Design and allocation of power system stabilizers using the particle swarm optimization technique for an interconnected power system

The guaranteed-performance consensualization for high-order linear and nonlinear multiagent systems with switching topologies is respectively realized in a completely distributed manner in the sense that consensus design criteria are independent of interaction topologies and switching motions. This paper first proposes an adaptive consensus protocol with guaranteed-performance constraints and switching topologies, where interaction weights among neighboring agents are adaptively adjusted and state errors among all agents can be regulated. Then, a new translation-adaptive strategy is shown to realize completely distributed guaranteed-performance consensus control and an adaptive guaranteed-performance consensualization criterion is given on the basis of the Riccati inequality. Furthermore, an approach to regulate the consensus control gain and the guaranteed-performance cost is proposed in terms of linear matrix inequalities. Moreover, main conclusions for linear multiagent systems are extended to Lipschitz nonlinear cases. Finally, two numerical examples are provided to demonstrate theoretical results.

Completely Distributed Guaranteed-Performance Consensualization for High-Order Multiagent Systems With Switching Topologies

Predictive maintenance (PdM) is an effective means to eliminate potential failures, ensure stable equipment operation and improve the mission reliability of manufacturing systems and the quality of products, which is the premise of intelligent manufacturing. Therefore, an integrated PdM strategy considering product quality level and mission reliability state is proposed regarding the intelligent manufacturing philosophy of ‘prediction and manufacturing’. First, the key process variables are identified and integrated into the evaluation of the equipment degradation state. Second, the quality deviation index is defined to describe the quality of the product quantitatively according to the co-effect of manufacturing system component reliability and product quality in the quality–reliability chain. Third, to achieve changeable production task demands, mission reliability is defined to characterise the equipment production states comprehensively. The optimal integrated PdM strategy, which combines quality cont...

Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis

Optimal Control of a Biharmonic Obstacle Problem, D. Adams et al.- Minimal Thinness and Beurling's Minimum Principle, H. Aikawa.- Progress in the Problem of Lp-Contractivity of Semigroups for Partial Differential Operators, A. Cialdea.- Uniqueness and Nonuniqueness in Inverse Hyperbolic Problems and the Black Hole Phenomenon, G. Eskin.- Global Green's Function Estimates, M.W. Frazier, I.E. Verbitsky.- On Spectral Minimal Partitions: the Case of the Sphere, B. Helffer et al.- Weighted Sobolev Space Estimates for a Class of Singular Integral Operators, D. Mitrea et al.- On general Cwikel-Lieb-Rozenblum and Lieb-Thirring inequalities, S. Molchanov, B. Vainberg.- Estimates for the Counting Function of the Laplace Operator on Domains with Rough Boundaries, Y. Netrusov, Y. Safarov.- W2,p-Theory of the Poincare Problem, D.K. Palagachev.- Weighted Inequalities for Integral and Supremum Operators, L. Pick.- Finite Rank Toeplitz Operators in the Bergman Space, G. Rozenblum.- Resolvent Estimates for Non-Selfadjoint Operators via Semigroups, J. Sjoestrand.

Analysis and applications

The current paper addresses the distributed guaranteed-cost synchronization problems for general high-order linear multiagent networks. Existing works on the guaranteed-cost synchronization usually require all state information of neighboring agents and cannot give the cost budget previously. For both leaderless and leader-following interaction topologies, the current paper first proposes a dynamic output feedback synchronization protocol with guaranteed-cost constraints, which can realize the tradeoff design between the energy consumption and the synchronization regulation performance with the given cost budget. Then, according to different structure features of interaction topologies, leaderless and leader-following guaranteed-cost synchronization analysis and design criteria are presented, respectively, and an algorithm is proposed to deal with the impacts of nonlinear terms by using both synchronization analysis and design criteria. Especially, an explicit expression of the synchronization function is shown for leaderless cases, which is independent of protocol states and the given cost budget. Finally, numerical examples are presented to demonstrate theoretical results.

Dynamic Output Feedback Guaranteed-Cost Synchronization for Multiagent Networks With Given Cost Budgets

A limited-memory BFGS-based differential evolution algorithm for optimal control of nonlinear systems with mixed control variables and probability constraints

This paper considers an optimal feedback control problem for a class of fed-batch fermentation processes. Our main contributions are as follows. Firstly, a dynamic optimization problem for fed-batch fermentation processes is modeled as an optimal control problem of switched dynamical systems, and a general state-feedback controller is designed for this dynamic optimization problem. Unlike the existing switched dynamical system optimal control problem, the state-dependent switching method is applied to design the switching rule, and the structure of this state-feedback controller is not restricted to a particular form. Then, this problem is transformed into a mixed-integer optimal control problem by introducing a discrete-valued function. Furthermore, each of these discrete variables is represented by using a set of 0-1 variables. By using a quadratic constraint, these 0-1 variables are relaxed such that they are continuous on the closed interval $ [0, 1] $. Accordingly, the original mixed-integer optimal control problem is transformed intoa nonlinear parameter optimization problem. Unlike the existing works, the constraint introduced for these 0-1 variables are at most quadratic. Thus, it does not increase the number of locally optimal solutions of the original problem. Next, an improved gradient-based algorithm is developed based on a novel search approach, and a large number of numerical experiments show that this novel search approach can effectively improve the convergence speed of this algorithm, when an iteration is trapped to a curved narrow valley bottom of the objective function. Finally, numerical results illustrate the effectiveness of this method developed by this paper.

Optimal feedback control for a class of fed-batch fermentation processes using switched dynamical system approach

A gradient-based algorithm for non-smooth constrained optimization problems governed by discrete-time nonlinear equations with application to long-term hydrothermal optimal scheduling control

This paper considers an optimization problem of switched dynamical processes, which is essentially a mixed integer optimization problem. Unlike the existing optimization problem, the state-dependent switching strategy is adopted to design the switching rules. Our main contributions are as follows. Firstly, by introducing a discrete-valued function, a control vector parameterization technique, and a novel relaxation technique, the switched dynamical process optimization problem is approximated by using a more tractable nonlinear constrained parameter optimization problem. Unlike the existing works, the number of locally optimal solution does not increase due to the relaxation transformation. Next, in order to obtain a numerical solution of this parameter optimization problem, a novel penalty function approach is developed. Unlike existing penalty functions, this novel penalty function is neither quadratic nor linear, and has exponential convergence rates. Furthermore, a sensitivity analysis method is developed for investigating the effect of small perturbations in constraints and switching conditions on the objective function. Finally, a dynamic optimization problem in cancer chemotherapy process is presented to illustrate the effectiveness of the approach provided by this paper. Numerical simulation results show that the approach provided by this paper is low time-consuming, has faster convergence speed, can obtain a better objective function value than the existing methods, and is robust for the small perturbations in constraints and switching conditions. 

Sensitivity analysis for the optimization of switched dynamical processes with state-dependent switching conditions and its application

This paper considers an optimization problem of nonlinear dynamical processes with discrete-valued input and continuous-time inequality constraints. This problem requires to determine an order and some instants. Since the order is a discrete variable and the constraints are complex, classical optimal control approaches cannot be used directly to solve this problem. To tackle this issue, a penalty function-based greedy diffusion search algorithm is proposed based on a novel transformation for each discrete value, a novel second-order smoothing technique, a $ l_1 $ penalty function, and a novel greedy rule. In the novel greedy rule, there exist only two parameters, which can be set in advance. Thus, unlike existing hybrid approaches, no parameters is required adjusted in the penalty function-based greedy diffusion search algorithm. Following that, the global convergence results of the proposed algorithm are established. Finally, an energy management problem in an electrical vehicle is used to illustrate the effectiveness of the proposed algorithm. The numerical simulation results show that the algorithm proposed by this paper can obtain a better solution with less time-consuming, fewer iterations, and fewer errors compared with existing methods. 

A penalty function-based greedy diffusion search algorithm for the optimization of constrained nonlinear dynamical processes with discrete-valued input

Xiang Wu

Papers

A limited-memory BFGS-based differential evolution algorithm for optimal control of nonlinear systems with mixed control variables and probability constraints

Optimal feedback control for a class of fed-batch fermentation processes using switched dynamical system approach

A gradient-based algorithm for non-smooth constrained optimization problems governed by discrete-time nonlinear equations with application to long-term hydrothermal optimal scheduling control

Sensitivity analysis for the optimization of switched dynamical processes with state-dependent switching conditions and its application

A penalty function-based greedy diffusion search algorithm for the optimization of constrained nonlinear dynamical processes with discrete-valued input