Data‐based learning control for optimization of nonlinear systems

Abstract

With the development of science and technology, practical systems such as the power systems, traffic systems, robot manipulator systems, etc., have become more complex. Therefore, it is difficult to build practical systems by accurate models. Under the lack of accurate process models, using system data to improve system performance and learn optimal decisions becomes very important. Through the recent years, data-based learning control theories and technologies have widely been investigated, including adaptive dynamic programming, reinforcement learning, iterative learning control, and so on. Data-based methods require the system data instead of the accurate knowledge of system dynamics that can be considered as model-free learning control methods. The data-based methods are effective solutions for the optimal control of nonlinear systems, which motivate this special issue. This special issue aims to collect and present original research dealing with data-based learning and their applications for optimization and control problems. The first group of papers1-7 focuses on data-based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick-slip type piezoelectric actuator to realize the precise control of the end effector.1 A systematic online adaptive dynamic programming control framework is proposed for smart buildings control to ensure hard constraints to be satisfied.2 A multi-verse optimizer tuned PI-type active disturbance rejection generalized predictive control method is described for the motion control problems of ships.3 The sufficient optimality conditions for the optimal controls are established under some convexity assumptions.4 A receding-horizon reinforcement learning algorithm is proposed for near-optimal control of continuous-time systems under control constraints.5 In order to solve the interference compensation control problem of a class of nonlinear systems, a method based on memory data is introduced to suppress interference greatly.6 A new controller design method is proposed for the trajectory tracking problem of robots with imprecise dynamic properties and interference.7 The second group of papers8-12 considers iterative learning identification and iterative learning control. An iterative learning control approach is proposed for linear parabolic distributed parameter systems with multiple actuators and multiple sensors.8 The quantized data-based iterative learning tracking control problem is studied for nonlinear networked control systems with signals quantization and denial-of-service attacks.9 The output tracking problem is considered for a class of nonlinear parabolic distributed parameter systems with moving boundaries.10 A just-in-time learning based dual heuristic programming algorithm is proposed to optimize the control performance of autonomous wheeled mobile robots under faults or disturbances.11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems.12 The third group of papers13-19 focuses on robustness on data-based optimal learning control. A novel Nash game-theoretical optimal adaptive robust control design approach is proposed to address the constraint-following control problem for the uncertain underactuated mechanical systems with fuzzy evidence theory.13 A partial model-free sliding mode control strategy is proposed for a class of disturbed systems.14 A new data-based adaptive dynamic programming algorithm is proposed to solve the optimal control policy for discrete-time systems with uncertainties.15 A method that applies event-triggered mechanism H ∞ $$ {\mathrm{H}}_{\infty } $$ control to continuous-time nonlinear systems with asymmetric constraints based on dual heuristic dynamic programming structure is proposed.16 A novel anti-disturbance inverse optimal controller design method is proposed for a class of high-dimensional chain structure systems with any disturbances, matched, or mismatched.17 A data-driven H ∞ $$ {\mathrm{H}}_{\infty } $$ controller design method is studied for continuous-time linear periodic systems.18 The problem of the post-stall pitching maneuver of an aircraft with lower deflection frequency of control actuator is studied by considering the unsteady aerodynamic disturbances.19 The fourth group of papers20-23 focuses on neural networks and deep neural networks learning methods for optimal control. An optimal tracking control problem for the injection flow front position arising in the filling process in the injection molding machine is considered, and an intelligent real-time optimal control method based on deep neural networks is developed for the online tracking of the flow front position to improve the efficient production process of the plastics.20 An efficient and systematic method is proposed for model-based predictive control synthesis.21 The decentralized control issues of nonlinear large-scale systems are investigated via critic-only adaptive dynamic programming learning methods.22 A singularity-free online neural network-based sliding mode control method is proposed to realize the fixed-wing perch maneuver.23 The fifth group of papers24-27 discusses data-based control for distributed control systems. A mission-driven control scheme, including a consensus-based near-optimal formation controller and a finite-time precise formation controller, is proposed aiming at different requirements of unmanned aerial vehicle swarm.24 The neural network adaptive formation control of a class of second-order nonlinear systems with unmodeled dynamics is investigated, where the control law merely depends on the relative bearings between neighboring agents.25 The neighbor Q-learning based consensus control algorithm is developed for discrete-time multiagent systems.26 The fault-tolerate containment control problem is considered for stochastic nonlinear multiagent systems in the presence of input saturation and sensor faults.27 The sixth group of papers28-30 considers applications of data-based learning methods to industrial processes. A stochastic gradient algorithm based on the minimum Shannon entropy is proposed to identify a type of Hammerstein system with random noise.28 A predictive control strategy based on Hammerstein–Wiener inverse model compensation is proposed aiming at the nonlinearity and large lag of the pH change in wet flue gas desulfurization process.29 An algorithm called the kernel entropy regression is proposed to enhance the interpretability between the fault and the key performance indicator.30 The seventh group of papers31-36 focuses on machine learning, data mining, and practical applications in automation. The performance of a Takagi–Sugeno fuzzy-model-based observer is enhanced by proposing a featured multi-instant united switch-type observer.31 The reinforcement learning theory with deep Q-network is applied for the mobile robot to achieve a collision-free path in an unknown dynamic environment.32 An energy-saving velocity planning algorithm is proposed for rail transit train with running and computation delays.33 A novel COVID-19 transmission model is established by introducing traditional susceptible–exposed–infected–removed disease transmission models into complex network.34 A novel collaborative diagnosis method is presented by combining variational modal decomposition and stochastic configuration network for incipient faults of rolling bearing.35 The linear dependence graph associated with a finite-dimensional vector space is studied.36 In summary, this special issue provides an opportunity to review the most recent developments in data-based learning control for optimization of nonlinear systems, by considering theory, algorithms, and applications.