The arm-driven inverted pendulum is an unstable and nonlinear system. Therefore, the feedback controller designed by the linear control theory cannot be stabilized in the wide range. This paper discusses a nonlinear control design based on the optimal regulator problem. However, it is difficult to get the analysis solution for this problem. In this paper, DE (Differential Evolution) and PSO (Particle Swarm Optimization) methods are used to get an approximate solution. In addition to the first order feedback gain designed by the linear optimal regulator, we design the higher order gain. Finally, the validity of our nonlinear controller designed by DE is verified through some experiments.
This paper is concerned with a formation control problem of nonholonomic mobile robots with obstacle avoidance. It is known that the mobile robots can achieve an arbitrary formation asymptotically by using their virtual structures. However, in the presence of obstacles, the real robots may not be able to avoid them even if the corresponding virtual robots can avoid. In this paper, we remedy this difficulty by improving the virtual structure approach. To be more precise, each robot is applied a new formation control law which incorporates the potential function of the distance between the robot and obstacles, and takes trade-off between the formation maintenance and the obstacle avoidance by adaptive switching of the feedback gains. The effectiveness of the proposed control method is verified by numerical simulations.
In this paper, we develop a controller tuning method of the Smith compensator for multi input multi output (MIMO) linear time-invariant systems with output time delays. Particularly, we apply fictitious reference iterative tuning (FRIT), which is one of the controller tuning methods with only one-shot experimental data, to such systems. Since the Smith compensator contains a model of a plant, it is expected that the model is also obtained together with control parameters by FRIT. To realize this expectation, we introduce a special structure of the Smith compensator with tunable parameters. Then, we also show that the application of FRIT to the parameterized Smith compensator yields not only a desired control parameter but also an appropriate mathematical model of a plant. To show the validity of the proposed method, we give an illustrative numerical example.
This paper proposes a optimum pacing system for manufacturing process in which DBR parameters in theory of constraints(TOC) can be optimized by multi-objective optimization. In this system,the manufacturing process and DBR system are modeled by Petri net and they are used repeatedly with optimization technique. Conventionally, the DBR parameters have been decided empirically but are not necessarily optimum. The optimization method has not yet been established for the large scale and complex process containing the multiple bottleneck process. The optimal pacing method is characterized by the extending of DBR function and the parameters optimization for the cycle which is constructed in the manufacturing, the demand forecasting and the market. In the objective functions, the supply delay and the transition time to steady state manufacturing are evaluated and they are minimized. As the results, it is confirmed that the optimal pacing to enable the quick product supply to the market and the stable and quick shift to the steady state manufacturing can be realized by letting the proposed method cooperate with the demand forecasting.
In this paper, towards efficient state estimation for large-scale networked linear systems, we propose a method for designing observers to estimate the average behavior of systems, which we call average state observers. First, we give a mathematical formulation of the average state observer. Furthermore, we derive the error system clarifying that not only an initial state estimation error but also the initial value response of systems and the input signal are relevant to the observation error. On the basis of this error analysis, we devise a systematic design procedure for average state observers. The proposed method does not require the information on how to choose a set of average states of systems. The efficiency of the proposed method is shown through a numerical example for a diffusion system.