In this paper, a novel robust tracking control strategy based on funnel control is proposed for servo drive systems with unknown disturbances. A modified funnel variable is defined and incorporated ...into the control design to guarantee the tracking error within a prescribed boundary. To reject the bounded disturbances, a robust integral of the sign of the error (RISE) controller based on the funnel variable is proposed for servo drive systems. Moreover, the desired compensation technique is incorporated into the developed controller to reduce the sensor noise. The proposed robust controller theoretically guarantees asymptotic tracking control performance with external disturbances. The closed-loop system convergence is analyzed via the Lyapunov stability theory. Comparative numerical and experimental results of the servo drive system are provided.
•A novel funnel variable is modified and incorporated into control design to improve the transient and steady-state performance.•Using the FC and RISE technique, a funnel tracking control scheme is proposed for servo mechanisms to achieve asymptotic tracking control.•Comparative experimental studies show the advantages of the proposed control method in comparison to other control schemes.
This study proposes a novel topology for reducing commutation torque ripple in a brushless DC motor (BLDCM) drive system using a three-level neutral-point-clamped (NPC) inverter combined with ...single-ended primary-inductor converter (SEPIC) converters. In the BLDCM, current ripples arise because of the influence of stator winding inductance, which generates torque ripples. The torque ripple that is generated in the commutation period prevents the use of BLDCM in high-precision servo drive systems. In this study, two-stage converters are proposed to reduce the torque ripple. The first stage consists of two SEPIC converters to obtain the desired commutation voltage according to motor speed. A dc-link voltage selection circuit is combined with the SEPIC converters to apply the optimised voltage during the commutation interval. To reduce the torque ripple further, a three-level NPC inverter is used to apply a half dc-link voltage across the motor winding and this effectively reduces the torque ripple. Experimental results show that the proposed topology is able to reduce commutation torque ripple significantly under both low-speed and high-speed operation.
A simple and efficient parameter identification algorithm for a permanent magnet synchronous motor (PMSM) drive system is proposed using a recursive least square (RLS) algorithm without any ...additional sensor. Servo motor control system requires parameter identification due to aging, parameter changes, and mechanical components for driving the reliability and performance of PMSMs. Regarding industrial applications, model-based prediction algorithms are generally expected to be developed. The most important reason is that complex mathematical operations and advanced digital filters are unsuitable for low-cost processors. In this study, all motor parameters (stator resistance, stator inductance, flux linkage, viscous friction, and inertia) are identified using the RLS method as offline. The proposed parameter estimation method for PMSM has been implemented by using the RLS algorithm. Experimental results demonstrate the feasibility and effectiveness of the proposed method.
The problem of robustifying optimal linear quadratic regulator (LQR) is considered for a class of uncertain linear systems. First, an optimal controller is designed for the nominal system and an ...integral sliding surface is constructed. The ideal sliding motion can minimize a given quadratic performance index, and the reaching phase, which is inherent in conventional sliding mode control, is completely eliminated. Then the sliding mode control law is synthesized to guarantee the reachability of the specified sliding surface. The system dynamics is global robust to uncertainties which satisfy matching conditions. Finally, the proposed global robust optimal sliding mode controller (GROSMC) is applied to an electrical servo drive system. Simulation results show that the GROSMC has both optimal performance and robustness to parameter variations and external load disturbances, which are superior to the traditional LQR.
This study presents a self-evolving probabilistic fuzzy (PF) neural network with asymmetric membership function (SPFNN-AMF) controller for the position servo control of a permanent magnet linear ...synchronous motor (PMLSM) servo drive system. In the beginning, the dynamic model for the PMLSM is analysed on the basis of field-oriented control. Subsequently, an SPFNN-AMF control system, which integrates the advantages of self-evolving NN, PF logic system, and AMF, is proposed to handle vagueness, randomness, and time-varying uncertainties of the PMLSM servo drive system during the control process. For the SPFNN-AMF, the proposed learning algorithm consists of the structure learning and parameter learning in which the former is used to grow and prune the fuzzy rules automatically, whereas the latter is utilised to train the network parameters dynamically. Finally, detailed experimental results of two position commands tracking at different operation conditions demonstrate the validity and robustness of the proposed SPFNN-AMF for controlling the PMLSM servo drive system.
In this paper, to improve the robustness and realize the precision positioning as well as speed control of the servo drive system, an integrated sliding-mode control (ISMC) optimized by differential ...evolution (DE-ISMC) algorithm is proposed. ISMC guarantees that a motor can reach at given position with given speed, and given speed is set first. ISMC, which is combined with speed regulator and position regulator, is different from the conventional sliding-mode control (SMC) replacing speed regulator. ISMC can guarantee that motor speed is controlled under position control mode, which is a novelty and an advantage of ISMC in this paper. To achieve good control performance of ISMC, parameters of it should be chosen exactly, so DE algorithm is selected to optimize them. The most significant influence factor of DE algorithm is the optimization iteration. Once parameters of ISMC are optimized under convergent iteration, servo system performance reaches given indices in the shortest time. Experimental results indicate that robustness of the servo system is improved; correctness and effectiveness of DE-ISMC are verified.
A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) control is proposed in this paper for position servo drive systems to track various periodical position references with ...robustness. The adopted position servo drive system is designed using a six-phase PMSM and equipped with a fault-tolerant control scheme. First, an ideal computed torque controller is designed for the tracking of the rotor position reference command. Since the uncertainties of the PMSM position servo drive system are difficult to know in advance, it is impossible to design an ideal computed control law for practical applications. Therefore, the RFNCMAN is proposed to mimic the ideal computed torque controller with a compensated controller to compensate the approximation error. In the RFNCMAN, a recurrent fuzzy cerebellar model articulation network (RFCMAN) is adopted in the first dimension to enhance the online learning rate and localisation learning capability. Moreover, a general recurrent fuzzy neural network (RFNN) is adopted in the second dimension to enhance the generalisation performance and to reduce the required memory and rule numbers. Finally, the proposed position control system is implemented in a 32-bit floating-point DSP. The effectiveness of the proposed RFNCMAN control system is verified by some experimental results.
The meta-heuristic algorithms have aroused great attention for controller optimization. However, most of them are inseparable from the explicit system models when addressing a constrained ...optimization problem (COP). In this paper, we propose a data-driven constrained bat algorithm via a gradient-based depth-first search (GDFS) strategy. In the proposed scheme, the GDFS strategy can predetermine a search space that satisfies some strict constraints (e.g., stability requirements) of the optimized system. Meanwhile, an improved boundary constraint handling method is proposed to limit the exploration process to the predetermined space. In this way, the proposed algorithm can solve the COP by utilizing experimental data from real scenes, thereby relieving the dependence on precisely modeling the complex system. Together with an ɛ-constraint-handling method, the bat algorithm is employed to seek the global optimum of the COP. The search performance is enhanced by the designed linear-varying elite layer-based local search and a social learning-based walk mechanism to dynamically balance exploration and exploitation. The convergence is ensured based on the criteria of the stochastic optimization algorithm. Experimental results on a servo drive system and benchmark test functions verify the effectiveness of the proposed algorithm.
•Design a data-driven optimization method for the constrained controller tuning.•Design a gradient-based depth-first search strategy for strict constraints.•BA integrates a novel search mechanism to prevent premature convergence.•An improved boundary constraint handling scheme is proposed.
