Hidenori Shingin

Brief paper: Disturbance rejection with information constraints: Performance limitations of a scalar system for bounded and Gaussian disturbances

This paper derives performance limitations for disturbance rejection of scalar systems under information constraints subject to either bounded or Gaussian disturbances. Two kinds of disturbance are treated in a unified manner, using appropriate entropies and distortions. It is shown that the achievable performance cannot be improved even if the maximum information constraint is relaxed to an average information constraint. Another observation is that, while the information constraints are weaker than bit-rate or signal-to-noise ratio constraints on the communication channel, the same performance levels are achieved by the best encoder and decoder for disturbance rejection with the information constraints.

Disturbance Rejection with Information Constraints: Performance Limitations of a Scalar System for Bounded and Gaussian Disturbances

Abstract We deal with disturbance rejection of a scalar system and show a unified derivation of the performance limitations for bounded and Gaussian disturbances under corresponding information constraints. The derivation is based on the common properties of two kinds of entropies. The limitations are described as the quantitative trade-offs between available amount of information and achievable control performances. We see that the limitations under communication constraints can be derived from the fact that the information constraints are weaker than the communication constraints, but the limitations under the information constraints can be achieved by the optimal coding system under the communication constraints.

Stabilization Under Signal-to-Noise Ratio Constraints on Feedback Links with Bounded Noise

Abstract This paper furnishes a new system-theoretic meaning to the product of unstable poles of discrete-time linear systems. This index has been known to represent limitations of stabilizing feedbacks in various situations. Our result shows that the index is equal to the infimum of the signal-to-noise ratio (SNR) with respect to amplitude when the feedback loop is stabilized through a noisy channel with bounded noise. Moreover, the result is extended to the probabilistic case by an information theoretical approach based on zero-order entropy.

SNR Limitation for Feedback Control Systems with Amplitude-Bounded Noise*

This paper presents an SNR limitation in control under amplitude-bounded noise. The SNR of feedback link in control system is evaluated with respect to amplitude and minimized over all causal control laws. The minimum SNR is represented by the absolute value of the product of unstable poles of the plant. This index is known to describe performance limitations in various control problems. The optimal control law minimizing the SNR is simply constructed as a combination of scaling factor and the state feedback gain for minimum energy control. This control law overcomes the drawback of computational complexity of the encoding scheme minimizing bit-rate, which is employed to minimize an amplitude-base SNR in our preceding work. This simplification is accomplished by relaxing the requirement of stability to the convergence of the state to near the origin and introducing a new criterion for evaluating SNR.

Amplitude SNR limitation in control over channels with bounded noise

This paper furnishes a new system-theoretic interpretation to the product of unstable poles of discrete-time linear systems. This index has been known to represent limitations of feedback control in various situations. Our result shows that the index gives the limitation of the signal-to-noise ratio (SNR) with respect to amplitude when the feedback loop is stabilized through a multidimensional channel with bounded noise.

Reduction of state variables based on regulation and filtering performances

Abstract This paper provides a principal component analysis of linear discrete-time systems on the basis of optimal control and estimation. The analysis is to reveal the important state components which remain necessary for reducing performance degradation under dimensional constraints on control and estimation laws. The trade-off relations between the dimension and performance degradation are expressed as system invariants representing the importance of each principal component, which are characterized as the eigenvalues of matrices depending on the solutions of both Lyapunov and Riccati equations. Based on the analysis, the paper also provides model reduction techniques for the systems generating the optimal input and estimate with the desirable properties of stability, reachability, and observability being preserved in the reduced systems.

SNR limitation for amplitude-bounded noise in control over channels with feedback

This paper shows a limitation of signal-to-noise ratio (SNR) for amplitude-bounded noise in control of linear discrete-time systems over channels with feedback. The SNR allowed in stabilizing the closed-loop system is shown to depend on the product of unstable poles of the plant. The control law for minimizing the SNR is constructed from state-feedback controller and two observers employed in the data transmission and reception processes. The gain applied to the data to be transmitted is determined from the solution of a Riccati equation in minimum energy control depending on the observer gain. Moreover, we can observe in the case of SISO plants that lack of feedback for communication does not degrade the SNR limitation when the feedback for control is available in communication.

Sliding Mode Heading Control for Autonomous Small UAV without Gyro Information

We have proposed a control algorism using holonomy for small UAVs. Because this control algorism is feedfoerward control, it has possibility that the heading angle overshoots the target yaw angle. This paper presents the sliding mode control for small UAV to reduce the overshoot. Sliding mode has feature of less overshoot. Holonomy control is used in the case that the target yaw angle is large. When the target angle becomes small, sliding mode control is used. In this way, poorly-convergence is inhibited by using sliding mode. Generally, plane is controlled using gyro rate. In this algorism, it is controlled using roll angle without gyro information. The altitude is controlled using delayed feedback control (DFC) at steady flight. DFC has characteristic to converge on unknown equilibrium position. We show that the heading control using sliding mode control and landing control. At landing operation, trajectory tracking is introduced as target angle determination. This algorism can guide UAV to runway. International standard atmosphere, steady wind and continuous gust wind are considered in the numerical simulation. In this paper, we demonstrate efficacy of sliding mode control by simulation.

Micro UAV Holonomy Control System Robust for Gust Wind

We consider autonomous control for micro UAV. The direction of movement is controlled using holonomy. The model airplane is difficult to be controlled due to nonholonomic system. It is effective to use the holonomy that is the response for the periodic input signal. Holonomy is displacement of state quantity by cycle input. At steady flight, short cycle block impulse is input to rudder. Yaw angle and roll angle change. However roll angle returns to 0 [deg] by attitude stabilizing apparatus and UAV’s stability, and yaw angle converges to a certain value. In this study, position, bank angle, and direction angle of the model airplane are controlled by the rudder only. Also, the altitude is controlled using delayed feedback control (DFC) at steady flight. The role of DFC is to make the state converge to unknown equilibrium position. During landing control, the thrust is fixed to 30% of the max thrust. We show that the direction control using holonony and landing control without gust wind are succeeded. International standard atmosphere, steady wind and continuous gust are considered in the numerical simulation.