The wave excitation force estimation and prediction plays an important role in improving the performance of causal and non-causal controllers for wave energy converters (WECs). This paper proposes a robust adaptive sliding-mode observer (ASMO) to estimate the wave excitation force subject to unknown disturbances and parametric uncertainties for a multi-motion multi-float WEC, called M4. Both the convergence time and the estimation error can be explicitly bounded within expected limits by tuning the ASMO parameters, which are essentially beneficial for causal controllers to maintain the control performance. A fixed-time convergent sliding variable is designed to drive the estimation error into a small region within a fixed time. Due to the adaptive law, the overall system is proven to be finite-time stable, which allows explicit formulations of the convergence time and the estimation error. Moreover, based on the wave force estimation by the ASMO, an improved Auto-Regressive (AR) model whose coefficients are updated by online training is developed to predict the wave excitation force. The prediction errors can also be explicitly estimated to achieve guaranteed control performance for the non-causal controller requiring future excitation force. From the comparison based on a realistic sea wave gathered from Cornwall, UK, it can be found that compared with the conventional Kalman Filter, the ASMO achieves a smaller steady-state estimation error and has satisfactory robustness performance against 30% model mismatch.
A plant/controller integrated design strategy based on the nested optimization strategy and guaranteed cost control for a motor driving system is developed. Because of the coupling between the plant design and the controller design, the conventional sequential design methods cannot guarantee an overall optimality for the controlled motor driving system. The integrated design strategy proposed in this paper aims to tackle this problem by simultaneously optimizing the controller design and plant design. The integrated design objective is to drive the largest load and guarantee the satisfactory robust control performance for reference tracking subject to parametric uncertainties. This is enabled by integrating a guaranteed cost controller into the integrated design strategy and improves the reliability of the control scheme. A novel combined performance index including the plant design objective and the control performance is developed as the integrated design cost function for the motor driving system. A nested optimization strategy is employed so that the optimality of the system can be calculated in an efficient and reliable way. Experimental results demonstrate the effectiveness of the proposed integrated design method. Index Terms-Integrated design of plant/controller, Guaranteed cost control, Motor driving system, Nested optimization method.
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