For large scale system, distributed model predictive control(MPC) based on Nash optimality can reduce the online computational burden.
It could make a good trade off among the initial feasible region, the control performance, and the online computation burden.
According to the design of the HFNN hierarchical fuzzy-neural network, the observer-based adaptive controller can alleviate the online computation burden.
Moreover, the proposed hierarchical fuzzy structure can greatly reduce the number of adjusted parameters of the IT2HFNC, and then, the problem of online computational burden can be solved.
The characteristic property of the multi-step control set also makes it possible to derive an algorithm with low online computation burden.
Meanwhile, an efficient version of RMPC is also developed to reduce the online computational burden, which makes the design more practical.
Moreover, to relax the online computation burden existing in the ABNC, a simplified ABNC (Simpl_ABNC) with less parameters to be adjusted online is proposed to improve the control performance.
Linear aggregation in the input is an effective method to reduce the online computational burden of model predictive control (MPC) but at the cost of degradations in the closed-loop performance.
In matters concerning one's reputation and dignity online, the burden of proof is placed on the individual, who is already at a disadvantage with little say in how their personal data is distributed, disseminated, and to who.
For example, Worldmapper is an online tool (burden of illness) that relates the size of countries to an outcome of interest, such as number of nurses working or deaths from noncommunicable diseases [ 30].
