Artificial gene circuits have been proposed to be embedded into microbial cells that function as switches, timers, oscillators, and the Boolean logic gates. Building more complex systems from these basic gene circuit components is one key advance for biologic circuit design and synthetic biology. However, the behavior of bioengineered gene circuits remains unstable and uncertain. In this study, a nonlinear stochastic system is proposed to model the biological systems with intrinsic parameter fluctuations and environmental molecular noise from the cellular context in the host cell. Based on evolutionary systems biology algorithm, the design parameters of target gene circuits can evolve to specific values in order to robustly track a desired biologic function in spite of intrinsic and environmental noise. The fitness function is selected to be inversely proportional to the tracking error so that the evolutionary biological circuit can achieve the optimal tracking mimicking the evolutionary process of a gene circuit. Finally, several design examples are given in silico with the Monte Carlo simulation to illustrate the design procedure and to confirm the robust performance of the proposed design method. The result shows that the designed gene circuits can robustly track desired behaviors with minimal errors even with nontrivial intrinsic and external noise.
Based on the steady-state analyses of the synergism and saturation system (S-system) model, a robust control method is proposed for biochemical networks via feedback and feedforward biochemical circuits. Two robust biochemical circuit design schemes are developed. One scheme is to improve the system's structural stability so as to tolerate larger kinetic parameter variations, whereas the other is to compensate for the kinetic parameter variations to eliminate their effects. In addition, a multi-objective biochemical circuit design scheme is introduced for both the robust design against kinetic parameter variations and a desired sensitivity design to eliminate the effect of external disturbance simultaneously. The proposed robust circuit design schemes will provide a systematic method with potential applications in synthetic circuit design for biotechnological purpose and drug design purpose. Recent advances in both metabolic and genetic engineering have made the robust biochemical circuit control approach feasible through the design and implementation of synthetic biological networks amenable to mathematical modeling and quantitative analysis. Finally, several examples including the robust circuit design of the tricarboxylic acid cycle are used in silico to illustrate the design procedure and to confirm the performance of the proposed design method.
Abstract-A robust tracking control design of robot systems including motor dynamics with parameter perturbation and external disturbance is proposed in this study via adaptive fuzzy cancellation technique. A minimax controller equipped with a fuzzy-based scheme is used to enhance the tracking performance in spite of system uncertainties and external disturbance. The design procedure is divided into three steps. At first, a linear nominal robotic control design is obtained via model reference tracking with desired eigenvalue assignment. Next, a fuzzy logic system is constructed and then tuned to eliminate the nonlinear uncertainties as possibly as it can to enhance the tracking robustness. Finally, a minimax control scheme is specified to optimally attenuate the worst-case effect of both the residue due to fuzzy cancellation and external disturbance to achieve a minimax tracking performance. In addition, an adaptive fuzzybased dynamic game theory is introduced to solve the minimax tracking problem. The proposed method is appropriate for the robust tracking design of robotic systems with large parameter perturbation and external disturbance. A simulation example of a two-link robotic manipulator driven by dc motors is also given to demonstrate the effectiveness of proposed design method's tracking performance.Index Terms-Fuzzy-based dynamic game theory, fuzzy cancellation, tracking enhancement, uncertain robot systems.
A nonlinear guidance law based on a fuzzy model is proposed for tactical missiles pursuing maneuvering targets in three-dimensional (3-D) space. In the proposed guidance scheme, the relative motion equations between the missile and target are first interpolated piecewise by Takagi-Sugeno linear fuzzy models. Then, a nonlinear fuzzy guidance law is designed to eliminate the effects of approximation error and external disturbances to achieve the desired goal. The linear matrix inequality (LMI) technique is then employed to treat this optimal guidance design in consideration of control constraints. Finally, the problem is further transformed into a standard eigenvalue problem so that it can be efficiently solved via a convex optimization algorithm, which is available from a numerical computation software.
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