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This paper addresses the stability of a newly-developed control strategy for networked control systems (NCS). This control strategy hones the potential of constrained model predictive control (MPC) by buffering the predicted control sequence at the actuator in anticipation of typical data transmission errors associated with NCS. Closed-loop stability in the sense of Lyapunov is guaranteed for the controller in the linear case, by bounding the projected receding horizon costs by lower-and upper-bounding terms using a predetermined terminal cost. A stability theorem is developed, which provides a suboptimal measure for the controller in real time, and is sufficient to estimate the worst-case transmission delay that can be handled by the developed control buffering strategy. The stability conditions, as governed by the theorem, are validated through real-time implementation on an electro-hydraulic servo system of an industrial processing machine, through an Ethernet network.

A typical mechatronic problem (modeling, identification, and design) entails finding the best system topology as well as the associated parameter values. The solution requires concurrent and integrated methodologies and tools based on the latest theories. The experience on natural evolution of an engineering system indicates that the system topology evolves at a much slower rate than the parametric values. This paper proposes a two-loop evolutionary tool, using a hybrid of genetic algorithm (GA) and genetic programming (GP) for design optimization of a mechatronic system. Specifically, GP is used for topology optimization, while GA is responsible for finding the elite solution within each topology proposed by GP. A memory feature is incorporated with the GP process to avoid the generation of repeated topologies, a common drawback of GP topology exploration. The synergic integration of GA with GP, along with the memory feature, provides a powerful search ability, which has been integrated with bond graphs (BG) for mechatronic model exploration. The software developed using this approach provides a unified tool for concurrent, integrated, and autonomous topological realization of a mechatronic problem. It finds the best solution (topology and parameters) starting from an abstract statement of the problem. It is able to carry out the process of system configuration realization, which is normally performed by human experts. The performance of the software tool is validated by applying it to mechatronic design problems.

Particle Swarm optimization (PSO) is a robust stochastic evolutionary computation technique which is based on the movement and intelligence of swarms. In this paper the PSO algorithm is modified to improve its performance in a class of design applications in heat transfer. The developed approach includes a new term called a chaotic acceleration factor (Ca) into the algorithm, which enhances its convergence rate and its accuracy. The modified PSO is empirically tested with well-known benchmark functions. Next it is applied in plate-fin design with the objective of dissipating the maximum heat generation from an electronic component by minimizing the entropy generation rate to obtain the highest heat transfer efficiency.