Meng Zhang

Bio: Meng Zhang is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Stability (learning theory) & Fuzzy logic. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Data-Driven Adaptive Optimal Control for Linear Systems With Structured Time-Varying Uncertainty

Abstract: In this paper, a data-driven adaptive optimal control strategy is proposed for a class of linear systems with structured time-varying uncertainty, minimizing the upper bound of a pre-defined cost function while maintaining the closed-loop stability. An off-policy data-driven reinforcement learning algorithm is presented, which uses repeatedly the online state signal on some fixed time intervals without knowing system information, yielding a guaranteed cost control (GCC) law with quadratic stability for the system. This law is further optimized through a particle swarm optimization (PSO) method, the parameters of which are adaptively adjusted by a fuzzy logic mechanism, and an optimal GCC law with the minimum upper bound of the cost function is finally obtained. The effectiveness of this strategy is verified on the dynamic model of a two-degree-of-freedom helicopter, showing that both stability and convergence of the closed-loop system are guaranteed and that the cost is minimized with much less iteration than the conventional PSO method with constant parameters.

Adaptive Guaranteed Cost of Control of Systems with Uncertain Parameters

Abstract: : Guaranteed cost control is a method of synthesizing a closed-loop system in which the controlled plant has large parameter uncertainty. This paper gives the basic theoretical development of guaranteed cost control, and shows how it can be incorporated into an adaptive system. The uncertainty in system parameters is reduced first by either on line measurement and evaluation prior knowledge on the parametric dependence of a certain easily measured situation parameter. Guaranteed cost control is then used to take up the residual uncertainty. (Author)

Compact Model-Free Adaptive Control Algorithm for Discrete-Time Nonlinear Systems

Abstract: In model free adaptive control (MFAC), a virtual equivalent dynamic linearized model is built. The linearization length constants (LLCs) of the virtual equivalent dynamic linearized model are selected by the practitioner based on experience. In this paper, the optimal LLCs are investigated, and compact model free adaptive control (CMFAC) is introduced for a class of unknown discrete-time nonlinear systems. Compared with MFAC, the proposed CMFAC does not need to consider the values of LLCs, and the optimal LLCs are decided by the desired tracking error of systems. Simulation experiments are taken, and the simulation results indicate that the proposed control algorithm is effective and can achieve asymptotic tracking.

Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems

Abstract: In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a ‘black box’ with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights’ adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications.

Autonomous Trajectory Tracking Control for a Large-Scale Unmanned Helicopter under Airflow Influence

Abstract: In this paper, a complete nonlinear dynamic unmanned helicopter model considering wind disturbance is proposed to achieve realistic simulations and teasing out the effect of wind on the control system. The wind velocity vector which is horizontal as seen in the inertial frame can be obtained by subtracting the airspeed measured by atmospheric data computer from the inertial speed measured by GPS. The design of the controller fully considers the existence of wind, and the wind disturbance is suppressed by the method of hierarchical control combined with the integral sliding mode control (SMC). The stability proof is given. Hardware in the loop (HIL) tool is employed as a practical engineering solution, and it is an essential step in validating the new algorithm before moving to real flight experiments.