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The complex nonlinear systems, which are difficult to be mathematically modelled, can be described by a fuzzy model. This article attempts to improve and to address the problems concerning the systematic fuzzy-logic modelling of multi-input-muiti-output (MIMO) systems, by introducing the following three concepts. 1) A generalized and parameterized reasoning mechanism constructed based on the weighted sum of the normalized defuzzified output value of each individual rule. Then the crisp outputs of the fuzzy model can be directly calculated from the crisp inputs using the parameterized reasoning mechanism. This reasoning mechanism is suitable for online learning and real-time control applications. 2) A gradient-descent based parameter adjustment to tune the parameters of reasoning mechanism (which are equal to the number of rules) instead of the existing heuristic complex parameter identification in the literature. 3) An improved method to select the main system input from all input candidates in the presence of singularity. The proposed systematic method of fuzzy modelling has the advantages of simplicity, flexibility, and high accuracy. The two example data, which have been widely used in the literature as benchmark, are used to evaluate the performance of the proposed method.

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High-performance robust controller design for nonlinear uncertain dynamical systems such as cable-driven parallel robot manipulators is a challenging work. In this paper, a new and systematic approach to combine sliding mode control, adaptive control design techniques and PID control for tracking control of cable-driven parallel robot manipulators, in the presence of model uncertainties is presented. In the proposed method, structured (parametric) and unstructured (un-modeled) uncertainties are lumped into one term and one uncertain parameter (term) is considered corresponding to each degrees of freedom of robot manipulator. Therefore, the problem of computation burden and large number of parameters, which are not addressed in the literature, is solved to a large extent. The global uniform ultimate boundedness stability is obtained in the presence of fast time-varying uncertainties. The simulation and experimental results revealed that the proposed method is robust against uncertainties and its simplicity makes the approach attractive for industrial applications.

A closed-loop control of the laser cladding process is desired due to difficulties encountered in depositing a layer with acceptable quality from both geometrical and metallurgical point of views. One of the main parameters to achieve the desired geometry in laser cladding process is the height of the deposited layers. In this paper, a real-time measurement and control of the clad height is presented. Due to complex nature of the process and presence of uncertainties, a robust and adaptive sliding mode control is proposed and implemented to control the clad height. The velocity of the substrate is used as a control input while the molten pool height, which is obtained using a charge-coupled device (CCD) camera and an image processing algorithm is used as a feedback signal. Stability of the controller is proven in the presence of time-varying uncertainties and the performance of the closed-loop system is validated by simulation and experiments. The experimental results are promising and show that the geometrical accuracy of the deposited layers can be improved significantly.