Jianchang Liu

Consensus stabilization of stochastic multi-agent system with Markovian switching topologies and stochastic communication noise ☆

In this paper, we study stochastic consensus stabilization problems for a stochastic high-order multi-agent system with Markovian switching topologies and stochastic communication noise. By using the generalized Ito^ formula for the Markovian switching multi-agent system with stochastic communication noise, a state-feedback controller is constructed to ensure that the stochastic high-order multi-agent system reaches consensus in mean square sense when each agents dynamics has unstable open-loop poles. A necessary and sufficient condition for the stochastic mean square consensus stabilization of the stochastic multi-agent system subject to Markovian switching topologies and additive disturbance is established under a distributed control protocol, i.e., the digraph is balanced and the union of the communication topology set contains a spanning tree. Numerical simulation is presented to demonstrate the theoretical analysis.

Robust consensus algorithm for multi-agent systems with exogenous disturbances under convergence conditions

In this paper, a robust consensus algorithm is developed and sufficient conditions for convergence to consensus are proposed for a multi-agent system (MAS) with exogenous disturbances subject to partial information. By utilizing H∞ robust control, differential game theory and a design-based approach, the consensus problem of the MAS with exogenous bounded interference is resolved and the disturbances are restrained, simultaneously. Attention is focused on designing an H∞ robust controller (the robust consensus algorithm) based on minimisation of our proposed rational and individual cost functions according to goals of the MAS. Furthermore, sufficient conditions for convergence of the robust consensus algorithm are given. An example is employed to demonstrate that our results are effective and more capable to restrain exogenous disturbances than the existing literature.

R2-M0PS0: A multi-objective particle swarm optimizer based on R2-indicator and decomposition

This paper proposes a general multi-objective particle swarm optimizer based on R2-indicator and decomposition (called R2-MOPSO) to deal with multi-objective optimization problems and then to solve many-objective optimization problems. R2-MOPSO makes use of the R2 contribution of the archived solutions to select global best leaders and update the swarm. R2-MOPSO uses decomposition method for selecting the personal best leaders and updates them for each particle in the population. In order to enhance the diversity of the particles, elitist-learning strategy and gaussian learning strategy are used. Our proposed algorithm is evaluated adopting benchmark test problems and indicators reported in the specialized literature, comparing is results with respect to those obtained by the state-of-the-art multi-objective evolutionary algorithms. Our preliminary results indicate that our proposal is competitive with respect to state-of-the-art multi-objective evolutionary algorithms, being particularly suitable for solving multi-objective and many-objective optimization problems.

Consensusability of discrete-time multi-agent systems with one-step predictive output feedback

This article is devoted to consensus control problem for discrete-time higher-order multi-agent systems. A novel distributed control protocol with one-step predictive output feedback is proposed to improve dynamic performance for consensus convergence. It is shown that consensus is reached if and only if there exists a common control gain which simultaneously stabilizes N-1 systems in a special form, where N is the number of agents. A simulation is performed to illustrate the effectiveness of the theoretical results.

Research on continuous rolling process control system based on multi-agent

Continuous rolling of plate and strip is a typical complex, multi-variable and multi-objective system, which has shown the characteristics such as complication, large-scale and close coupling increasingly. Therefore, it has become more and more difficult to design complex industrial control systems. This paper describes a multi-agent control system for a strip rolling process. In order to realize the distributed and intelligent control of the system, after analyzing the characteristics and structure of this control system, the multi-agent theory was applied and then a control strategy was designed based on multi-agent interaction. Finally, the partition method, the coordination mechanism, and the model structure of the multi-Agent control system for the strip rolling process were analyzed. This paper concludes with a new control structure and system for strip rolling process, which helps to build up the generally integrated model, to eliminate the coupling among control systems, and to realize more intelligent control.

Improvement and application of Automatic Gauge Control system in hot strip rolling mills

Mill Modulus Control (MMC) is one of important methods on Automatic Gauge Control (AGC) systems of strip rolling mills. It would result in a slower adjusting speed of strip rolling gap to combine the former AGC system and MMC simply without changing the compensating coefficient of rolling efficiency, which was deduced in detail by some inferential reasoning formulas. A new compensating coefficient that could let the AGC system make a quick response to the entry thickness deviation was put forward, based on studying deeply related control theories and models. With the compensating coefficient of rolling efficiency being reworked, the AGC system got an improvement on quickening its responding speed, and strip thickness precision became better on the production line, too.

An Indicator and Decomposition Based Steady-State Evolutionary Algorithm for Many-Objective Optimization

An indicator based selection method is a major ingredient in the formulation of indicator based evolutionary multiobjective optimization algorithms. The existing classical indicator based selection methodologies have demonstrated an excellent performance to solve low-dimensional optimization problems. However, the indicator based evolutionary multiobjective optimization algorithms encounter enormous challenges in high-dimensional objective space. Our main purpose is to explore how to extend the indicator to handle many-objective optimization problems. After analyzing the indicator, the objective space partition strategy, and the decomposition method, we propose a steady-state evolutionary algorithm based on the indicator and the decomposition method, named, -MOEA/D, to obtain well-converged and well-distributed Pareto front. The main contribution of this paper contains two aspects. (1) The convergence and diversity for the indicator based selection are analyzed. Two improper selection situations will be properly solved via applying the decomposition method. (2) According to the position of a new individual in the steady-state evolutionary algorithm, two different objective space partition strategies and the corresponding selection methods are proposed. Extensive experiments are conducted on a variety of benchmark test problems, and the experimental results demonstrate that the proposed algorithm has competitive performance in comparison with several tailored algorithms for many-objective optimization.

Stochastic output-only state space modeling based on stable recursive canonical variate analysis

An adaptive recursive stochastic output-only state space modeling approach is developed to improve the accuracy of modeling time-varying processes. The exponential weighted moving average approach is adopted to update the covariance and cross-covariance of past and future observation vectors. A novel method for adjusting forgetting factors based on the concept of angle between subspaces is proposed. To ensure stability of the identified model, we propose a constrained weighted recursive least square approach and propose a stable recursive canonical variate analysis (SRCVA) method. The performance of the proposed method is illustrated with simulation of the Tennessee Eastman (TE) process. Simulation results indicate that the accuracy of proposed SRCVA modeling method is superior to that of stochastic output-only state space modeling with canonical variate analysis.

Consensusability of multi-agent systems via multi-order relative output derivative feedback

This article addressed the consensus control problem for continuous higher-order multi-agent systems with multiple outputs. A novel distributed control protocol with multi-order relative output derivative feedback is proposed to improve dynamic performance for consensus convergence and the highest order of derivative item is decided by relative degree of the agent systems. It is shown that consensus is reached if and only if there exists a common control gain which simultaneously stabilizes N-1 systems in a special form, where N is the number of agents. A simulation is performed to illustrate the effectiveness of the theoretical results.

Research on decoupling method of thickness and tension control in rolling process

In the process of rolling, the thickness control and tension control tend to influence each other. In order to reduce mutual interference between the thickness control and tension control of rolled piece, and further improve the control precision of the rolled piece thickness and tension, in view of the coupling of the thickness control and tension control system, We design a decoupling compensator using invariance principle, to realize the decoupling of the thickness and tension control system. And through theoretical derivation and simulation, the effect of the designed decoupling compensator is verified, realizing the decoupling of the thickness and tension control system, and reducing the mutual interference with each other.