Stochastic differential equations and diffusion processes

We review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

/pdf/algorithms-for-collision-free-navigation-of-mobile-robots-in-1s1ksc76if.pdf

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Mathematics and Computers in Simulation

1. What is an ultrasonic motor? 2. Theoretical treatment of component elements 3. The Piezoelectric element and vibrator 4. Theory and experiments on the wedge-type motor 5. Theory of the ultrasonic wave motor 6. Equivalent-circuit analysis for the travelling-wave motor 7. Design, assembly and testing of a prototype ultrasonic motor 8. Comparison with electromagnetic motors Autobiographical notes (Toshiiku Sashida) Index

An introduction to ultrasonic motors

One of the most well-known bio-inspired algorithms used in optimization problems is the particle swarm optimization (PSO), which basically consists on a machine-learning technique loosely inspired by birds flocking in search of food More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum The Darwinian particle swarm optimization (DPSO) is an evolutionary algorithm that extends the PSO using natural selection, or survival of the fittest, to enhance the ability to escape from local optima This paper firstly presents a survey on PSO algorithms mainly focusing on the DPSO Afterward, a method for controlling the convergence rate of the DPSO using fractional calculus (FC) concepts is proposed The fractional-order optimization algorithm, denoted as FO-DPSO, is tested using several well-known functions, and the relationship between the fractional-order velocity and the convergence of the algorithm is observed Moreover, experimental results show that the FO-DPSO significantly outperforms the previously presented FO-PSO

/pdf/introducing-the-fractional-order-darwinian-pso-1tln0u6ssp.pdf

Introducing the fractional-order Darwinian PSO

This paper proposes a decentralized model predictive control method based on a dual decomposition technique. A model predictive control problem for a system with multiple subsystems is formulated as a convex optimization problem. In particular, we deal with the case where the control outputs of the subsystems have coupling constraints represented by linear equalities. A dual decomposition technique is applied to this problem in order to derive the dual problem with decoupled equality constraints. A projected subgradient method is used to solve the dual problem, which leads to a decentralized algorithm. In the algorithm, a small-scale problem is solved at each subsystem, and information exchange is performed in each group consisting of some subsystems. Also, it is shown that the computational complexity in the decentralized algorithm is reduced if the dynamics of the subsystems are all the same. Numerical examples are given to show the effectiveness of the proposed method.

/pdf/decentralized-model-predictive-control-via-dual-2tev2ebqy5.pdf

Decentralized model predictive control via dual decomposition

http://www.nt.ntnu.no/users/skoge/prost/proceedings/PID-12/papers/0066.pdf

Online controller tuning via FRIT and recursive least-squares

Ultrasonic motors (USMs) intrinsically have a dead-zone property which is sensitive to load changes. This paper proposes a fictitious reference iterative tuning (FRIT) method for systems with a dead-zone property such as USMs. The standard FRIT method is basically developed for linear systems and may not give a satisfactory control performance for noninvertible nonlinear systems including USMs. In contrast, the proposed FRIT method can achieve such a performance by introducing a right inverse of a dead-zone function as a dead-zone compensator. In the optimization process of FRIT, the so-called covariance matrix adaptation evolution strategy (CMA-ES) is used for simultaneously searching a dead-zone parameter as well as controller parameters. CMA-ES is a kind of stochastic multi-point search techniques and is effective for nondifferentiable and nonconvex optimization problems. Experimental results for a USM are given to show the effectiveness of the proposed method.

FRIT for Systems with Dead-Zone and Its Application to Ultrasonic Motors

PID controller tuning based on the covariance matrix adaptation evolution strategy (特集 進化技術の基礎と応用)

In this paper, a stochastic asymptotic stabilization method is proposed for deterministic input-affine control systems, which are randomized by including Gaussian white noises in control inputs. The sufficient condition is derived for the diffucion coefficients so that there exist stochastic control Lyapunov functions for the systems. To illustrate the usefulness of the sufficient condition, the authors propose the stochastic continuous feedback law, which makes the origin of the Brockett integrator become globally asymptotically stable in probability.

Kanya Tanaka

Papers

Decentralized model predictive control via dual decomposition

Online controller tuning via FRIT and recursive least-squares

FRIT for Systems with Dead-Zone and Its Application to Ultrasonic Motors

PID controller tuning based on the covariance matrix adaptation evolution strategy (特集進化技術の基礎と応用)

Stochastic Asymptotic Stabilizers for Deterministic Input-Affine Systems Based on Stochastic Control Lyapunov Functions

Kanya Tanaka

Papers

Decentralized model predictive control via dual decomposition

Online controller tuning via FRIT and recursive least-squares

FRIT for Systems with Dead-Zone and Its Application to Ultrasonic Motors

PID controller tuning based on the covariance matrix adaptation evolution strategy (特集 進化技術の基礎と応用)

Stochastic Asymptotic Stabilizers for Deterministic Input-Affine Systems Based on Stochastic Control Lyapunov Functions

PID controller tuning based on the covariance matrix adaptation evolution strategy (特集進化技術の基礎と応用)