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This paper provides necessary and sufficient conditions for the existence of the static output-feedback (OPFB) solution to the H∞ control problem for linear discrete-time systems. It is shown that the solution of the static OPFB H∞ control is a Nash equilibrium point. Furthermore, a Q-learning algorithm is developed to find the H∞ OPFB solution online using data measured along the system trajectories and without knowing the system matrices. This is achieved by solving a game algebraic Riccati equation online and using the measured data. A simulation example shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the first issue of Nature 2015, Google DeepMind published a paper “Human-level control through deep reinforcement learning.” Furthermore, in the first issue of Nature 2016, it published a cover paper “Mastering the game of Go with deep neural networks and tree search” and proposed the computer Go program, AlphaGo. In March 2016, AlphaGo beat the world’s top Go player Lee Sedol by 4:1. This becomes a new milestone in artificial intelligence history, the core of which is the algorithm of deep reinforcement learning (RL). [ABSTRACT FROM AUTHOR]

There has been continuously increasing interest in applying neural networks (NNs) to identification and adaptive control of practical systems that are characterized by nonlinearity, uncertainty, communication constraints, and complexity. The past few years have witnessed a variety of new developments in NN-based approaches for behavior learning, information processing, autonomous decision, and system control. Biologically inspired NN structures can significantly enhance the capabilities of information processing, control, and computational performance. New discoveries in neurocognitive psychology, sociology, and elsewhere reveal new neurological learning structures with more powerful capabilities in complex problem solving and fast decision in dynamic environments. The goal of the special issue is to consolidate recent new developments in NN structures for signal processing, autonomous decision, and adaptive control with application to complex systems. It includes contributions from a wide range of research aspects relevant to the topic, ranging from neural computing, adaptive control, cooperative control, autonomous decision systems, mathematical and computational models, neuropsychology decision and control, algorithms and simulation, to applications and/or case studies. This issue contains 24 papers and the contents of which are summarized below. [ABSTRACT FROM PUBLISHER]