This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

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

Model predictive control power management strategies for HEVs: A review

Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SoC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SoC trajectories that are utilized as final-state constraints in the MPC level. This PHEV energy management framework is evaluated under three different scenarios: 1) without traffic flow information; 2) with static traffic flow information; and 3) with dynamic traffic flow information. Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.

Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles

Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus

Increasing costs of fossil fuels and the requirement of reduced CO2 emissions for road vehicles make the development of alternative propulsion systems a top priority in automotive research. Hybrid hydraulic vehicles (HHVs) can contribute to improving the fuel efficiency of heavy vehicles such as garbage trucks and city buses. The combination of a conventional diesel engine with an additional hydraulic powertrain allows for regenerative braking. Further improvements with regard to fuel efficiency become possible through additional optimization of the energy management strategy, which decides when to apply which propulsion system. Rule-based strategies are the state of the art, but they cannot utilize the full potential because their performance is only superior on the cycles for which they have been developed. Approaches including numerical optimization are independent from the actual drive cycle and result in much higher savings. However, these techniques usually require a prediction of the driving profile. In this paper, a complete solution for predictive energy management in HHVs is presented. The fuel savings obtained through the developed algorithms used for prediction and optimization are determined in a simulation study, and the functionality of the concept is proven in a hybrid hydraulic testing vehicle.

Drive Cycle Prediction and Energy Management Optimization for Hybrid Hydraulic Vehicles

During the virtual development and experimental testing of advanced construction machinery, automation approaches for automated task execution can prove very valuable In this paper, modeling and automation approaches for a hydraulic mini excavator are developed In particular, a physical model for detailed system analysis and a simplified Hammerstein model for controller tuning are developed and validated with measurement data from the mini excavator For attitude estimation of the excavator, inertial measurement units and extended Kalman filters are used in a sensor fusion framework The control concept for automation is based on a virtual driver consisting of a state machine for task coordination as well as offset-free model predictive controllers (MPCs) for decentralized and robust tracking control of all motion axes The constrained MPC optimization problems are solved in real time by means of the accelerated proximal gradient method Experimental results from the mini excavator prove the developed control approach to be valuable for virtual development and automated testing during the commissioning of hydraulic machinery Note to Practitioners —In this paper, a hydraulic mini excavator is considered for demonstrating the benefits of automation with regard to the development of advanced mobile machinery A detailed physical model and a simplified model are introduced for virtual analysis and comissioning of the excavator This allows for detailed system analysis even at an early development stage Then, a framework for automated testing of the real prototype is introduced This concept is based on attitude estimation filters, a state machine, and model predictive controllers and closely resembles the human driver in its behavior, but allows for reproducible testing results and therefore reduces commissioning efforts and development costs Particular attention is paid to the robustness of the control concept, since the coupling of the hydraulic axes and digging forces lead to disturbances that need to be compensated A simple, yet efficient and real-time capable algorithm is provided for numerical optimization Experimental results show that the developed methods can contribute to the automation of hydraulic machinery, and that the introduced framework can easily be extended in order to automate other types of machinery with simple hydraulics

Modeling and Offset-Free Model Predictive Control of a Hydraulic Mini Excavator

An investigation on the fuel savings potential of hybrid hydraulic refuse collection vehicles

Continuum manipulators enable new fields of application by their inherent flexibility and continuous deformations. However, they cannot compete with conventional robots yet. This is partially caused by a lack of practical and powerful trajectory generation algorithms. Therefore, an online TCP trajectory generator for redundant continuum manipulators has been developed, that computes desired actuator states for given task variables that can be used for actuator controllers. Using the fast gradient optimization algorithm and quadratic programming, actuator velocities are optimized considering actuator constraints with respect to a cost function including the differential kinematics. Experiments prove the online capabilities of the proposed trajectory generation approach. Furthermore, the presented method can be used for controlling continuum robots by incorporating inputs from human-machine-interaction devices.

Online TCP trajectory planning for redundant continuum manipulators using quadratic programming

Hydraulic excavators play a crucial role on worldwide construction sites. Efficient operation of these machines therefore contributes to a quick completion of the construction task. In particular, optimized control strategies can lead to improvements with regard to machine performance, fuel consumption, and pollutant emissions. In this work, a nonlinear model of a hydraulic excavator is considered. It is shown that a simplified nonlinear model with Hammerstein structure can accurately represent the underlying dynamics for the purpose of control. Based on this model, a nonlinear model predictive control approach including an optimization algorithm is developed in order to have the excavator perform a task given through target positions of the four motion axes. Simulation results based on an application of the developed controller to a complex physical model of the excavator indicate good tracking performance and fast execution of the task.

Frank A. Bender

Papers

Drive Cycle Prediction and Energy Management Optimization for Hybrid Hydraulic Vehicles

Modeling and Offset-Free Model Predictive Control of a Hydraulic Mini Excavator

An investigation on the fuel savings potential of hybrid hydraulic refuse collection vehicles

Online TCP trajectory planning for redundant continuum manipulators using quadratic programming

Nonlinear model predictive control of a hydraulic excavator using Hammerstein models