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This paper presents a practical discrete-time fractional order terminal sliding mode (DFOTSM) control strategy for high-precision tracking tasks based on a linear motor. In particular, the practical parametric uncertainties involving sliding friction, uncertain payload, and disturbance in tracking tasks are considered in this paper. Combining Grünwald–Letnikov fractional order definition and terminal sliding mode technique, the proposed method synthesizes a novel DFOTSM control law to drive the sliding mode dynamics into the stable region in finite steps theoretically, even though the system is suffering from uncertainties and disturbances, and the motion on the surface can guarantee higher tracking precision than the conventional discrete-time terminal sliding surface by selecting suitable controller parameters. The theoretical analyses give out the guideline of parameter selection, and experiments are carried out on the linear-motor-based test platform to demonstrate that the proposed controller is easily implemented and can achieve high-precision tracking, fast response, and considerable robustness to uncertainties. [ABSTRACT FROM AUTHOR]

In the centralized heating, ventilating and air-conditioning (HVAC) system, air handling units (AHUs) are traditionally controlled by single-loop proportional-integral-derivative (Pm) controllers. The control structure is simple, but the performance is usually not satisfactory. In this paper, we propose a cascade control strategy for temperature control of AHU. Instead of a fixed PID controller in the classical cascade control scheme, a neural network (NN) controller is used in the outer control loop. This approach not only overcomes the tedious tuning procedure for the inner and outer loop PID parameters of a classical cascade control system, but also makes the whole control system be adaptive and robust. The multilayer NN is trained online by a special training algorithm-simultaneous perturbation stochastic approximation (SPSA)-based training algorithm. With the SPSA-based training algorithm, the weight convergence of the NN and stability of the control system is guaranteed. The novel cascade control system has been implemented on an experimental HVAC system. Testing results demonstrate the effectiveness of the proposed algorithm over the classical cascade control system. [ABSTRACT FROM AUTHOR]

This paper presents a wavelet fuzzy neural network (WFNN) structure for identifying and controlling nonlinear dynamic systems. The proposed WFNN is constructed on the base of a set of fuzzy rules. Each rule includes a wavelet function in the consequent part of the rules. A training algorithm adopting a gradient descent method is employed to identify the unknown parameters in the WFNN. For the control problem, a WFNN-based predictive control (WFNNPC) law is derived via a generalized predictive performance criterion, and the control algorithm is proven to guarantee the convergence of the WFNNPC controller. The conditions of the stability analysis of the resulting control system are presented based on the Lyapunov stability theorem. Finally, the WFNN is applied in numerical simulations and experiments (identification and control of nonlinear dynamic systems and a physical positioning mechanism). The results confirm the effectiveness of the WFNN. [ABSTRACT FROM AUTHOR]