Category: IEEE Transactions on Industrial Informatics

In this paper, a robust position tracking controller is proposed using an invariant dynamic surface approach in a cascade control system structure. There are two main contributions. The first is to design a dynamic surface giving the desired tracking-performance with a time-varying cutoff frequency automatically adjusted by the proposed auto-tuner, which can enhance the reliability of the resulting control system. The second is to derive the inner- and outer-loop control laws driving the closed-loop trajectories to the dynamic surface, incorporating a disturbance observer with the use of only nominal plant parameters. A reduction of plant parameter dependence can be accomplished by the second contribution. The realistic simulation results confirm the advantages of the proposed technique.

Most industrial robots are not capable of teaching by hand and require path points to be specified by teaching pendants. To enable the teaching of industrial robots by hand without any force sensors, this paper proposes a scheme to minimize the external force estimation error and reduce disturbance in the guiding task by using the virtual mass and virtual friction model. In this case, the maximum velocity and acceleration of the robot end effector shall be limited to ensure safety. Thus, the operator is allowed to guide the robot by hand. The joint torque is obtained from the motor current. The inertial force and friction of the links and driving systems are analyzed. The nonlinear dynamic model of the industrial robot is built and its parameters are calibrated by a nonlinear method. The force estimation is referenced to set the virtual friction and to design the force-following controller. Hence the end effector can follow the direction of external force compliantly and suppress jitters. Finally, several experiments on a six degrees of freedom industrial robot demonstrate the validity of the proposed control scheme.

Forecasting spot prices of electricity is challenging because it not only contains seasonal variations, but also random, abrupt spikes, which depend on market conditions and network contingencies. In this paper, a hybrid model has been developed to forecast the spot prices of electricity in two main stages. In the first stage, the prices are forecasted using autoregressive time varying (ARXTV) model with exogenous variables. To improve the forecasting ability of the ARXTV model, the price variations in the training process have been smoothened using the wavelet technique. In the second stage, a kernel regression is used to estimate the price spikes, which are detected using support vector machine based model. In addition, mutual information technique is employed to select appropriate input variables for the model. A case study is carried out with the aid of price data obtained from the Australian energy market operator. It is demonstrated that the proposed hybrid method can accurately forecast electricity prices containing spikes.