Aspects Of Intuitive Control Framework: Stabilize, Optimize, And Identify

The duality of estimation and control problems is a well known fact in control theory literature. Parameter convergence and closed loop stability are usually competing interests for a given control scheme. This motivates identification routines to be performed only in offline experiments. On the other hand stable controllers do not guarantee parameter convergence to true parameters. Thus there is a need for a higher level abstraction for a control scheme which acts in stages and prioritizes various aspects at different stages. The stage abstraction for controller is inspired by human intuition towards dealing with control and identification simultaneously and hence named Intuitive control framework. The first stage prioritizes stabilization of states only. The controller moves onto the next stage after the unknown system is stabilized. The subsequent stages involves optimization with different performance metrics through adaptive learning. After enough information for identification is acquired, the control schemes developed for various optimal metrics are used to estimate the unknown parameters in the final stage. This narrative for selective prioritization of objectives and a higher level abstraction for control schemes is illustrated for a continuous linear time invariant state space realization with state feedback. Numerous real-world applications can benefit from this online system identification routine inspired by the human cognitive process. This offers a seamless integration of control and identification with a higher level of priorities. Such framework is presented with explicit formulations for certain classes of dynamic systems, and evaluated with computer simulations as well as experimental results. Further computation of forward reachable sets after identification also offers the only way to perform such computation for an unknown system without the need for experimentation. Identified reachable sets are also presented with a discussion on their accuracy

Implementation and Testing of Adaptive Augmentation Techniques on a 2-DOF Helicopter

This paper presents the design procedure and experimental results of a high performance adaptive augmentation technique applied to a controller derived based on linear quadratic methods. The Quanser 2-DOF helicopter was chosen as the experimental platform on which these controllers were implemented. The paper studies the implementation of each of these controllers stand-alone as well as in the augmented scheme, and discusses its performance and robustness for cases with parametric uncertainties, and unmodeled dynamics. An attempt is made to combine linear quadratic tracker’s reliability with the adaptive augmentation’s robustness towards modeling uncertainties. It is found that appropriate tuning of parameters in the adaptive framework is key to its performance and thus the process of choosing the parameters is elaborated along with guidelines for choosing a reference model. Tuning considerations for controller implementation on the experimental setup as compared to the same on the numerical model are also addressed. The experiments performed on this nonlinear MIMO system serve as a suitable research test and evaluation basis for robotics and flight control applications.</jats:p

A Pressure Modulating Sensorized Soft Actuator Array for Pressure Ulcer Prevention

Pressure ulcers are a serious reoccurring complication among wheelchair users with impaired mobility and sensation. It is postulated that external mechanical loading, specifically on bony prominences, is a major contributing factor in pressure ulcer formation. Prevention strategies mainly center on reducing the magnitude and duration of external forces acting upon the body. Seat cushion technologies for reducing pressure ulcer prevalence often employ soft materials and customized cushion geometries. Air cell arrays used in time-based pressure modulation techniques are seen as a promising alternative; however, this approach could be further enhanced by adding real-time pressure profile mapping to enable automated pressure modulation customizable for each user’s condition. The work presented here describes the development of a prototype support surface and pressure modulation algorithm which can monitor interface pressure as well as automatically offload and redistribute concentrated pressure. This prototype is comprised of arrays of sensorized polymeric soft air cell actuators which are modulated by a pneumatic controller. Each actuator’s pressure can be changed independently which results in a change to the interface pressure allowing us to offload targeted regions and provide local adjustment for redistribution. The pressure mapping, redistribution, and offloading capabilities of the prototype are demonstrated using pressure modulation algorithms described here.</jats:p