Brief Non-fragile H∞ control for linear systems with multiplicative controller gain variations

Since a direct drive motor (DDM) does not require a reduction gear, the drive system can be made simple, and therefore it is used in high-precision robot and machine tool applications. However, without reduction gear, a disturbance torque is directly reflected to the motor shaft, and a torque ripple generated by the motor is directly transmitted to the load, causing poor speed control characteristics. An improved torque control system for DDM with a permanent magnet rotor, equipped with a torque observer implemented by digital signal software, is proposed. The speed fluctuation can be fully removed with a torque feedforward loop. >

Autocompensation of torque ripple of direct drive motor by torque observer

We developed a robust control policy design method in high-dimensional state space by using differential dynamic programming with a minimax criterion. As an example, we applied our method to a simulated five link biped robot. The results show lower joint torques from the optimal control policy compared to a hand-tuned PD servo controller. Results also show that the simulated biped robot can successfully walk with unknown disturbances that cause controllers generated by standard differential dynamic programming and the hand-tuned PD servo to fail. Learning to compensate for modeling error and previously unknown disturbances in conjunction with robust control design is also demonstrated.

/pdf/minimax-differential-dynamic-programming-an-application-to-xx2bubilz3.pdf

Minimax Differential Dynamic Programming: An Application to Robust Biped Walking

A longstanding goal of reinforcement learning is to develop non-parametric representations of policies and value functions that support rapid learning without suffering from interference or the curse of dimensionality. We have developed a trajectory-based approach, in which policies and value functions are represented nonparametrically along trajectories. These trajectories, policies, and value functions are updated as the value function becomes more accurate or as a model of the task is updated. We have applied this approach to periodic tasks such as hopping and walking, which required handling discount factors and discontinuities in the task dynamics, and using function approximation to represent value functions at discontinuities. We also describe extensions of the approach to make the policies more robust to modeling error and sensor noise.

/pdf/nonparametric-representation-of-policies-and-value-functions-2pb42w708h.pdf

Nonparametric Representation of Policies and Value Functions: A Trajectory-Based Approach

This paper presents a control scheme for an induction motor drive which consists of a compensator, neural network identification (NNI), and neural network load torque estimator (NNLTE) based on the conventional proportional-integral controller. The NNI is a two-layer neural network which uses a projection algorithm to estimate the parameters of the induction motor and to regulate the gain of the compensator such that the response of the induction motor follows that of the nominal plant. The NNLTE is a two-layer neural network which uses the steepest descent algorithm to estimate the load disturbance and forward feed, resulting in equivalent control such that the speed response of the induction motor is robust against the load disturbance. Computer simulations and experimental results demonstrate that the proposed control scheme can obtain a robust speed control.

Robust control of induction motor with a neural-network load torque estimator and a neural-network identification

Numerical control of a DC motor is achieved by minimizing a quadratic criterion. This quadratic criterion, which is based on the state-space model of the motor and the converter, is minimized using dynamic programming. The optimal control law is given by the product of the state-space vector and an optimal feedback matrix, and the product is added to a constant term to minimize the steady-state error. The load torque is added to the state-space vector to form an augmented model to take into account torque variations. This scheme is implanted on a 5 kW DC motor, and a comparison between the pole assignment control law and the optimal control law is made. The superiority of the optimal control law over the pole assignment method is observed. The optimal control law is also tested when the inertia moment is varying, and it is shown that this scheme is robust. >

Robust optimal control of a DC motor

A robust DC motor position control system is presented. The variable-structure system theory and practical considerations are used to design a robust linear controller incorporating a disturbance estimator for a second-order system. The control law is numerically implemented for position control. The performance of the control law is illustrated by simulation results, taking into account sampling time, computation time delay, the current regulator, and limitations on the control signal. The proposed algorithm is easy to design and implement. Simulation results have shown its robustness toward parameter variations, good dynamic response to a step in load torque, and a null static error. >

DC motor position control using sliding mode and disturbance estimator

With every-increasing requirements for higher frequency and higher power electronic converters, thermal management considerations and means for thermal analysis have become of great importance. A thermal optimization algorithm of the power devices's positions on the heat sink by means of the NISA II-PC application program is proposed. This algorithm is based on an elaborate thermal and electrical converter model and the three-dimensional finite element method. >

https://ieeexplore.ieee.org/iel2/568/4228/00162724.pdf

Thermal optimization in a power electronic supply

A comprehensive analysis for the location problem of an autonomous mobile robot is presented. In order to decrease the frequency of the measurement and modeling errors correction, a method using an approximate nonlinear filtering technique was investigated. The performance of three filters has been analyzed for this problem of estimating vehicle position and heading using optical range measurement and odometry. The three filters yielded equal performances. However, for practical implementation reasons, the first-order filter was retained to extract the exact state of the mobile robot. >

https://ieeexplore.ieee.org/iel2/608/4487/00178037.pdf

Nonlinear filtering and control for an autonomous vehicle

The authors present the results of a digital simulation of a smooth positioning system for a self-controlled permanent-magnet synchronous motor. The parameter estimation that is included in the controls is based only on the position feedback. The use of predictive control and parameter estimation enables fast, accurate, and nonoscillatory positioning of a directly driven load varying over a wide range. >

Y. Dube

Papers

Robust optimal control of a DC motor

DC motor position control using sliding mode and disturbance estimator

Thermal optimization in a power electronic supply

Nonlinear filtering and control for an autonomous vehicle

Digital simulation of a smooth positioning system with parameter estimation (permanent magnet synchronous motors)