There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

https://www.fossen.biz/home/papers/JohansenFossen%20Automatica%202013.pdf

Control allocation—A survey

This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises

Nonlinear System Identification

http://www-personal.umich.edu/%7Ehpeng/On-board%20state%20of%20health%20monitoring%20of%20lithium-ion%20batteries.pdf

On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression

Learning And Soft Computing Support Vector Machines Neural Networks And Fuzzy Logic Models

http://www.umich.edu/~racelab/static/Webpublication/2009-MATCOM-CharlesL.pdf

Linear programming support vector regression with wavelet kernel: A new approach to nonlinear dynamical systems identification

Overactuated systems often arise in automotive, aerospace, and robotics applications, where for reasons of redundancy or performance constraints, it is beneficial to equip a system with more control inputs than outputs. This necessitates control allocation methods that distribute control effort amongst many actuators to achieve a desired effect. Until recently, most methods have treated the control allocation as static in the sense that different dynamic authorities of the actuators were not taken into account. Recent advances have used model predictive control allocation (MPCA) to consider the dynamic authorities of the actuators over a receding horizon. In this paper, we consider the dynamic control allocation problem for overactuated systems where each actuator has different dynamic control authority and hard saturation limits. A modular control design approach is proposed, where the controller consists of an outer loop controller that synthesizes a desired virtual control input signal and an inner loop controller that uses MPCA to achieve the desired virtual control signal. We derive sufficient stability conditions for the composite feedback system and show how these conditions may be realized by imposing an additional constraint on the MPCA design. An automotive example is provided to illustrate the effectiveness of the proposed algorithm.

Model predictive control allocation for overactuated systems - stability and performance

This paper addresses the challenge of controlling an overactuated engine thermal management system where two actuators, with different dynamic authorities and saturation limits, are used to obtain tight temperature regulation. A modular control strategy is proposed that combines model predictive control allocation (MPCA) with the use of an inner loop reference model. This results in an inner loop controller that closely matches a dynamic specification for input-output performance while addressing actuator dynamics and saturation constraints. This paper presents the design and implementation strategy and illustrates the effectiveness of the proposed solution through real-time simulation and experimental results.

/pdf/predictive-control-allocation-for-a-thermal-management-1rokd2iysn.pdf

Kenneth Butts

Papers

Linear programming support vector regression with wavelet kernel: A new approach to nonlinear dynamical systems identification

Model predictive control allocation for overactuated systems - stability and performance

Predictive Control Allocation for a Thermal Management System Based on an Inner Loop Reference Model—Design, Analysis, and Experimental Results

Emissions Modeling of a Light-Duty Diesel Engine for Model-Based Control Design Using Multi-Layer Perceptron Neural Networks

Improving Model-based Design for Automotive Control Systems Development