to represent the lateral slosh motion. The simulation results demonstrate that the proposed model-free method has a good potential in reducing the liquid slosh without explicitly modeling the liquid slosh behavior.

Using spiral dynamic algorithm for maximizing power production of wind farm

This paper presents a preliminary study of a model-free approach based on spiral dynamic algorithm (SDA) formaximizing wind farms power production. The SDA based approach is utilized to find the optimal control parameter of each turbine to maximize the total power production of a wind farm. For simplicity, a single row wind farm model with turbulence interaction between turbines is used to validate the proposed approach. Simulation results demonstrate that the SDA based method produces higher total power production compared to the particle swarm optimization (PSO) and game theoretic (GT) based approaches.

A data-driven sigmoid-based PI controller for buck-converter powered DC motor

This paper presents a novel data-driven sigmoid-based PI for tracking of angular velocity of dc motor powered by a dc/dc buck converter. A global simultaneous perturbation stochastic approximation (GSPSA) is employed to find the optimum sigmoid-based PI parameters such that the angular velocity error is minimized. The merit of the proposed approach is that it can produce fast PI parameter tuning without using any plant model by measuring the I/O data of the system. Moreover, the proposed PI parameters that are varied based on sigmoid function of angular velocity error has great potential in improving the control performance compared to the conventional PI controller. A well-known buck converter powered DC motor model is considered to validate our data-driven design. In addition, the performances of the proposed method are examined in terms of angular velocity trajectory tracking and duty cycle in comparison with other existing approaches. Numerical example shows that the data-driven sigmoid-based PI approach provides better control performances as compared to existing methods.

Evaluation of Mobile Phone Wireless Charging System Using Solar and Inductive Coupling

The wireless charging system now becomes one of the emerging technologies especially in the application of communication systems and beneficial to the wireless electronic appliances. Among them are mobile phones, cameras, personal digital assistance (PDA), cooler, torchlight and drill. Those wireless devices require battery to store and provide power before the device can be used. Hence, in order to solve the problem of short life of the battery of mobile phone, this project proposes adding a solar charging system base on inductive coupling method to the mobile phone to improve the usage of mobile phone in term of standby time, talk-time, online applications and power consumption especially in the remote area. Inductive coupling is among the effective method in wireless charging system to charge electronics device and reduce the constraint of the power cord or wired system. Meanwhile, solar cell is among the energy harvesting devices that is widely employed in many electronics application. The outcome of the project describes the comparison of the power consumption between the wire charging systems with solar-based wireless charging system. From the analysis of the results, solar-powered mobile phone with inductive coupling produced 21 h 46 min standby time after charging for 13 h 15 min compared to the existing charging system (wired system) which produce 17 h 5 min standby time after charging for 2 h 30 min. In addition, proposed system has high power consumption in term of standby time, talk-time and online application. Based on the results of the project, it could suggest that the wireless solar-powered mobile phone can replace the existing charging system in term of standby time.

Identification of continuous-time Hammerstein models using Simultaneous Perturbation Stochastic Approximation

This paper performs an initial study on identification of continuous-time Hammerstein models based on Simultaneous Perturbation Stochastic Approximation (SPSA). While the structure information such as the system order is available for the linear subsystems, the structure of nonlinear subsystem is assumed to be completely unknown. For handling it, a piecewise-linear functions are used as a tool to approximate the unknown nonlinear functions. The SPSA based method is then used to estimate the parameters in both the linear and nonlinear parts based on the given input and output data. A numerical example is given to illustrate that the SPSA based algorithm can give an accurate parameter estimation of the Hammerstein models with high probability through detailed simulation.