In this paper, we present a design method based on the concept of equivalent input disturbance (EID) to reject disturbances for a linear time-invariant system. A generalized state observer (GSO) is used to estimate an EID of the external disturbances, and the pole-assignment algorithm is employed to select the matrices of the GSO. Simulation and experimental results of a rotational speed control system demonstrate the validity of our method.

This paper relieves the ‘curse of dimensionality’ problem, which becomes intractable when scaling reinforcement learning to multi-agent systems. This problem is aggravated exponentially as the number of agents increases, resulting in large memory requirement and slowness in learning speed. For cooperative systems which widely exist in multi-agent systems, this paper proposes a new multi-agent Q-learning algorithm based on decomposing the joint state and joint action learning into two learning processes, which are learning individual action and the maximum value of the joint state approximately. The latter process considers others’ actions to insure that the joint action is optimal and supports the updating of the former one. The simulation results illustrate that the proposed algorithm can learn the optimal joint behavior with smaller memory and faster learning speed compared with friend-Q learning and independent learning.

An alternating least squares approach is developed in this paper to identify the exponential recovery dynamic load model of wide-area power systems. The nonlinear optimization problem is decomposed to two linear least squares problems, and solved in an alternating way. Then, a new algorithm for numerical derivative calculation using discrete Fourier transform is proposed to attenuate the effect of noises in the process of parameter estimation. Based on the estimated dynamic load characteristics, the application on voltage stability is analyzed. Finally, numerical and laboratory examples are conducted to demonstrate the effectiveness of the proposed methods.

The blending process as the first working procedure in lead–zinc sintering processes (LZSP) is a key step to guarantee the quality of products in the lead–zinc sintering production. This paper presents an intelligent integrated optimization system (IIOS) with a hierarchical configuration for raw material proportioning in the lead–zinc sintering blending process (LZSBP). First, considering the relationships between the mixture ratio and the production indices, back-propagation neural network (BPNN) models are established to predict the agglomerate compositions. Then, a raw material proportioning optimization strategy (RMPOS) is proposed to determine an optimal mixture ratio and actualize the optimization of the blending process. The proportioning optimization is implemented through qualitative and quantitative synthetic optimization for the primary proportioning, the zone optimization for the secondary proportioning, and the intelligent coordination between them. The practical running results demonstrate the validity of the proposed optimization strategies.

There are many kinds and a large number of raw materials in the sintering material ground to be managed, while it is difficult to obtain the precise inventory values, which often leads to high cost. Furthermore, the external factors of material ground are difficult to handle, such as weather variation, order fluctuation, measurement failure and so on. To solve such raw material management problems, a digital management system has been developed. First, the practical requirements and the raw material management processes are analyzed. Then, optimization and prediction methods are used to calculate the inventory according to the practical situation. With the help of practical technologies and production conditions, the developed system has been applied to a large-scale sintering material ground. The practical running results of the application demonstrate the validity of the proposed digital management system.

This paper presents a novel design method for discrete-time repetitive control systems (RCS) based on two-dimensional (2D) discrete-time model. Firstly, the 2D model of an RCS is established by considering both the control action and the learning action in RCS. Then, through constructing a 2D state feedback controller, the design problem of the RCS is converted to the design problem of a 2D system. Then, using 2D system theory and linear matrix inequality (LMI) method, stability criterion is derived for the system without and with uncertainties, respectively. Parameters of the system can be determined by solving the LMI of the stability criterion. Finally, numerical simulations validate the effectiveness of the proposed method.

This paper addresses the motion control problem of an n-link planar underactuated manipulator with any one of its joints being passive. A reduced order approach is proposed to achieve a swing-up control and a balancing control. The presented approach involves two stages. In Stage 1, (n− 2) control laws are first designed to force both the angles and angular velocities of (n− 2) actuated links to converge to zero, which guarantees that the n-link manipulator is reduced to like a 2-link one. Then the control law of the rest actuated link is temporarily set to constant to simplify the design of the control laws. In Stage 2, the states of the reduced system are guaranteed by maintaining the (n− 2) control laws designed in Stage 1. Meanw'hile, a swing-up control law and a balancing control law are designed for the rest actuated link. Integrated with the (n− 2) nonlinear control laws designed in Stage 1, the n-link underactuated manipulator is swung up quickly and balanced at the straight-up unstable equilibrium position effectively. Simulation results on a 4-link underactuated manipulator with different passive joint positions demonstrate the effectiveness of the proposed control approach.Highlights► A general n-link underactuated manipulator with single passive joint is concerned. ► Unified reduced order approach based on two stages is presented. ► The n-link underactuated manipulator is first reduced to be an acrobot or a pendubot. ► Quick swing-up control and effective balancing control are achieved.

This paper presents a disturbance rejection method for time-delay systems. The configuration of the control system is constructed based on the equivalent-input-disturbance (EID) approach. A modified state observer is applied to reconstruct the state of the time-delay plant. A disturbance estimator is designed to actively compensate for the disturbances. Under such a construction of the system, both matched and unmatched disturbances are rejected effectively without requiring any prior knowledge of the disturbance or inverse dynamics of the plant. The presentation of the closed-loop system is derived for the stability analysis and controller design. Simulation results demonstrate the validity and superiority of the proposed method.

This paper presents an integrated neural-network-based model for predicting the burn-through point (BTP) of a lead–zinc sintering process. This process features strong nonlinearity and time-varying parameters. First, experiments were carried out to establish a model of the gas temperature distribution (GTD) in the sintering machine; and based on the GTD model, a surface temperature model of the material (STMM) was established. Second, based on the STMM, a method of estimating the BTP that uses a soft-sensing technique was devised. In order to improve the estimation precision, a time-sequence-based model for predicting the BTP was built using grey system theory. Since the BTP is also affected by process parameters, a technological-parameter-based model for predicting the BTP was then built using a neural network. Finally, an integrated model for predicting the BTP was constructed by combining the time-sequence-based and the technological-parameter-based models using a fuzzy classifier. The result of actual runs shows that, compared to the manual control, the integrated prediction model reduced the variation in BTP by about 50%. This guarantees the improvement of the quality and quantity of the sinter.Highlights► We built a surface temperature model of the material (STMM). ► Based on the STMM, we devised a method of estimating the burn-through point (BTP). ► We built a time-sequence-based prediction model (TSBPM) using grey system theory. ► We also built a technological-parameter-based prediction model (TPBPM) using an NN. ► We then constructed an integrated model by combining the TSBPM and TPBPM using a fuzzy classifier.

This paper deals with the problem of passivity analysis for neural networks with both time-varying delay and norm-bounded parameter uncertainties by employing an improved free-weighting matrix approach. Some useful terms have been retained, which were used to be ignored in the derivative of Lyapunov–Krasovskii functional. Furthermore, the relationship among the time-varying delay, its upper bound and their difference is taken into account. As a result, for two types of time-varying delays, less conservative delay-dependent passivity conditions are obtained in terms of linear matrix inequalities (LMIs), respectively. Finally, a numerical example is given to demonstrate the effectiveness of the proposed techniques.

This paper investigates the problem of exponential synchronization for neural networks with mixed delays using sampled-data feedback control. Lyapunov–Krasovskii functional combining with the input delay approach as well as the improved free-weighting matrix approach are employed to derive several sufficient criteria ensuring the delayed neural networks to be exponentially synchronous. The conditions obtained are dependent not only on the maximum sampling interval but also on the exponential synchronization rate. A numerical example is given to demonstrate the usefulness and merits of the proposed scheme.

This paper presents a fuzzy-control method for the motion control of an acrobot. First, an explanation is given of the singularity that arises when a motion control law based on a Lyapunov function has an integrated control objective for energy and posture. Then, a fuzzy controller is designed that solves the singularity problem through regulation of a design parameter in the control law. Finally, an additional fuzzy controller is designed that improves the control performance through regulation of another design parameter in the control law. Simulation results demonstrate the effectiveness of this integrated fuzzy-control strategy.

This paper deals with the asymptotic stability problem of uncertain T-S fuzzy systems with time-varying delay by employing a further improved free-weighting matrix approach. The relationship among the time-varying delay, its upper bound and their difference is taken into account. As a result, some less conservative LMI-based delay-dependent stability criteria are obtained without ignoring any useful terms in the derivative of Lyapunov-Krasovskii functional. Finally, two numerical examples are given to demonstrate the effectiveness and the merits of the proposed methods.

This paper concerns stability analysis and controller design for repetitive control. First, a two-dimensional (2D) continuous-discrete hybrid model of a repetitive control system is established. Next, new criteria for the asymptotic stability of the system are presented based on the model. Then, these criteria are extended to calculate lower bounds on stability margins to design a suitable controller. Unlike existing methods, the one in this paper employs a 2D hybrid model to independently handle the two different types of actions involved in repetitive control: continuous control and discrete learning. A numerical example demonstrates that this approach provides good performance.

This paper studies the delay-dependent robust stability for time-delay systems with nonlinear perturbations. A new method is proposed to estimate the upper bound of the derivative of Lyapunov functional without ignoring some useful terms. Improved delay-dependent stability criteria are established by taking into account the range for the time-delay. In addition, augmented Lyapunov functionals are introduced to derive less conservative delay-dependent stability conditions. Finally, numerical examples are given to demonstrate the effectiveness and the merits of the proposed method.

This paper is concerned with the stability problem of uncertain T–S fuzzy systems with time-varying delay by employing a further improved free-weighting matrix approach. By taking the relationship among the time-varying delay, its upper bound and their difference into account, some less conservative LMI-based delay-dependent stability criteria are obtained without ignoring any useful terms in the derivative of Lyapunov–Krasovskii functional. Finally, two numerical examples are given to demonstrate the effectiveness and the merits of the proposed methods.