Design and implementation of a self-tuning adaptive controller for vector-controlled induction motor drives

This paper presents a new scheme of adaptive vector control for inverter-fed induction motors, which can identify crucial motor parameters in a real-time manner. The total system has a novel configuration consisting of two subsystems, a vector control system in the synchronous frame and an adaptive identification system in the stationary frame. The proposed adaptive identification system is new and can adaptively and simultaneously identify two motor parameters varying dynamically with temperature, magnetic saturation or motor faults, which are related directly with control performance and possibly with real-time motor fault detection and diagnosis. High performance of the proposed scheme is verified through practical evaluation tests of the adaptive vector control system. Variation of torque generated by the system to motor frame temperature variation of about 50/spl deg/C is only 0.5%. Linearity of torque generation up to 300% of torque rating is ensured. Deviation from ideal response is almost constant up to 300% of torque rating, whose magnitude is only about 1% of torque rating. Inverse of rotor time constant, which is one of the identified parameters for high control performance, seems to present also straight and valuable information on rotor-bar breaks where rotor resistance increases, inductance decreases and consequently the change of inverse of rotor time constant as their ratio is much enhanced.

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

Hydraulic system is widely used in industry where large actuation forces are needed such as electro-hydraulic positioning system, active suspension control, and so on. One of the main problem in hydraulic system is the nontriviality, for instance, the deadbands in valve operation, frictions of links, saturations, pressure dynamics, etc. both system unknown nonlinearities and external disturbances should be considered in controller design. For underwater hydraulic manipulator, because of the environment of sea and the large nonlinearities of the hydraulic manipulator system, both system unknown nonlinearities and external disturbances should be considered in controller design. A lot of control methods have been proposed to solve the problem properly, such as sliding mode control, adaptive control, fuzzy adaptive control, adaptive robust control, ect. However, due to limited knowledge about hydraulic manipulator system, it is hard to precisely estimate the nonlinearities for a possible higher performance. In this paper, a neural network adaptive robust control method is proposed to help control an underwater hydraulic manipulator. In order to achieve better performance using adaptive robust control strategy, unknown nonlinearities in the system are approximated by the outputs of radial basis function neural network (RBFNNs). A projection law has been used to bound the NN weights when they are tuning on-line to avoid possible adaptive divergence, and the tracking transient performance is guaranteed by robust control law. Simulation results illustrate that manipulator has a good tracking performance.

This paper presents a new robust adaptive current control strategy for induction motor drive systems. This control scheme is based on an adaptive pole placement control strategy (APPC) integrated to a sliding mode control scheme (SMC). The proposed APPC employs the internal model principle for achieving a zero steady state tracking error of machine stator currents. Moreover, this current controller can be implemented on the stator reference frame transformations. With this control scheme, fast transient response and robustness to parametric uncertainties and unmodeled disturbances are obtained. These disturbance can be owing to saturation, temperature changes or loading conditions. Simulation and experimental results are presented for showing the effectiveness of the proposed close-loop current control strategy.

The authors present an adaptive velocity controller for a permanent magnet synchronous motor without using a shaft sensor. Two line-to-line voltages and two stator currents are sensed to produce the flux position. The design part of this work is concerned with the formulation of control algorithms for a current-regulated pulse width modulated inverter and a vector control strategy for the speed loop. Under the vector control framework, self-tuning, model following, and model reference adaptive control are applied to the design for the speed loop controllers. The implementation part of the work integrates the control of current loop and speed loop using microprocessor-based controllers. Experimental results validate the theoretical development. A novel approach for designing an advanced adaptive controller for a sensorless AC drive is provided. >

This paper proposes a new adaptive fuzzy nonlinear control strategy that allows designers to systematically construct fuzzy control. The control strategy proposes the use of fuzzy logic systems within a well understood control structure that is called nonlinear internal model control (NIMC) structure. The attractive feature of the NIMC structure is that the relations between some designed parameters and the performance of the control system can be found explicitly. Thus, this control structure allows designers to systematically construct fuzzy control. In addition to using fuzzy logic systems within a well understood control structure, an adaptive control strategy is also proposed in this paper. The control strategy is applied to control the inverted pendulum model. The results show that the proposed strategy can maintain the system stability and give a good control performance.

In order to improve the operation performance of AC drive system, a combined control strategy for matrix converter fed induction motor drive system is proposed, which implements both space vector modulation of matrix converter and direct field oriented control of induction motor. Using this combined control strategy, it is possible to combine the advantages of matrix converter and the advantages of vector control technique and realize high performance control of induction motor. Applied with Matlab/Simulink, the simulation of the matrix converter with such strategy is carried out and the results of simulation are presented. Inductive load simulation is carried out on this matrix converter prototype. The simulation results verify the workability of the vector control strategy for matrix converter and the control performance of AC drive system is improved.

SUMMARYIn most dynamic adaptive control simulation of robotic manipulators, the Langrange–Euler (L–E) dynamic equations are first piecewise linearized about the desired reference and then discretized and rewritten in a state space form. This makes things very complicated and it is easy to make errors. What is more is that with a different reference this work must be done again. A new simulation scheme – Backward Recursive Self-Tuning Adaptive (BRSTA) – as it will be called, is suggested in this paper for adaptive controller design of robot manipulators. A two degree of freedom robot manipulator is used to verify the scheme in the condition of highly nonlinear and highly coupled system. A one degree of freedom robot manipulator is used for comparing both the forward and backward methods. The main advantages of this scheme include that it can be used for evaluating the self-tuning adaptive control laws and provide the initial process parameters for real-time control. And it is concluded here that the Newton–Euler (N–E) dynamic equations are equally well qualified as the Langrange–Euler (L-E) equations for the simulation of self-tuning adaptive control of robot manipulators.

Nonlinear Adaptive Control for Turning Processes

Recently, transformable Unmanned Aerial Vehicles (UAVs) have become a subject of great interest in the field of flying systems, due to their maneuverability, agility and morphological capacities. They can be used for specific missions and in more congested spaces. Moreover, this novel class of UAVs is considered as a viable solution for providing flying robots with specific and versatile functionalities. In this paper, we propose (i) a new design of a transformable quadrotor with (ii) generic modeling and (iii) adaptive control strategy. The proposed UAV is able to change its flight configuration by rotating its four arms independently around a central body, thanks to its adaptive geometry. To simplify and lighten the prototype, a simple mechanism with a light mechanical structure is proposed. Since the Center of Gravity (CoG) of the UAV moves according to the desired morphology of the system, a variation of the inertia and the allocation matrix occurs instantly. These dynamics parameters play an important role in the system control and its stability, representing a key difference compared with the classic quadrotor. Thus, a new generic model is developed, taking into account all these variations together with aerodynamic effects. To validate this model and ensure the stability of the designed UAV, an adaptive backstepping control strategy based on the change in the flight configuration is applied. MATLAB simulations are provided to evaluate and illustrate the performance and efficiency of the proposed controller. Finally, some experimental tests are presented.

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be from the MIMO sector-bounded nonstationary nonlinearities class. The task is to obtain robust adaptive stabilization. Some absolute stability theorems and integral evaluations of the state and control vectors are applied in the MIMO fuzzy adaptive controller design procedure for the first time. A method for automatically obtaining a near-optimal nonlinear dynamic reference model is described. A practical step-by-step procedure of the MIMO adaptive fuzzy controller design is proposed, resulting in highly interpretable MIMO fuzzy control rules based on triangular fuzzy sets and strong triangular fuzzy partition. An example of the fuzzy controller design according to the proposed procedure is given.

In this article, a fuzzy-approximation-based adaptive backstepping control method for dual-arm of a humanoid robot was proposed. The purpose of this control system is to provide coordinated movement assistance to enable the humanoid robot’s human-like forearm to grab objects coordinately (or track any continuous desired trajectory), even in the presence of environmental disturbances and parametric uncertainties. We analyze the proposed adaptive backstepping by mathematical modeling and actually measure the robot dual-arm motion information of a number of case when they simulate the trajectory to verify the model. We design the adaptive fuzzy-approximation control strategy and combining the synthesis of the robust design, backstepping control, and Lyapunov function method, the proposed adaptive fuzzy backstepping control does not need to know the humanoid robot’s arms model precisely. In the control system proposed here, once the desired trajectories of the robot’s dual-arm positions are given, the adaptive fuzzy system was closed to any unknown functions and to the derivative of the virtual control law of the humanoid robot system. In this case, a robust design scheme was utilized to compensate for any approximation errors. With the proposed trajectory tracking, not only able to generate the coordinate motions for a humanoid robot’s two arms, but it can also control the arms to move to the desired positions. The proposed closed-loop system under the adaptive fuzzy backstepping control design was effective and that asymptotic stability was successfully achieved. The adaptive fuzzy-approximation backstepping control strategy should be more complete and intelligent and more actual test should be conducted to further evaluate the effect of the proposed trajectory tracking. The instability of dual-arm of humanoid robot system is systematically analyzed and a backstepping control strategy based on the adaptive fuzzy-approximation to improve the continuity of trajectory tracking of the robot’s arms is proposed.

The two most commonly used induction motor control methods are the scalar (V/f) and vector (field-oriented) control techniques. V/f control has good steadystate response and robustness against the variation in machine parameters, but the resulting torque dynamics are poorer. Vector control achieves better dynamic performance by decoupling the fluxand torque-producing components of the stator current. Vector-controlled techniques are heavily dependent on the machine parameters and are sensitive to the variation in machine parameters. This paper proposes a novel control technique, which integrates the robustness features of scalar control and good dynamic performance of vector control. Estimation of the transient vector is responsible for improving dynamics in the proposed control. The proposed control technique uses an optimal controller with an output feedback law. The operation of the proposed control is validated experimentally under both steady-state and transient conditions. Finally, the proposed control is compared with both the V/f and vector control strategies in terms of dynamic performance and parameter sensitivity.

Compactly supported radial basis functions for adaptive process control

This paper presents a new adaptive vector control scheme for inverter-fed induction motors. The scheme proposes new system structure consisting of two subsystems for adaptive vector control, a vector control system realized on synchronous frame and a parameter identification system on stationary frame. Proposed adaptive identification system can perform in different rate from that of the control system, which adaptively identifies motor parameters varying dynamically with temperature and magnetic saturation and determines critical parameters of vector control system. The high performance of the proposed scheme verified by a real drive system is also shown.