A velocity control method based on composite observer and adaptive super-torsional sliding mode

Abstract

This invention proposes a speed control method based on a composite observer and adaptive super-torsional sliding mode, comprising the following steps: First, an extended dynamic model of the PMLSM system is established by introducing extended variables related to speed measurement; second, based on unknown motor parameters, internal disturbances, and external load changes, a composite observer consisting of a model reference adaptive observer and a generalized proportional-integral observer is designed according to the extended dynamic model of the PMLSM; next, for the extended dynamic model of the PMLSM after compensating for lumped disturbances, an outer-loop AST speed control law based on the composite observer is designed to enhance the robustness of the system; finally, a PI-based inner-loop d-q axis current vector control method is adopted to achieve the motor stator current tracking its given value. This invention can comprehensively consider complex actual operating conditions such as unknown mover mass, internal and external disturbances, and speed measurement noise, enabling the speed control of the PMLSM drive system to have good dynamic and steady-state performance.

Description

Technical Field

[0001] This invention belongs to the field of permanent magnet synchronous linear motor control, and relates to a speed control method based on a composite observer and adaptive super-twisting sliding mode. Background Technology

[0002] PMLSMs (Programmable Matrix Multi-Screen Machines) possess advantages such as fast response, high precision, large thrust, and low noise, and are widely used in precision CNC machine tools, lithography machines, and high-speed logistics equipment. To achieve efficient precision machining and production, the control requirements for PMLSM drive systems are fast response, high precision, and strong robustness. When designing control strategies for PMLSM drive systems, it is essential to comprehensively consider various factors encountered during operation, such as unknown mover mass and viscous friction coefficient, internal disturbances like nonlinear friction and thrust fluctuations, external disturbances like load changes, and speed measurement noise pollution. To address these issues, several robust control strategies based on observers have emerged. While these methods can overcome the influence of parameter uncertainties or resist internal and external disturbances, they still cannot meet the high precision requirements of PMLSM drive systems. Specific shortcomings are as follows:

[0003] (1) For PMLSM drive systems, existing observer-based control strategies only consider parameter uncertainties or only consider external disturbances, without fully considering various complex actual working conditions such as unknown mover mass, internal disturbances and external disturbances. In addition, the speed measurement noise problem in speed negative feedback control is not considered.

[0004] (2) At present, the control strategy of PMLSM drive system only designs the mover mass observer or disturbance observer, but rarely considers the design of observer that can simultaneously estimate the mover mass, the actual mover speed and the lumped disturbance.

[0005] (3) For PMLSM drive systems, existing observer-based control strategies usually require not only measuring speed signals, but also measuring current or voltage signals. Each signal measurement inevitably introduces measurement noise, which will inevitably affect the estimation accuracy of the observer and thus affect the control accuracy of the system.

[0006] (4) Currently, the commonly used observers for PMLSM drive system control strategies are sliding mode observer (SMO), Kalman filter observer (EKFO), extended state observer (ESO) and disturbance observer (DOB). However, SMO suffers from chattering and overshoot, EKF has high computational cost and large computational load, ESO is only used to estimate constant or slow time-varying physical quantities, and DOB requires a trade-off between system bandwidth and noise suppression. Summary of the Invention

[0007] This invention proposes a speed control method based on a composite observer and adaptive super-torsional sliding mode, which comprehensively considers a series of adverse effects during motor operation and designs countermeasures. It not only ensures high-precision speed control of the PMLSM drive system, but also has good dynamic and steady-state performance. Furthermore, its stability has been rigorously proven mathematically, demonstrating strong practicality and feasibility.

[0008] This invention includes the following steps:

[0009] Step 1: Based on the cloaked polar PMLSM dynamic equations and velocity noise conditions in the dq-axis rotating coordinate system, establish an extended dynamic model of the PMLSM.

[0010] The dynamic equations of the hidden polar PMLSM in the dq-axis rotating coordinate system are as follows:

[0011]

[0012] In the formula, v m The actual velocity of the PMLSM mover without noise; i q q-axis stator current; M is mover mass; B is viscous friction coefficient; F e Electromagnetic thrust; F f It is a nonlinear frictional force; F r For thrust fluctuation; F d For external load; n p ψ is the number of magnetic pole pairs; f τ is the constant flux linkage of the permanent magnet; τ is the pole distance; t is the current time.

[0013] Introducing the extended variable v related to speed measurement, the PMLSM extended model becomes:

[0014]

[0015] In the formula, v is the integral value of the speed measurement signal containing noise; η is the speed measurement noise; k f Let d be the electromagnetic thrust coefficient, d be the system lumped disturbance, and k be the electromagnetic thrust coefficient. f The expressions for and d are as follows:

[0016]

[0017] In the formula, This is the reference value for the q-axis current.

[0018] Because of B, F f F r F d If M and d are both unknown, then d is also unknown.

[0019] Step 2: Design a composite observer based on the PMLSM extended dynamic model in Step 1. The composite observer consists of a model reference adaptive observer and a generalized proportional integral observer.

[0020] Design a model reference adaptive observer to obtain the PMLSM mover mass estimate.

[0021] The motioner mass valuation for:

[0022]

[0023] In the formula, T s is the sampling period; k represents the kth sampling time; β is the positive real gain; The estimated mass of the motor at time k; The difference between the q-axis current reference values ​​at sampling times (k-2) and (k-1) is, i.e. e 0 (k) is the difference between the actual value of v at time k and the prior estimate, i.e., Wherein, the prior estimate of v(k) Represented as

[0024] Step 3: Design a generalized proportional-integral observer to estimate the PMLSM lumped perturbation and the actual velocity of the mover;

[0025] The generalized proportional-integral observer is:

[0026]

[0027] In the formula, This is an estimate of v; For v m The estimated value; This is an estimate of d; for The estimated value; a i >0 (i = 0, 1, 2, 3) represents the generalized proportional-integral observer gain; Estimation of the mass of the moving part.

[0028] Step 4: Based on the model reference adaptive observer in Step 2 and the generalized proportional-integral observer in Step 3, design the outer-loop AST velocity control law based on the composite observer;

[0029] The q-axis current reference value is:

[0030]

[0031] In the formula, u is the outer loop AST speed control law; Let be the estimated value of the lumped disturbance d, i.e.

[0032] Lumped disturbances to the system With compensation, the extended system model of PMLSM is expressed as:

[0033]

[0034] In the formula, This is an estimate of v; For v m The estimated value, that is,

[0035] The outer-loop AST velocity control law based on the composite observer is:

[0036]

[0037] In the formula, s is the PMLSM reference velocity; s is the sliding surface; u1 is the rate of change of the reference velocity; u2 is the super-torsion sliding control law; sign(s) is the sign function;

[0038] l is the super-twisted sliding mode gain, and its adaptive law is:

[0039]

[0040] In the formula, The initial value of l is a positive real number; the initial value of l(0) is a positive real number; the sliding surface s is...

[0041] Step 5: Design the inner loop dq-axis current control law using PI-based vector control;

[0042] The inner loop dq-axis current control law is as follows:

[0043]

[0044] In the formula, u d * The d-axis reference voltage; u q * i is the q-axis reference voltage; d * The d-axis reference current is set to 0; k pd d-axis proportional gain; k pq k is the q-axis proportional gain; id For d-axis integral gain; k iq k is the q-axis integral gain; pd k pq k id kiq All are positive real numbers.

[0045] The beneficial effects of this invention are as follows:

[0046] (1) This invention fully considers the actual complex working conditions such as unknown mover mass, internal disturbance, external disturbance and velocity noise, integrates relevant uncertain parameters, various internal and external disturbances and q-axis current into lumped disturbance, and establishes an extended dynamic model of PMLSM, which provides convenience for the design of composite observer and AST control law.

[0047] (2) The present invention designs a composite observer consisting of two estimators connected in series, which can simultaneously estimate the mover mass, the actual mover velocity and the lumped disturbance, so that the control strategy based on the composite observer can resist the adverse effects of various parameter uncertainties and internal and external disturbances faced by the PMLSM drive system.

[0048] (3) The composite observer of the present invention only uses speed measurement signals and does not need to measure current or voltage signals. This not only reduces the system hardware installation cost, but more importantly, avoids the adverse effects of current measurement noise, thereby improving the estimation accuracy of the composite observer and thus improving the control accuracy of the PMLSM drive system.

[0049] (4) The model reference adaptive observer designed in this invention, due to the adoption of the model reference adaptive mechanism, can estimate the constant or time-varying quantum mass more quickly and accurately compared with other existing estimation methods such as the Coulomb-viscous friction model and the extended Kalman filter, and can completely eliminate the adverse effects of lumped disturbances.

[0050] (5) The generalized proportional-integral observer designed in this invention introduces the integral variable of the velocity measurement which is contaminated by noise, thus achieving complete decoupling between the gain selection of the generalized proportional-integral observer and the velocity measurement noise. This eliminates the influence of the velocity measurement noise on the estimation accuracy of the generalized proportional-integral observer. Furthermore, by reasonably selecting the gain of the generalized proportional-integral observer, the estimation error can be reduced and the estimation accuracy can be improved. The generalized proportional-integral observer can simultaneously estimate the time-varying lumped disturbance of PMLSM and the actual velocity of the mover. Compared with the sliding mode observer, disturbance observer and extended state observer, this invention does not estimate chattering, so there is no need to make a trade-off between system bandwidth and noise suppression.

[0051] (6) The present invention compensates for the lumped disturbance estimated by the composite observer. The designed AST control law can not only completely resist the lumped disturbance, but also enhance the robustness to the change of mover mass. In addition, the AST control strategy based on the composite observer designed in this invention can also reduce system chattering and effectively improve the speed control accuracy of the PMLSM drive system.

[0052] Figure caption

[0053] Figure 1 This is a control block diagram of the PMLSM drive system in this embodiment;

[0054] Figure 2 This is a block diagram of the equivalent nonlinear feedback system in this embodiment;

[0055] Figure 3 This is the system control flowchart of this embodiment;

[0056] Figure 4 This is a schematic diagram comparing speed and speed error under three outer-loop speed control strategies in Experiment 1 (with reference speed variation).

[0057] Figure 5 This is a schematic diagram of the motor load changes in Experiment 2;

[0058] Figure 6 This is a schematic diagram comparing speed and speed error under three outer loop speed control strategies in Experiment 2 (under varying load conditions);

[0059] Figure 7 This is a schematic diagram comparing the electromagnetic thrust under three outer loop speed control strategies in Experiment 2 (under varying load conditions);

[0060] Figure 8 This is a schematic diagram comparing the velocity errors under three outer-loop velocity control strategies in Experiment 3 (under the condition of changing mover mass);

[0061] Figure 9 This is a schematic diagram comparing the q-axis current under three outer loop velocity control strategies in Experiment 3 (under the condition of changing mover mass);

[0062] Figure 10 This is a schematic diagram comparing the speed errors under three outer-loop speed control strategies in Experiment 4 (under the condition of varying viscous friction coefficient);

[0063] Figure 11 This is a schematic diagram comparing the q-axis current under three outer ring velocity control strategies in Experiment 4 (under the condition of varying viscous friction coefficient). Detailed Implementation

[0064] The invention will now be described clearly and completely with reference to the figures and implementation methods.

[0065] Example

[0066] Taking the elusive pole PMLSM as the research object, this embodiment proposes a velocity control method based on a composite observer and adaptive super-torsional sliding mode to ensure that the PMLSM velocity can quickly and accurately track the given value. The specific steps include the following:

[0067] Step 1: Based on the PMLSM dynamic equations, considering the speed measurement noise condition, establish its extended dynamic model;

[0068] The equations of motion and thrust of the elusive pole PMLSM in the dq rotating coordinate system are as follows:

[0069]

[0070] Equation (1) can be rewritten as follows

[0071]

[0072] In the formula,

[0073]

[0074] Considering the presence of speed measurement noise in the PMLSM feedback control system, a new extended variable is introduced, defined as:

[0075]

[0076] Combining equations (2) and (3), the extended PMLSM system model is as follows:

[0077]

[0078] Considering that the actual value of M is unknown, and also considering B and F f F r With F d Since both are unknown, d is also unknown. Therefore, this invention designs a composite observer, the block diagram of which is shown below. Figure 1 As shown in the dashed box, the composite observer consists of a model reference adaptive observer and a generalized proportional-integral observer. The model reference adaptive observer is used to estimate the mover mass M, and the generalized proportional-integral observer is used to estimate the lumped perturbation d and the actual mover velocity v. m .

[0079] Step 2: Based on the extended dynamic model of PMLSM described in Step 1, design the model reference adaptive observer in the composite observer, which is used to estimate the mass of the PMLSM mover.

[0080] The design process of the model reference adaptive observer is as follows:

[0081] Discretizing equation (4) yields:

[0082]

[0083] Equation (5) can also be expressed as:

[0084]

[0085] Substituting equation (6) into equation (5) yields:

[0086]

[0087] Since the system sampling frequency is very high, the following equation can be assumed to hold true at adjacent sampling times:

[0088]

[0089] Substituting equation (8) into equation (7), we get:

[0090]

[0091] In the formula,

[0092]

[0093] According to equation (9), the reference model of the model reference adaptive observer is established as follows:

[0094]

[0095] Based on equation (11), the prior and posterior adjustable models of the model reference adaptive observer are established as follows:

[0096]

[0097] In the formula, Let a be an estimated value; and These are the prior and posterior estimates of v(k), respectively.

[0098] Correspondingly, the prior estimation error e of the model reference adaptive observer 0 (k) and the posterior estimation error e(k) are defined as follows:

[0099]

[0100] design The adaptive law is:

[0101]

[0102] Therefore, by The estimated mass of the mover can be obtained as follows:

[0103]

[0104] As can be seen from equation (17), the valuation Convergence depends on valuation The convergence of the model reference adaptive observer is ensured in this embodiment. The model reference adaptive observer is asymptotically stable, guaranteeing the convergence of the estimated value. Converging to the true value 'a', thus ensuring the valuation. It converges to the truth value M, and the proof of the former is as follows:

[0105] Class A algorithms based on Landau's discrete parameter recursive mechanism The adaptive laws are as follows:

[0106]

[0107] In the formula, It's about e 0 The correction function of (k) or e(k).

[0108] To avoid estimation inaccuracies caused by delayed valuation, we first determine φ[e(k)], and then determine φ[e... 0 (k)], finally obtained

[0109] Therefore, substituting (19) into equation (15) yields

[0110]

[0111] This embodiment constructs a nonlinear feedback system that is completely equivalent to equation (20), and the system block diagram is as follows. Figure 2 As shown, G e (z) = 1, and the correction function is: β is a positive real gain.

[0112] Because of the linear element G in the forward path e (z) = 1 is strictly positive and real, and the nonlinear element of the feedback channel satisfies Popov's integral inequality, that is:

[0113]

[0114] Therefore, according to the theory of hyperstability, Figure 2 The nonlinear feedback system shown is asymptotically stable. The designed φ[e(k)] can guarantee that the system output e(k) converges to 0, that is, it guarantees that the estimation error of the model reference adaptive observer converges to 0, thereby ensuring... It converges to the truth value a.

[0115] In practical applications, it should be estimated according to equation (18). Therefore, φ[e] needs to be obtained. 0 (k)]. Through derivation, it can be obtained as follows:

[0116]

[0117] Substituting equation (22) into equation (18) yields the valuation shown in equation (16). Reconsider The valuation shown in equation (17) can be obtained. The above analysis applies the theory of superstability to ensure the valuation. asymptotic convergence.

[0118] Step 3: Based on the extended dynamic model of PMLSM described in Step 1, design the generalized proportional integral observer in the composite observer to estimate the PMLSM lumped disturbance and the actual velocity of the mover.

[0119] Consider B and F f F r With F d Since both are unknown, d is also unknown. Furthermore, considering the velocity measurement noise in the PMLSM feedback control system, the generalized proportional-integral observer used to estimate the lumped disturbance and the actual velocity of the mover is:

[0120]

[0121] Select an appropriate gain parameter a i (i = 0, 1, 2, 3) can guarantee the stability and convergence of the generalized proportional integral observer, and the proof is as follows.

[0122] Define the estimation error as:

[0123]

[0124] The estimation error equation for the generalized proportional-integral observer is:

[0125]

[0126] In the formula,

[0127]

[0128] Therefore, the characteristic polynomial of the generalized proportional-integral observer estimation error equation is:

[0129] △(λ)=λ 4 +a3λ 3 +a2λ 2 +a1λ+a0 (27)

[0130] Select gain a i (i = 0, 1, 2, 3) allows A to be a Hurwitz matrix, thus guaranteeing the stability of the generalized proportional-integral observer. Meanwhile, the designed generalized proportional-integral observer exhibits convergence, and its analysis process is as follows:

[0131] Since the system shown in equation (25) is stable, for any given symmetric positive definite matrix Q1, there must exist a unique symmetric positive definite matrix P1 that satisfies the following Lyapunov equation:

[0132] A T P1 + P1A = -Q1 (28)

[0133] Choose the Lyapunov function as:

[0134] V(E)=E T P1E / 2 (29)

[0135] The derivative of equation (29) is:

[0136]

[0137] In the formula, γ≥sup t ||η d (t)||, This represents the smallest eigenvalue of the matrix.

[0138] From equation (30), it can be seen that if the estimation error of the generalized proportional-integral observer satisfies the following inequality:

[0139]

[0140] but According to Lyapunov stability theory, ||E|| will decrease until... The estimation error trajectory of the generalized proportional-integral observer will then converge to a bounded region:

[0141]

[0142] Equation (32) shows that, under the premise that A is a Hurwitz matrix, the gain a is chosen to be... i (i = 0, 1, 2, 3) such that λ min (AA T As the value increases, the error norm ||E|| decreases, thereby improving the estimation accuracy of the generalized proportional-integral observer.

[0143] Step 4: Design the outer-loop AST velocity control law based on the composite observer, and analyze its stability using Lyapunov stability theory.

[0144] The design concept of the outer-loop AST speed control law is as follows: First, the extended dynamic model of the PMLSM is compensated to completely eliminate the adverse effects of the lumped disturbance term; then, the AST control law is designed for the compensated extended dynamic model of the PMLSM; finally, the stability of the control law is proved using Lyapunov stability theory; specifically:

[0145] First, consider the estimates from the composite observer. and The design reference value for the q-axis current is:

[0146]

[0147] Substitute equation (33) into equation (4), and simultaneously use and Replace M and v respectively m The true value of d is related to the lumped disturbance. The compensated PMLSM extended system model is rewritten as follows:

[0148]

[0149] Next, for the extended dynamic model of PMLSM shown in equation (34), its AST velocity control law is designed as follows:

[0150] Select the sliding surface s as:

[0151]

[0152] The AST velocity control law is:

[0153]

[0154] The adaptive law of l is:

[0155]

[0156] Finally, the stability analysis of the AST based on Lyapunov stability theory is as follows:

[0157] Introduce the following coordinate transformations:

[0158]

[0159] The derivative of equation (38) is:

[0160]

[0161] Let ξ = [ξ1 ξ2] T In the formula, ξ2=y2 / l.

[0162] Define the following Lyapunov function:

[0163]

[0164] Differentiating equation (40) yields:

[0165]

[0166] In the formula,

[0167]

[0168] Substituting the adaptive law of equation (37)l into equation (41), we get:

[0169]

[0170] Because λ min (Q2)||ξ|| 2 ≤ξ T Q2ξ1, so equation (43) satisfies the following inequality:

[0171]

[0172] And because of λ min (Q2)>0, so when l>0, When l≤0, it can be seen from equation (37) that l will increase to Thus In summary Therefore, based on Lyapunov stability theory, the designed AST speed control law can enable the PMLSM drive system to operate stably.

[0173] Step 5: Design the inner loop dq-axis current control law using PI-based vector control;

[0174] The inner loop dq-axis PI current control law is:

[0175]

[0176] To verify the effectiveness and feasibility of the method proposed in this embodiment, simulation models of the PMLSM drive system under three outer-loop control strategies were built on the StarSim hardware-in-the-loop simulation platform for verification. The three outer-loop control strategies are: ordinary adaptive super-twisted sliding mode (AST), adaptive super-twisted sliding mode combined with a generalized proportional-integral observer (GPIO-AST), and this embodiment (CO-AST).

[0177] To ensure a fair comparison, the inner loop currents of the three PMLSM drive systems are controlled by PI-based vector control with identical parameters. The parameters of the generalized proportional-integral observer in GPIO-AST are exactly the same as in this embodiment, except that the nominal value of M is used instead of the estimated value of the model reference adaptive observer.

[0178] The nonlinear friction force and thrust fluctuation are set as F. f +F r =10sin(4πt), while using a white noise with a mean of 0 and a standard deviation of 10-5 to simulate the speed measurement noise η.

[0179] Other parameters involved in StarSim hardware-in-the-loop simulation are shown in Table 1.

[0180] Table 1 Hardware-in-the-loop simulation parameter settings

[0181]

[0182] Experiment 1: The load is constant at 0N, the reference speed is initially set to 1m / s, and then suddenly drops to -1m / s at 0.5s.

[0183] Figure 4 (a) A speed comparison of the PMLSM drive system under three outer loop control strategies is given under the condition of reference speed variation; Figure 4 (b) presents a comparison of the speed errors of the PMLSM drive system under three outer-loop control strategies when the reference speed changes; Figure 4 As can be seen from (a) and 4(b), compared with AST and GPIO-AST, this embodiment can significantly speed up the system response, reduce overshoot and weaken speed fluctuations. This embodiment can improve speed control accuracy and enhance the system's robustness against internal and external disturbances and interference factors such as measurement noise.

[0184] Experiment 2: Load changes as follows Figure 5 As shown, the reference speed is constant at 1 m / s.

[0185] Figure 6 (a) A speed comparison of the PMLSM drive system under three outer-loop control strategies is presented under varying load conditions. Figure 6 (b) A comparison of the speed errors of the PMLSM drive system under three outer loop control strategies is presented under varying load conditions; Figure 7 A comparison of the electromagnetic thrust of the PMLSM drive system under three outer-loop control strategies is presented under varying load conditions.

[0186] Depend on Figure 6 (a) Figure 6 (b) and Figure 7 It can be seen that, compared with AST and GPIO-AST, this embodiment can significantly reduce speed fluctuations and electromagnetic thrust fluctuations during load changes, and enhance the system's robustness to interference factors such as load changes, internal and external disturbances, and measurement noise.

[0187] Experiment 3: The load is constant at 500N and the reference speed is constant at 1m / s. At 0s, M suddenly decreases to 0.5M.

[0188] Figure 8 A comparison of the speed errors of the PMLSM drive system under three outer-loop control strategies is presented when the mover mass changes. Figure 9A comparison of the q-axis currents of the PMLSM drive system under three outer-loop control strategies is presented when the mover mass changes.

[0189] Experiment 4: The load is constant at 500N and the reference speed is constant at 1m / s. At time 0s, B suddenly increases to 2B.

[0190] Figure 10 A comparison of the speed errors of the PMLSM drive system under three outer-loop control strategies is presented for different viscous friction coefficients. Figure 11 A comparison of the q-axis currents of the PMLSM drive system under three outer-loop control strategies is presented when the viscous friction coefficient varies.

[0191] Depend on Figure 8-11 It can be seen that, under the condition of sudden changes in motor parameters, compared with AST and GPIO-AST, the speed fluctuation in this embodiment is smaller and the q-axis current is more stable. This shows that this embodiment can enhance the robustness of the system to interference factors such as sudden changes in motor parameters, internal and external disturbances and measurement noise.

Claims

1. A speed control method based on a composite observer and adaptive hyper-stiction sliding mode, characterized in that, The method comprises the following steps: Step 1: based on the d-q axis rotating coordinate system under the hidden pole type PMLSM dynamics equation and the speed measurement noise working condition, an extended dynamic model of the PMLSM is established; The d-q axis rotating coordinate system under the hidden pole type PMLSM dynamics equation is: ； where v m is the actual speed of the PMLSM mover without noise; i q is the q-axis stator current; M is the mover mass; B is the viscous friction coefficient; F e is the electromagnetic thrust; F f is the nonlinear friction force; F r is the thrust fluctuation; F d is the external load; n p is the number of pole pairs; is the permanent magnet flux linkage with a constant value; is the pole pitch; t is the current time; Introducing an extended variable related to speed measurement Afterwards, the PMLSM extended model is: ； where v is the value of the integration of the speed signal containing noise; is the speed measurement noise; k f is the electromagnetic thrust coefficient; d is the system lumped disturbance; k f and d are given by the expressions ； ； In the formula, is the q-axis current reference value; Since B, F f , F r , F d , M are unknown, d is also unknown; Step 2: Design a compound observer based on the PMLSM extended dynamic model in Step 1, which consists of a model reference adaptive observer and a generalized proportional integral observer. Design the model reference adaptive observer to obtain the PMLSM mover mass estimate ; Step 3: a generalized proportional integral observer is designed for estimating the PMLSM lumped disturbance and the actual speed of the mover; Step 4: based on the model reference adaptive observer in step 2 and the generalized proportional integral observer in step 3, an outer ring AST speed control law based on a composite observer is designed; Step 5: a PI-based vector control is used to design an inner ring d-q axis current control law.

2. The speed control method based on a compound observer and adaptive hyper-stiction sliding mode according to claim 1, wherein, The mover mass estimate in step 2 is: ； where T is the sampling period; k represents the kth sampling time; β is a positive real gain; s is the sampling period; k represents the kth sampling time; β is a positive real gain; is the mover mass estimate at time k; is the difference between the q-axis current reference values at the two sampling times (k-2) and (k-1), i.e., ; is the difference between the actual value and the prior estimate of v at time k, i.e., where the prior estimate of v(k) is ; ; is the estimate of the auxiliary variable at the kth time, ; k f is the electromagnetic thrust coefficient; is the integrated sampling value of the speed measurement signal containing noise at the kth time.

3. The speed control method based on a compound observer and adaptive hyper-stiction sliding mode according to claim 1, wherein, The generalized proportional integral observer in step 3 is: ； wherein is an estimate of v; is an estimate of v m is an estimate of v; is an estimate of d; is an estimate of is an estimate of is a generalized proportional-integral observer gain; is an estimate of the mass of the mover M at the kth time instant.

4. The speed control method based on a compound observer and adaptive hyper-stiction sliding mode according to claim 1, wherein, The q-axis current reference value designed in step 4 is: ； where u is the outer loop AST speed control law; is the estimate of the system aggregate disturbance d at the kth instant, ; aggregate disturbance estimate for the system The extended system model of PMLSM is: ； wherein is an estimate of v; is an estimate of is an estimate of ; The outer ring AST speed control law based on the composite observer is: ； In the formula, is the PMLSM reference speed; s is the sliding surface; u1 is the reference speed change rate; u2 is the super-twisted sliding mode control law; is the sign function; l is a super-torsional sliding mode gain, and the adaptive law is: ； wherein is a positive real number; the initial value l(0) of l is a positive real number; the sliding surface s is .

5. The speed control method based on composite observer and adaptive hyper-stable sliding mode according to claim 1, wherein, The inner ring d-q axis current control law designed in step 5 is: ； where u d * is the d-axis reference voltage; u q * is the q-axis reference voltage; i d * is the d-axis reference current, which is taken as 0; k pd is the d-axis proportional gain; k pq is the q-axis proportional gain; k id is the d-axis integral gain; k iq is the q-axis integral gain; k pd , k pq , k id , k iq are all positive real numbers.

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