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In dynamically switched systems with unknown switching signal, the control system is conventionally designed based on the worst switching scenario to ensure system stability. Such conservative design demands excessive control effort in less critical switching configurations. In the case of continuum mechanics systems, such excessive control inputs result in increased structural deformations and resultant modeling uncertainties. These deformations alter differential equations of motion which cripple the task of control. In this paper, a new approach for tracking control of uncertain continuum mechanics multivariable systems undergoing switching dynamics and unknown time delay has been proposed. Control algorithm is constructed based on the mathematical rigid model of the plant and a Common Lyapunov Function (CLF) is proposed upon sliding hyperplane regarding all switching configurations. Considering the model-based nature of sliding mode control (SMC) and inevitable uncertainties induced from modeling simplifications of continuum system or parameter evaluation errors, Finite Element Analysis (FEA) is utilized to approximate total model uncertainties. To obtain robust stability, instead of conventional switching functions in the construction of control law, the control inputs are selected such that system dynamics reside within stability bounds which are calculated based on the Lyapunov asymptotic stability criterion. Therefore, the unwanted chattering issue caused by continuous switching is not observed in control input signals. Eventually, the accuracy of the proposed method has been verified through the student version of ANSYS® mechanical APDL-based simulations and its effectiveness has been demonstrated in multiple operating conditions.

This paper is concerned with the cooperative output tracking problem of a class of unknown heterogeneous discrete-time multi-input multi-output (MI-MO) linear multi-agent systems (MASs) under switching topologies. A novel design framework is developed based on a distributed observer and a distributed multivariable adaptive control approach. First, a distributed observer is constructed to estimate the leader’s states by using the neighboring relative information under the influences of switching topologies. Then, to handle the unknown parameters of MIMO systems, a distributed multivariable model reference adaptive control approach is designed to follow the estimation states of the leader. It is shown that under the proposed design framework, the boundedness of all signals and variables in the closed-loop system is guaranteed, and the output tracking of the MASs is achieved. Moreover, the proposed design framework does not need the knowledge of global graph information. Finally, an example is provided to illustrate the effectiveness of the proposed approach.

There have been high demands of high-quality, highly efficient processing methodologies on “difficult-to-cut” titanium alloys. The current methods for dealing with this kind of materials are mainly mechanical cutting ones. However, because of high processing costs, poor surface qualities, and restrictive machining operations, the costs of mechanical cutting methods are high. Electrical discharge machining (EDM), because of its flexibility, was considered as a supplement. However, serious difficulties arose while machining titanium alloys by EDM. Because of low thermo-conductivity of titanium alloys, the liquid temperature in gap between electrode and workpiece rose quickly after a series of pulse discharges. The high temperature of gap liquid usually led to gap liquid breakdown strength to decline. The consequence was discharging pulses tended out to be stable arc pulses or short pulses, burning workpiece surface and wearing electrode. The machining process became unstable. The low thermal conductivity of titanium alloys was the inherent property which could be hardly changed, and at present, the only way to settle the hard-to-cut problem of machining titanium alloys by EDM was to seek a way to keep gap liquid breakdown strength not go down so fast but still be suitable for effective pulse discharges. To solve this problem, this paper first listed three conditions to be met and analyzed the reasons to affect gap liquid breakdown strength in detail and concluded with three factors, gap distance, amount of chips left in gap, and gap liquid deionization after pulse discharges and then came up with a proposition to the problem. Technically, the proposition was accomplished by constructing a multiple-variable adaptive control system in which gap servo-voltage proportional to gap distance was in charge of discharging extent of pulses, electrode-discharging time decided the amount of chips produced in an electrode discharging cycle, and pulse-off time decided gap liquid deionization after discharges. These variables were timely regulated to keep the liquid breakdown strength suitable for discharging and meanwhile avoiding arcing in machining. The verification test demonstrated that the multivariable control system really helped electrical discharge machining titanium alloys in severe machining situations and proved its usefulness in applications.

Introduction. A wind energy conversion system needs a maximum power point tracking algorithm. In the literature, several works have interested in the search for a maximum power point wind energy conversion system. Generally, their goals are to optimize the mechanical rotation or the generator torque and the direct current or the duty cycle switchers. The power output of a wind energy conversion system depends on the accuracy of the maximum power tracking controller, as wind speed changes constantly throughout the day. Maximum power point tracking systems that do not require mechanical sensors to measure the wind speed offer several advantages over systems using mechanical sensors. The novelty. The proposed work introduces an intelligent maximum power point tracking technique based on a fuzzy model and multivariable predictive controller to extract the maximum energy for a small-scale wind energy conversion system coupled to the electrical network. The suggested algorithm does not need the measurement of the wind velocity or the knowledge of turbine parameters. Purpose. Building an intelligent maximum power point tracking algorithm that does not use mechanical sensors to measure the wind speed and extracts the maximum possible power from the wind generator, and is simple and easy to implement. Methods. In this control approach, a fuzzy system is mainly utilized to generate the reference DC-current corresponding to the maximum power point based on the changes in the DC-power and the rectified DC-voltage. In contrast, the fuzzy model-based multivariable predictive regulator follows the resultant reference current with minimum steady-state error. The significant issues of the suggested maximum power point tracking method, such as the detailed design process and implementation of the two controllers, have been thoroughly investigated and presented. The considered maximum power point tracking approach has been applied to a wind system driving a 5 kW permanent magnet synchronous generator in variable speed mode through the simulation tests. Practical value. A practical implementation has been executed on a 5 kW test bench consisting of a dSPACEds1104 controller board, permanent magnet synchronous generator, and DC-motor drives to confirm the simulation results. Comparative experimental results under varying wind speed have confirmed the achievable significant performance enhancements on the maximum wind energy generation and overall system response by using the suggested control method compared with a traditional proportional integral maximum power point tracking controller.

Controlling the looper height and strip tension is important in hot strip mills, because these variables a ect both the strip quality and strip threading. Many researchers have proposed and applied a variety of control schemes for this problem; however, the increasingly strict market demand for strip quality requires further improvements. This paper describes a Multivariable Active Disturbance Rejection Control (MADRC) strategy that realizes the decoupling control of a hydraulic looper multivariable system. Simulation experiments of a traditional Proportion-Integration (PI) controller and the proposed MADRC controller were conducted using MATLAB/Simulink software. The simulation results show that the proposed MADRC ensures good robustness and adaptability under modeling uncertainty and external disturbance. It is concluded that the designed MADRC controller produces better dynamic performance than the traditional PI controller does, and the proposed looper control system is e ective and practical.

To improve the state-of-the-art control of a switched reluctance motor (SRM) drive, this research paper proposes and applies a novel approach for tweaking the parameters of the proportional-integral (PI) current controller using a multivariable sliding-mode extremum-seeking (MSES). The proposed MSES-PI current control approach uses a sliding-mode extremum-seeking optimizer to fine-tune the PI control gains to minimize a cost function defined by the feedback error term. As a result, the PI current controller exhibits robustness against disturbances and insensitivity to changes in SRM parameters, while remaining sensitive to tracking reference current inputs. In addition, this study proposes an innovative speed control strategy for SRM drives using an adaptive extremum-seeking PI speed controller (AES-PI). The proposed speed control utilizes a PI controller and a separate version of the extremum-seeking controller to enhance the step responsiveness of SRM drives. In particular, AES minimizes a cost function analogous to the one used to quantify the performance of the PI speed controller. Both simulation and experimental verification show that MSES-PI and AES-PI outperform traditional PI controllers. Through the utilization of ES to fine-tune the PI controller’s settings, dynamic performance is enhanced, and torque ripple and speed oscillation are significantly suppressed.

To improve the dynamic response performance of a high-flow electro-hydraulic servo system, scholars have conducted considerable research on the synchronous and time-sharing controls of multiple valves. However, most scholars have used offline optimization to improve control performance. Thus, control performance cannot be dynamically adjusted or optimized. To repeatedly optimize the performance of multiple valves online, this study proposes a method for connecting a high-flow proportional valve in parallel with a low-flow servo valve. Moreover, this study proposes an algorithm in which a proportional–integral–derivative system and multivariable predictive control system are used as an inner loop and outer loop, respectively. The simulation and experimental results revealed that dual-valve parallel control could effectively improve the control accuracy and dynamic response performance of an electro-hydraulic servo system and that the proportional-integral-derivative–multivariable predictive control controller could further dynamically improve the control accuracy.

This paper focuses on the identification of a multivariable controlled autoregressive-like (CAR-like) system. A joint identification algorithm of stochastic gradient and least squares is deduced for estimating the system parameters by decomposing the multivariable CAR-like system into two subsystems, which avoids the calculation of the matrix inversion. To further improve the parameter estimation accuracy, a joint identification algorithm of hierarchical multi-innovation stochastic gradient and least squares is proposed by using the multi-innovation identification theory. The simulation results confirm that these proposed algorithms are effective.

A form with the extraction of the zero dynamics is the most convenient canonical form of a linear time-independent multivariable control system. Only systems with vector relative degree can be reduced to such a form. There exist control systems that, together with any system obtained from them by a time-independent change of outputs, have no relative degree. To ensure the relative degree conditions, we suggest to use an invertible dynamic change of measured outputs of the system, which allows one to solve the problem on the reduction of a linear time-independent MIMO-system to a form with relative degree in the most general case.

We consider a linear time-invariant multivariable square control system. For this system, we are interested in the description of zero dynamics, that is, the dynamics of the system for the case of identically zero output. We study the case in which the relative degree is undefined for the system.