Chih-Cheng Kao

Modified PSO Method for Robust Control of 3RPS Parallel Manipulators

We propose an effective method to design a modified particle swarm optimization (MPSO) singularity control method for a fully parallel robot manipulator. By adopting MPSO to obtain simple and effective estimated damping values, the result automatically adjusts the damping value around a singular point and greatly improves the accuracy of system responses. This method works by damping accelerations of the end effector, so that accelerations along the degenerated directions are zero at a singular point. These velocities, however, may not be zero in some situations, in which case, fluctuations will be encountered around a singular point. To overcome this drawback, we propose a control scheme that uses both damped acceleration and damped velocity, called the hybrid damped resolved-acceleration control (HDRAC) scheme. The MPSO optimization method can immediately provide optimal damping factors when used in an online application. Our proposed approach offers such superior features as easy implementation, stable convergence characteristics, and good computational efficiency. The main advantage of the HDRAC with MPSO in the 3RPS parallel manipulator control system is that it is not necessary for the system to plan its path for avoiding the singular point; thus, the workspace can be improved. Illustrative examples are provided to show the effectiveness of this HDRAC in practical applications, and experimental results verifying the utility of the proposed control scheme are presented.

Photovoltaic Energy Conversion System Fault Detection Using Fractional-Order Color Relation Classifier in Microdistribution Systems

In this paper, we propose a photovoltaic (PV) energy conversion system (PVECS) fault detection scheme using a fractional-order color relation classifier in microdistribution systems. Based on electrical examination method, output power degradation is used to monitor physical conditions with changes in a PV array’s circuitry, including grounded faults, mismatch faults, bridged faults between two PV panels, and open-circuit faults. The PV array power depends on solar radiation and temperature, and maximum power point tracking (MPPT) control is used to maintain stable power supply to a microdistribution system in the event of a fault in the PVECS. The MPPT algorithm is employed to estimate the desired maximum power, which is then compared with the meter-read power. Fractional-order dynamic errors are determined to quantify output power degradation between the desired maximum power and the meter-read power. Then, a color relation analysis is used to separate normal conditions from fault events. For a PVECS with two panels in parallel, the simulation results demonstrate that the proposed method is suitable for real-time applications and is flexible for fault identification. Its detection rates exceeded 88.23% for six events.

Singularity robustness of the 3RPS parallel manipulator by using the damped-rate resolved-acceleration control

A novel singularity control method for a fully parallel robot manipulator was proposed in this paper. The damped least-square method has frequently been used to solve the singularity problem of resolved-acceleration control schemes. It works by damping accelerations of the end-effector, so that accelerations in the degenerated directions are zero at a singular point. However, its velocities may not be zero in some situations, in those cases they will encounter fluctuations around the singular point. In this paper, the control using damped velocity, being called the damped-rate resolved-acceleration control scheme (DRRAC) is proposed to overcome this drawback. We will show that the DRRAC is asymptotically stable, and discuss its convergent properties. The main advantage of the DRRAC in the 3RPS parallel manipulator control system is not to plan its path to avoid the singular point, and could improve the workspace. Illustrative examples are given to show its effectiveness in practical applications, and experimental results are taken to verify the proposed control scheme.

Applying Modified MIMO DIVSC to Synchronism and Tension Control of Dual Motor Systems

In industrial manufacturing systems, one or more client motors are often arranged to follow the velocity of the master motor. The speed difference between these motors will affect the tension of the product. Hence, the synchronized control and the tension control are the important research topics in the manufacturing process. This paper proposes a novel modified multi-input-multi-output (MIMO) discrete integral variable structure control (DIVSC) for the synchronism and tension control of dual motor systems. By the Grey model theory, the unknown model of tension control can be established on-line to be a pseudo-Grey model. Based on these pseudo-Grey models, the model-based MIMO DIVSC can proceed with the design procedures. Finally, a prototype of the dual motor system is developed for testing the performance of the proposed control scheme. Experimental results will show the robust potential and the practicability of the proposed controller.

Identification of Flat Panel Glass-Handling Mechanism

The dynamic equations of a flat panel glass-handling robot driven by a permanent magnet synchronous motor (PMSM) are derived by use of Hamilton’s principle for the rigid and flexible models. In this paper, we adopt a new modified particle swarm optimization (MPSO) to identify all the parameters of the robot and PMSM simultaneously. This new algorithm is added with a “distance” term in the traditional PSO’s fitness function to avoid converging to a local optimum. It is found that the MPSO method can obtain optimal high-quality solutions with high calculation efficiency. In this study, the convergence characteristics of the real-coded genetic algorithm (RGA), PSO and MPSO are compared to demonstrate that the MPSO is superior to the others on identification of the dynamic flexible model.

Optimal Design and Control of a Slider-Crank Mechanism System

This paper mainly proposes an efficient modified particle swarm optimization (MPSO) method, to identify a slider-crank mechanism driven by a field-oriented PM synchronous motor. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In system identification, we adopt the MPSO method to find parameters of the slider-crank mechanism. This new algorithm is added with “distance” term in the traditional PSO’s fitness function to avoid converging to a local optimum. Finally, the comparisons of numerical simulations and experimental results prove that the MPSO identification method for the slider-crank mechanism is feasible.